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Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 10.8%, 16.2%, and 4.9% in prokaryotic hosts, and 1.7%, 6.6% and 11.5% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic hosts and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL identifies important proteins in the prediction task. We found them capturing key functions in virus-host specificity. Author summary: Being able to predict which viruses can infect which host species, and identifying the specific proteins that are involved in these interactions, are fundamental tasks in virology. Traditional methods for predicting these interactions rely on identifying common features among proteins, overlooking the structure of the protein "language" encoded in individual proteins. We have developed a novel method that combines a protein language model and multiple instance learning to allow host prediction directly from protein sequences, without the need to extract features manually. This method significantly improved prediction accuracy and revealed key proteins involved in virus-host interactions. [ABSTRACT FROM AUTHOR]

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains. Author summary: In immune receptors across various organisms, repeating protein structures play a crucial role in recognizing and responding to pathogen threats. These structures resemble the coils of a slinky toy, allowing these receptors to adapt and change over time. One particularly vital but challenging structure to study is the Leucine Rich Repeat (LRR). Traditional methods that rely just on analyzing the sequence of these proteins can miss subtle changes due to rapid evolution. With the introduction of protein structure prediction tools like AlphaFold 2, annotation methods can study the coarser geometric properties of the structure. In this study, we visualize LRR proteins in three dimensions and use a mathematical approach to 'flatten' them into two dimensions, so that the coils form circles. We then used a mathematical concept called winding number to determine the number of repeats and where they are in a protein sequence. This process helps reveal their repeating patterns with enhanced clarity. When we applied this method to immune receptors from a model plant organism, we found that our approach could accurately identify coiling patterns. Furthermore, we detected errors made by previous methods and highlighted unique structural variations. Our research offers a fresh perspective on understanding immune receptors, potentially influencing studies on their evolution and function. [ABSTRACT FROM AUTHOR]

(1) Background: The Mixed lineage leukemia 1 (MLL1) gene, located on chromosome 11q23, plays a pivotal role in histone lysine-specific methylation and is consistently associated with various types of leukemia. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) have been tied to numerous diseases, including cancers, and have become valuable cancer biomarkers. There's a notable gap in studies probing the influence of SNPs on MLL1 protein structure, function, and subsequent modifications; (2) Methods: We utilized an array of bioinformatics tools, including PredictSNP, InterPro, ConSurf, I-Mutant2.0, MUpro, Musitedeep, Project HOPE, RegulomeDB, Mutpred2, and both CScape and CScape Somatic, to meticulously analyze the consequences of nsSNPs in the MLL1 gene; (3) Results: Out of 2,097 nsSNPs analyzed, 62 were determined to be significantly pathogenic by the PredictSNP tool, with ten crucial MLL1 functional domains identified using InterPro. Additionally, 50 of these nsSNPs had high conservation scores, hinting at potential effects on protein structure and function, while 32 were found to undermine MLL1 protein stability. Notably, four nsSNPs were deemed oncogenic, with two identified as cancer drivers. The nsSNP, D2724G, between the MLL1 protein's FY-rich domains, could disrupt proteolytic cleavage, altering gene expression patterns and potentially promoting cancer; (4) Conclusions: Our research provides a comprehensive assessment of nsSNPs' impact in the MLL1 protein structure and function and consequently on leukemia development, suggesting potential avenues for personalized treatment, early detection, improved prognosis, and a deeper understanding of hematological malignancy genesis. [ABSTRACT FROM AUTHOR]

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively underexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF1, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement. Author summary: Redirecting T cells to recognize and combat infected or altered cells through the engineering of T cell receptors (TCR) has shown promising results, particularly in immunotherapy against cancer. Leveraging breakthroughs in deep learning within the biosciences, this study explores whether deep learning architectures for general protein design, such as ProteinMPNN and ESM-IF1, can design TCRs to bind target antigenic peptides presented by the MHC (pMHC) based on fixed-backbone structures of the complexes. We utilized a comprehensive set of sequence-based and structure-based metrics, derived from deep learning or classical physics-based principles, to evaluate the generated TCR designs. Our findings highlight the benefits of these methods compared to traditional physics-based design approaches and pinpoint deficiencies for improvement. The results contribute to advancements in the field of TCR design, serving as a guiding framework for the structure-based design of TCRs using deep learning, thereby providing the basis for computational engineering of specific TCRs for T cell-based immunotherapy. [ABSTRACT FROM AUTHOR]

Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment. [ABSTRACT FROM AUTHOR]

The Regulator of Telomere Helicase 1 (RTEL1) gene encodes a critical DNA helicase intricately involved in the maintenance of telomeric structures and the preservation of genomic stability. Germline mutations in the RTEL1 gene have been clinically associated with Hoyeraal-Hreidarsson syndrome, a more severe version of Dyskeratosis Congenita. Although various research has sought to link RTEL1 mutations to specific disorders, no comprehensive investigation has yet been conducted on missense mutations. In this study, we attempted to investigate the functionally and structurally deleterious coding and non-coding SNPs of the RTEL1 gene using an in silico approach. Initially, out of 1392 nsSNPs, 43 nsSNPs were filtered out through ten web-based bioinformatics tools. With subsequent analysis using nine in silico tools, these 43 nsSNPs were further shortened to 11 most deleterious nsSNPs. Furthermore, analyses of mutated protein structures, evolutionary conservancy, surface accessibility, domains & PTM sites, cancer susceptibility, and interatomic interaction revealed the detrimental effect of these 11 nsSNPs on RTEL1 protein. An in-depth investigation through molecular docking with the DNA binding sequence demonstrated a striking change in the interaction pattern for F15L, M25V, and G706R mutant proteins, suggesting the more severe consequences of these mutations on protein structure and functionality. Among the non-coding variants, two had the highest likelihood of being regulatory variants, whereas one variant was predicted to affect the target region of a miRNA. Thus, this study lays the groundwork for extensive analysis of RTEL1 gene variants in the future, along with the advancement of precision medicine and other treatment modalities. [ABSTRACT FROM AUTHOR]

Programmed death-ligand 1 (PD-L1), a transmembrane protein, is associated with the regulation of immune system. It frequently has overexpression in various cancers, allowing tumor cells to avoid immune detection. PD-L1 inhibition has risen as a potential strategy in the field of therapeutic immunology for cancer. In the current study, structure-based virtual screening of drug libraries was conducted and then the screened hits were docked to the active residues of PD-L1 to select the optimal binding poses. The top ten compounds with binding affinities ranging from -10.734 to -10.398 kcal/mol were selected for further analysis. The ADMET analysis of selected compounds showed the compounds meet the criteria of ADMET properties. Further, the conformational changes and binding stability of the top two compounds was analyzed by conducting 200 ns simulation and it was observed that the hits did not exert conformational changes to the protein structure. All the results suggest that the chosen hits can be considered as lead compounds for the inhibition of biological activity of PD-L1 in in vitro studies. [ABSTRACT FROM AUTHOR]

A protein superfold is a type of protein fold that is observed in at least three distinct, non-homologous protein families. Structural classification studies have revealed a limited number of prevalent superfolds alongside several infrequent occurring folds, and in α/β type superfolds, the C-terminal β-strand tends to favor the edge of the β-sheet, while the N-terminal β-strand is often found in the middle. The reasons behind these observations, whether they are due to evolutionary sampling bias or physical interactions, remain unclear. This article offers a physics-based explanation for these observations, specifically for pure parallel β-sheet topologies. Our investigation is grounded in several established structural rules that are based on physical interactions. We have identified "frustration-free topologies" which are topologies that can satisfy all the rules simultaneously. In contrast, topologies that cannot are termed "frustrated topologies." Our findings reveal that frustration-free topologies represent only a fraction of all theoretically possible patterns, these topologies strongly favor positioning the C-terminal β-strand at the edge of the β-sheet and the N-terminal β-strand in the middle, and there is significant overlap between frustration-free topologies and superfolds. We also used a lattice protein model to thoroughly investigate sequence-structure relationships. Our results show that frustration-free structures are highly designable, while frustrated structures are poorly designable. These findings suggest that superfolds are highly designable due to their lack of frustration, and the preference for positioning C-terminal β-strands at the edge of the β-sheet is a direct result of frustration-free topologies. These insights not only enhance our understanding of sequence-structure relationships but also have significant implications for de novo protein design. Author summary: A protein superfold is a protein fold that appears in at least three different non-homologous protein families. Superfolds are unique in their ability to accommodate multiple functions within a single fold, a feature not typically seen in other folds. Studies in structural classification have led to two notable observations: the existence of a limited number of common superfolds contrasted with a larger variety of less frequent folds, and a recurring pattern in α/β type superfolds where the C-terminal β-strand often occupies the edge of the β-sheet, while the N-terminal β-strand is usually found in the middle. The origins of these patterns, whether they stem from evolutionary sampling bias or physical interaction mechanisms, remain unclear. This article provides a physics-oriented explanation for these observations, specifically concentrating on pure parallel β-sheet topologies. The insights gained from this research are crucial in enhancing our understanding of the relationship between protein sequences and structures, and are expected to contribute significantly to the de novo design of new proteins. [ABSTRACT FROM AUTHOR]

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by noncoding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand-binding sites, RNA families, protein-binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences. There is an increasing number of experimentally determined 3D RNA structures, but the majority lack functional annotation. To address this gap, this study provides a database of 3D RNA structures with comprehensive, high-quality functional annotations to enable discovery of RNA functions from structural and sequence information [ABSTRACT FROM AUTHOR]

Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 487 protein complexes where AFM fails and obtain an increased success rate (MMscore>0.75) of 33% on these difficult targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks. Author summary: AI networks can now predict the structure of protein complexes with high accuracy in the majority of cases. The accuracy of the predicted protein complexes is directly related to the quality of the input information. However, this information can be very noisy making the output of varying quality. An interesting finding is that AI networks used for structure prediction tend to know when wrong predictions are made based on confidence in the predictions themselves. Together, this suggests that one can look for more useful input information with the predicted confidence from the AI network. To improve the structure prediction of protein complexes, we here learn how to filter the input information so that AlphaFold-multimer can use it better based on the predicted confidence. We show that it is possible to do this efficiently and improve the structures in 33% of cases where AlphaFold-multimer struggles. The same filtering procedure can be used for other tasks as well, e.g. to search for alternative conformations although this remains to be studied. [ABSTRACT FROM AUTHOR]

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms. Author summary: AlphaFold2 is software that combines sequence similarity and structure templating with the power of Artificial Intelligence (AI) to bridge the connection between the primary protein structure (amino acid sequence) and higher-level 3D structure (secondary, tertiary, and quaternary). AlphaFold2's impressive and easily accessible nature makes it a bioinformatics tool that has been seeing a wide range of applications in biochemical research. Given this large-scale application, we examined whether Alphafold2 could be integrated into an undergraduate curriculum. We developed a novel module for a senior-level undergraduate biochemistry laboratory class. Our goal was to lay a solid foundation for other undergraduate instructors to be able to adapt this module to fit their classroom needs. While we implemented and ran all predictions on an internal university computing cluster, we recommend ColabFold for those instructors who do not have access to large-scale computational clusters or whose internal clusters cannot scale to their classroom sizes. We have outlined 3 metrics to be quantitatively investigated in the module to give both instructors and students metrics to evaluate model confidence. We have also included a template worksheet, lecture slides, and example scripts to enable instructors to rapidly develop a similar module. We hope that more departments and programs will integrate AlphaFold2 into their undergraduate curriculums, giving students a highly in-demand skill to prepare them for their transition into a career or graduate school. [ABSTRACT FROM AUTHOR]

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems. Author summary: In this paper, we propose a new architecture that aims to improve upon protein structure prediction algorithms like AlphaFold or RoseTTAFold by considering the combinatorial nature of protein structure formation. Such an architecture, reflecting the physical principles of nature, is expected to yield beneficial results, particularly in scenarios with limited structure and sequence information. Named ComMat, this architecture does not focus on a single structure but rather on a set of multiple structures—a community—simultaneously. In this process, combinatorial exploration of protein structure is encouraged through information exchange among community members. ComMat is an instance that integrates this idea within the structure module of AlphaFold. Applying ComMat to antibody CDR H3 loop structure prediction yielded outstanding results in structure sampling and prediction when tested on the IgFold set and compared with IgFold and AlphaFold-Multimer. It confirmed that improved structure sampling stems from effective structural exploration. The proposed concept here could potentially be used in the development of various other combinatorial protein structure prediction and protein design methods. [ABSTRACT FROM AUTHOR]

The Organic Cation Transporter Novel 1 (OCTN1), also known as SLC22A4, is widely expressed in various human tissues, and involved in numerous physiological and pathological processes remains. It facilitates the transport of organic cations, zwitterions, with selectivity for positively charged solutes. Ergothioneine, an antioxidant compound, and acetylcholine (Ach) are among its substrates. Given the lack of experimentally solved structures of this protein, this study aimed at generating a reliable 3D model of OCTN1 to shed light on its substrate-binding preferences and the role of sodium in substrate recognition and transport. A chimeric model was built by grafting the large extracellular loop 1 (EL1) from an AlphaFold-generated model onto a homology model. Molecular dynamics simulations revealed domain-specific mobility, with EL1 exhibiting the highest impact on overall stability. Molecular docking simulations identified cytarabine and verapamil as highest affinity ligands, consistent with their known inhibitory effects on OCTN1. Furthermore, MM/GBSA analysis allowed the categorization of substrates into weak, good, and strong binders, with molecular weight strongly correlating with binding affinity to the recognition site. Key recognition residues, including Tyr211, Glu381, and Arg469, were identified through interaction analysis. Ach demonstrated a low interaction energy, supporting the hypothesis of its one-directional transport towards to outside of the membrane. Regarding the role of sodium, our model suggested the involvement of Glu381 in sodium binding. Molecular dynamics simulations of systems at increasing levels of Na+ concentrations revealed increased sodium occupancy around Glu381, supporting experimental data associating Na+ concentration to molecule transport. In conclusion, this study provides valuable insights into the 3D structure of OCTN1, its substrate-binding preferences, and the role of sodium in the recognition. These findings contribute to the understanding of OCTN1 involvement in various physiological and pathological processes and may have implications for drug development and disease management. [ABSTRACT FROM AUTHOR]

A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/. [ABSTRACT FROM AUTHOR]

Chromatin organization plays a crucial role in gene regulation by controlling the accessibility of DNA to transcription machinery. While significant progress has been made in understanding the regulatory role of clock proteins in circadian rhythms, how chromatin organization affects circadian rhythms remains poorly understood. Here, we employed ATAC-seq (Assay for Transposase-Accessible Chromatin with Sequencing) on FAC-sorted Drosophila clock neurons to assess genome-wide chromatin accessibility at dawn and dusk over the circadian cycle. We observed significant oscillations in chromatin accessibility at promoter and enhancer regions of hundreds of genes, with enhanced accessibility either at dusk or dawn, which correlated with their peak transcriptional activity. Notably, genes with enhanced accessibility at dusk were enriched with E-box motifs, while those more accessible at dawn were enriched with VRI/PDP1-box motifs, indicating that they are regulated by the core circadian feedback loops, PER/CLK and VRI/PDP1, respectively. Further, we observed a complete loss of chromatin accessibility rhythms in per01 null mutants, with chromatin consistently accessible at both dawn and dusk, underscoring the critical role of Period protein in driving chromatin compaction during the repression phase at dawn. Together, this study demonstrates the significant role of chromatin organization in circadian regulation, revealing how the interplay between clock proteins and chromatin structure orchestrates the precise timing of biological processes throughout the day. This work further implies that variations in chromatin accessibility might play a central role in the generation of diverse circadian gene expression patterns in clock neurons. Author summary: The intricate organization of chromatin is crucial for orchestrating precise gene expression patterns throughout various biological processes such as development and differentiation. In this study, we adapted ATAC-seq (Assay for Transposase-Accessible Chromatin) to assay chromatin accessibility genome-wide from FAC (Fluorescence-activated cell)-sorted Drosophila clock neurons at dawn and dusk timepoints over the circadian cycle. Our findings reveal that regulatory elements of hundreds of clock-regulated genes undergo diurnal oscillations in chromatin accessibility, with more accessible chromatin during the activation phase leading to higher transcription, and compacted chromatin during the repression phase leading to gene silencing. Motif analysis further elucidated distinct regulatory motifs associated with chromatin accessibility changes. Specifically, genes exhibiting increased accessibility around dusk were enriched with E-boxes, whereas those with enhanced accessibility around dawn showed enrichment for VP-boxes. Notably, our studies unveiled that these diurnal fluctuations in chromatin accessibility within clock neurons are dependent on the presence of a functional clock, as per01 null mutants displayed a loss of these rhythmic patterns. In summary, our studies shed light on the intricate relationship between circadian transcriptional regulation and chromatin accessibility dynamics. These insights underscore the role of chromatin accessibility dynamics in priming the genome for rhythmic gene expression, thereby contributing to the orchestration of circadian rhythms. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning. Author summary: Proteins are essential molecules in living organisms and some of them have highly dynamical structures, which makes understanding their biological roles challenging. Disordered proteins can be studied through a combination of computer simulations and experiments. Computer simulations are often resource-intensive. Recently, machine learning has been used to make this process more efficient. The strategy is to learn from previous simulations to model the heterogenous conformations of proteins. However, such methods still suffer from poor transferability, meaning that they tend to make incorrect predictions on proteins not seen in training data. In this study, we present idpSAM, a method based on generative artificial intelligence for modeling the structures of disordered proteins. The model was trained using a vast dataset and, thanks to its architecture and training procedure, it performs well on not just proteins in the training set but achieves high levels transferability to proteins unseen in training. This advancement is a step forward in modeling biologically relevant disordered proteins. It shows how the combination of generative modeling and large training sets can aid us understand how dynamical proteins behave. [ABSTRACT FROM AUTHOR]

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design. [ABSTRACT FROM AUTHOR]

Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets. Author summary: MAX effectors are a family of virulence proteins from the plant pathogenic fungus Pyricularia (syn. Magnaporthe) oryzae that share a similar 3D structure despite very low amino-acid sequence identity. Characterizing the function and evolution of these proteins requires a detailed understanding of their structural diversity. With this in mind, we have determined the NMR structures of four new MAX effectors and shown a near-perfect match with the corresponding AlphaFold2 (AF2) models. We then applied a prediction pipeline based on similarity searches with structural modeling using the AF2 software to predict MAX effectors in a collection of 120 P. oryzae genomes. The resulting models and experimental structures revealed that the MAX core while preserved is highly permissive to secondary structure variations and may coexists with extensive structural diversity in terms of structured N- or C-terminal extensions permitting their classification. For a subset of AF2 models, we have also analyzed the physico-chemical properties of the core domain surfaces, adding another, more functional perspective, notably surface electrostatics and stickiness. This work constitutes a major step in understanding the relationships among MAX effectors by analyzing their structural landscape and cataloguing specific physico-chemical properties. It also provides valuable insights for guiding research into the putative targets of these effectors in infected plant hosts. [ABSTRACT FROM AUTHOR]

We present here the solution structures of the protein thioredoxin-1 from Plasmodium falciparum (PfTrx-1), in its reduced and oxidized forms. They were determined by high-resolution NMR spectroscopy at 293 K on uniformly 13C-, 15N-enriched, matched samples allowing to identification of even small structural differences. PfTrx-1 shows an α/β-fold with a mixed five-stranded β-sheet that is sandwiched between 4 helices in a β1 α1 β2 α2 β3 α3 β4 β5 α4 topology. The redox process of the CGPC motif leads to significant structural changes accompanied by larger chemical shift changes from residue Phe25 to Ile36, Thr70 to Thr74, and Leu88 to Asn91. By high-field high-pressure NMR spectroscopy, rare conformational states can be identified that potentially are functionally important and can be used for targeted drug development. We performed these experiments in the pressure range from 0.1 MPa to 200 MPa. The mean combined, random-coil corrected B1* values of reduced and oxidized thioredoxin are quite similar with -0.145 and -0.114 ppm GPa-1, respectively. The mean combined, random-coil corrected B2* values in the reduced and oxidized form are 0.179 and 0.119 ppm GPa-2, respectively. The mean ratios of the pressure coefficients B2/B1 are -0.484 and -0.831 GPa-1 in the reduced and oxidized form respectively. They differ at some points in the structure after the formation of the disulfide bond between C30 and C33. The thermodynamical description of the pressure dependence of chemical shifts requires the assumption of at least three coexisting conformational states of PfTrx-1. These three conformational states were identified in the reduced as well as in the oxidized form of the protein, therefore, they represent sub-states of the two main oxidation states of PfTrx-1. [ABSTRACT FROM AUTHOR]

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders. [ABSTRACT FROM AUTHOR]

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy's interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy's interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient. Author summary: Foldy is a cloud-based application that enables scientists to use AI-based structural biology tools such as AlphaFold and DiffDock without software expertise. With many different deployment options, it can be set up by individuals, labs, universities, and companies in the cloud with no need for hardware resources. Foldy can predict the structure of proteins and complexes up to 6000 amino acids, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. Some structures are visible to the public on the Lawrence Berkeley Labs Foldy instance, and can be viewed at https://foldy.lbl.gov. Our manuscript highlights the user interface, deployment options, relative strengths of Foldy compared to existing tools, and some past applications of Foldy. It's an accessible solution for researchers who are not software experts. Many deployment options are possible and we highlight two: one of which can be set up in minutes, and the other can handle the traffic of thousands of users and hundreds of thousands of protein structures and docked ligands. This makes advanced AI-based tools more widely available, paving the way for accelerating life science research. By developing an easy-to-use platform, our work demonstrates that even computationally expensive AI-based tools like AlphaFold can be made accessible to a wide audience. Improvements in the accessibility of computational tools will allow more biologists to more easily apply computational tools to more problems. We are hopeful that Foldy addresses the growing need of making revolutionary computational tools accessible to more researchers. [ABSTRACT FROM AUTHOR]

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline. Author summary: Membrane proteins participate in many life processes. They constitute 30% of the human proteome and are targets for over 60% pharmaceuticals. Accurate and accessible computational tools to design membrane proteins will transform the platform to engineer membrane proteins for therapeutic, sensor, and separation processes. While soluble protein design has advanced, membrane protein design remains challenging due to the difficulties in modeling the lipid bilayer. Electrostatics plays an intimate role in the physics of membrane protein structure and function. However, accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive calculations that are not scalable. In this work, we contribute a fast-to-compute electrostatic model that considers different lipid bilayers and their features, making design calculations tractable. We demonstrate that the updated energy function improves the calculation of the tilt angle of membrane proteins, stability, and confidence in designing charged residues. [ABSTRACT FROM AUTHOR]

With the emergence of penicillin resistance, the development of novel antibiotics has become an urgent necessity. Semi-synthetic penicillin has emerged as a promising alternative to traditional penicillin. The demand for the crucial intermediate, 6-aminopicillanic acid (6-APA), is on the rise. Enzyme catalysis is the primary method employed for its production. However, due to certain limitations, the strategy of enzyme immobilization has also gained prominence. The magnetic Ni0.4Cu0.5Zn0.1Fe2O4 nanoparticles were successfully prepared by a rapid-combustion method. Sodium silicate was used to modify the surface of the Ni0.4Cu0.5Zn0.1Fe2O4 nanoparticles to obtain silica-coated nanoparticles (Ni0.4Cu0.5Zn0.1Fe2O4-SiO2). Subsequently, in order to better crosslink PGA, the nanoparticles were modified again with glutaraldehyde to obtain glutaraldehyde crosslinked Ni0.4Cu0.5Zn0.1Fe2O4-SiO2-GA nanoparticles which could immobilize the PGA. The structure of the PGA protein was analyzed by the PyMol program and the immobilization strategy was determined. The conditions of PGA immobilization were investigated, including immobilization time and PGA concentration. Finally, the enzymological properties of the immobilized and free PGA were compared. The optimum catalytic pH of immobilized and free PGA was 8.0, and the optimum catalytic temperature of immobilized PGA was 50°C, 5°C higher than that of free PGA. Immobilized PGA in a certain pH and temperature range showed better catalytic stability. Vmax and Km of immobilized PGA were 0.3727 μmol·min-1 and 0.0436 mol·L-1, and the corresponding free PGA were 0.7325 μmol·min-1 and 0.0227 mol·L-1. After five cycles, the immobilized enzyme activity was still higher than 25%. [ABSTRACT FROM AUTHOR]

Little information exists on the variation in morphological characteristics, nutritional value, ruminal degradability, and molecular structural makeup of diverse whole-plant silage corn (WPSC) cultivars among different growing regions. This study investigated the between-regions (Beijing, Urumchi, Cangzhou, Liaoyuan, Tianjin) discrepancies in five widely used WPSC cultivars in China (FKBN, YQ889, YQ23, DK301 and ZD958), in terms of 1) morphological characteristics; 2) crude protein (CP) chemical profile; 3) Cornell Net Carbohydrate and Protein System (CNCPS) CP subfractions; 4) in situ CP degradation kinetics; and 5) CP molecular structures. Our results revealed significant growing region and WPSC cultivar interaction for all estimated morphological characteristics (P < 0.001), CP chemical profile (P < 0.001), CNCPS subfractions (P < 0.001) and CP molecular structural features (P < 0.05). Except ear weight (P = 0.18), all measured morphological characteristics varied among different growing regions (P < 0.001). Besides, WPSC cultivars planted in different areas had remarkably different CP chemical profiles and CNCPS subfractions (P < 0.001). All spectral parameters of protein primary structure of WPSC differed (P < 0.05) due to the growing regions, except amide II area (P = 0.28). Finally, the area ratio of amide I to II was negatively correlated with the contents of soluble CP (δ = -0.66; P = 0.002), CP (δ = -0.61; P = 0.006), non-protein nitrogen (δ = -0.56; P = 0.004) and acid detergent insoluble CP (δ = -0.43; P = 0.008), in conjunction with a positive correlation with moderately degradable CP (PB1; δ = 0.58; P = 0.01). In conclusion, the cultivar of DK301 exhibited high and stable CP content. The WPSC planted in Beijing showed high CP, SCP and NPN. The low rumen degradable protein of WPSC was observed in Urumchi. Meanwhile, above changes in protein profiles and digestibility were strongly connected with the ratio of amide I and amide II. [ABSTRACT FROM AUTHOR]

Aurora kinases (AURKs) have been identified as promising biological targets for the treatment of cancer. In this study, molecular dynamics simulations were employed to investigate the binding selectivity of three inhibitors (HPM, MPY, and VX6) towards AURKA and AURKB by predicting their binding free energies. The results show that the inhibitors HPM, MPY, and VX6 have more favorable interactions with AURKB as compared to AURKA. The binding energy decomposition analysis revealed that four common residue pairs (L139, L83), (V147, V91), (L210, L154), and (L263, L207) showed significant binding energies with HPM, MPY, and VX6, hence responsible for the binding selectivity of AURKA and AURKB to the inhibitors. The MD trajectory analysis also revealed that the inhibitors affect the dynamic flexibility of protein structure, which is also responsible for the partial selectivity of HPM, MPY, and VX6 towards AURKA and AURKB. As expected, this study provides useful insights for the design of potential inhibitors with high selectivity for AURKA and AURKB. [ABSTRACT FROM AUTHOR]

We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the Cα backbone to achieve a minimal complexity curve representation of the manner in which the protein's secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results when applied to proteins recently. The SKMT smoothing is used to derive empirical bounds on a protein's entanglement relative to its number of secondary structure elements. We show that large scale helical geometries dominantly account for the maximum growth in entanglement of protein monomers, and further that this large scale helical geometry is present in a large array of proteins, consistent across a number of different protein structure types and sequences. We also show how these bounds can be used to constrain the search space of protein structure prediction from small angle x-ray scattering experiments, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. Finally we develop a structural comparison metric based on the SKMT smoothing which is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. We provide the SWRITHE interactive python notebook to calculate these metrics. Author summary: There is much interest in the development of quantitative methods to compare different protein structures or identify common substructures across protein families. As our understanding of the flexible and dynamic nature of protein structures advances it will be necessary to develop methods for comparing protein structure which accounts for this flexibility. This can be achieved by assessing and comparing the underlying shape of protein structures which are not obfuscated by the small scale (primary and secondary) complexity of the structure, and instead focus on their large scale (tertiary) entanglement. Here we present such a novel set of quantitative measures by smoothing and simplifying the amino-acid backbone into a minimal representation of its true flexibility. We demonstrate these measures of a protein chain's self-entanglement have a number of critical properties which make them potentially impactful. First, by studying the distribution of entanglement across a wide sample of proteins, we show that there exists a minimum expected amount (a lower bound) of entanglement given the protein's length. This bound is shown to be useful in ensuring realistic predictions from experimental structural determination methods. Second, using fundamental properties of this entanglement measure, we identify the presence of helical structures across various length scales in proteins, which provide stability to the structure. Third, we show they can be used to highlight significant structural similarity between two families of proteins currently classed as distinct, but which have been shown to share a surprising experimental link. Finally, we provide an interactive python notebook to compute these measures for a given protein. [ABSTRACT FROM AUTHOR]

The possibility of the protein backbone adopting lasso-like entangled motifs has attracted increasing attention. After discovering the surprising abundance of natively entangled protein domain structures, it was shown that misfolded entangled subpopulations might become thermosensitive or escape the homeostasis network just after translation. To investigate the role of entanglement in shaping folding kinetics, we introduce a novel indicator and analyze simulations of a coarse-grained, structure-based model for two small single-domain proteins. The model recapitulates the well-known two-state folding mechanism of a non-entangled SH3 domain. However, despite its small size, a natively entangled antifreeze RD1 protein displays a rich refolding behavior, populating two distinct kinetic intermediates: a short-lived, entangled, near-unfolded state and a longer-lived, non-entangled, near-native state. The former directs refolding along a fast pathway, whereas the latter is a kinetic trap, consistently with known experimental evidence of two different characteristic times. Upon trapping, the natively entangled loop folds without being threaded by the N-terminal residues. After trapping, the native entangled structure emerges by either backtracking to the unfolded state or threading through the already formed but not yet entangled loop. Along the fast pathway, trapping does not occur because the native contacts at the closure of the lasso-like loop fold after those involved in the N-terminal thread, confirming previous predictions. Despite this, entanglement may appear already in unfolded configurations. Remarkably, a longer-lived, near-native intermediate, with non-native entanglement properties, recalls what was observed in cotranslational folding. Author summary: Recently, a surprisingly large fraction of protein structures was shown to host topologically entangled motifs, whereby one protein chain portion is lassoed by a second portion, that loops between two residues in non-covalent contact with each other. Moreover, there is growing evidence that failure in adopting the correct entangled motifs may produce misfolded structures with impaired biological functions. Such structures are otherwise similar to the correct ones and can escape the cell quality control system for protein expression, leading to soluble and less functional protein species. Here, we study in detail the folding kinetics of an entangled small anti-freeze protein, using a simplified representation of the protein chain. We find a very rich folding behavior, unusual for small proteins, with different folding pathways. A fast pathway is followed if a crucial set of contacts is formed before lassoing takes place. If not, a misfolded structure which acts as a kinetic trap is formed, slowing down folding; in such structure, most of the contacts are correctly in place yet the lasso is not formed. The detailed understanding that we provide for a small protein may pave the way for similar studies for larger entangled proteins. [ABSTRACT FROM AUTHOR]

Neutralizing antibodies (NAbs) to multiple epitopes on the HIV-1-envelope glycoprotein (Env) have been isolated from infected persons. The potency of NAbs is measured more often than the size of the persistent fraction of infectivity at maximum neutralization, which may also influence preventive efficacy of active or passive immunization and the therapeutic outcome of the latter. Many NAbs neutralize HIV-1 CZA97.012, a clone of a Clade-C isolate, to ~100%. But here NAb PGT151, directed to a fusion-peptide epitope, left a persistent fraction of 15%. NAb PGT145, ligating the Env-trimer apex, left no detectable persistent fraction. The divergence in persistent fractions was further analyzed by depletion of pseudoviral populations of the most PGT151- and PGT145-reactive virions. Thereby, neutralization by the non-depleting NAb increased, whereas neutralization by the depleting NAb decreased. Furthermore, depletion by PGT151 increased sensitivity to autologous neutralization by sera from rabbits immunized with soluble native-like CZA97.012 trimer: substantial persistent fractions were reduced. NAbs in these sera target epitopes comprising residue D411 at the V4-β19 transition in a defect of the glycan shield on CZA97.012 Env. NAb binding to affinity-fractionated soluble native-like CZA97.012 trimer differed commensurately with neutralization in analyses by ELISA and surface plasmon resonance. Glycan differences between PGT151- and PGT145-purified trimer fractions were then demonstrated by mass spectrometry, providing one explanation for the differential antigenicity. These differences were interpreted in relation to a new structure at 3.4-Å resolution of the soluble CZA97.012 trimer determined by cryo-electron microscopy. The trimer adopted a closed conformation, refuting apex opening as the cause of reduced PGT145 binding to the PGT151-purified form. The evidence suggests that differences in binding and neutralization after trimer purification or pseudovirus depletion with PGT145 or PGT151 are caused by variation in glycosylation, and that some glycan variants affect antigenicity through direct effects on antibody contacts, whereas others act allosterically. Author summary: Neutralizing antibodies block the entry of HIV-1 into cells and protect against HIV-1 infection in animal models. Therefore, vaccination aims to elicit antibodies that potently neutralize most HIV-1 variants. Such antibodies suppress virus levels when given to HIV-1-infected patients. Their potency is often measured as the concentration that gives 50% or 80% neutralization. But higher degrees of neutralization are needed to protect an organism from infection. And for some antibodies a ceiling is reached, so that even with increasing concentration a constant fraction of infectious virus persists. We studied the carbohydrate moieties on the envelope glycoprotein, which is the sole target for neutralizing antibodies, of one HIV-1 isolate of the most widespread subtype, Clade C, prevalent in Africa and Asia. We show how differences in carbohydrates can contribute to persistent infectivity, because distinct carbohydrates fit different antibodies. With a new three-dimensional structure of the entry-mediating protein from the Clade-C isolate, we illustrate that some carbohydrate differences occur exactly where the antibodies bind, whereas others are located elsewhere and can act indirectly. When we combined two neutralizing antibodies, the persistent infectivity shrank. Our results reinforce the need for multiple specificities of neutralizing antibodies in prevention and therapy. [ABSTRACT FROM AUTHOR]

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody–protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces. [ABSTRACT FROM AUTHOR]

Interleukin (IL)-7 is broadly active on T-cell populations, and modified versions have been clinically evaluated for a variety of therapeutic applications, including cancer, lymphopenia, and infectious diseases; and found to be relatively well-tolerated and biologically active. Here we describe novel IL-7R agonists that are unrelated in structure to IL-7, bind to the receptor subunits differently from IL-7, but closely emulate IL-7 biology. The small size, low structural complexity, and the natural amino acid composition of the pharmacologically active peptide MDK1472 allows facile incorporation into protein structures, such as the IgG2-Fc fusion MDK-703. This molecule possesses properties potentially better suited to therapeutic applications than native IL-7 or its derivatives. We compared these compounds with IL-7 for immune cell selectivity, induction of IL-7R signaling, receptor-mediated internalization, proliferation, and generation of immune cell phenotypes in human and non-human primate (NHP) peripheral blood cells in vitro; and found them to be similar in biological activity to IL-7. In cynomolgus macaques, MDK-703 exhibits a circulating half-life of 46 hr and produces sustained T-cell expansion characteristic of IL-7 treatment. In the huCD34+-engrafted NSG mouse model of the human immune system, MDK-703 induces an immune cell profile very similar to that generated by IL-7-derived compounds; including the pronounced expansion of memory T-cells, particularly the population of stem-like memory T-cells (Tscm) which may be important for anti-tumor activities reported with IL-7 treatment. Clinical administration of IL-7 and modified variants has been reported to induce anti-drug antibodies (ADAs), including IL-7 neutralizing antibodies. The novel peptide agonist reported here scores very low in predicted immunogenicity, and because the peptide lacks sequence similarity with IL-7, the problematic immunogenic neutralization of endogenous cytokine should not occur. The properties we report here implicate MDK-703 as a candidate for clinical evaluation in oncology, anti-viral and other infectious disease, vaccine enhancement, and treatment of lymphopenia. [ABSTRACT FROM AUTHOR]

Archaeal pleomorphic viruses belonging to the Pleolipoviridae family represent an enigmatic group as they exhibit unique genomic features and are thought to have evolved through recombination with different archaeal plasmids. However, most of our understanding of the diversity and evolutionary trajectories of this clade comes from a handful of isolated representatives. Here we present 164 new genomes of pleolipoviruses obtained from metagenomic data of Australian hypersaline lakes and publicly available metagenomic data. We perform a comprehensive analysis on the diversity and evolutionary relationships of the newly discovered viruses and previously described pleolipoviruses. We propose to classify the viruses into five genera within the Pleolipoviridae family, with one new genus represented only by virus genomes retrieved in this study. Our data support the current hypothesis that pleolipoviruses reshaped their genomes through recombining with multiple different groups of plasmids, which is reflected in the diversity of their predicted replication strategies. We show that the proposed genus Epsilonpleolipovirus has evolutionary ties to pRN1-like plasmids from Sulfolobus, suggesting that this group could be infecting other archaeal phyla. Interestingly, we observed that the genome size of pleolipoviruses is correlated to the presence or absence of an integrase. Analyses of the host range revealed that all but one virus exhibit an extremely narrow range, and we show that the predicted tertiary structure of the spike protein is strongly associated with the host family, suggesting a specific adaptation to the host S-layer glycoprotein organization. Author summary: Pleomorphic viruses of the Pleolipoviridae family are abundant in hypersaline environments and are currently known to infect diverse members of halophilic archaea. Pleolipoviruses are able to establish chronic infections during which both, viruses and cells, are able to replicate and coexist, a rather rare trait for prokaryotic viruses. Our understanding of their diversity and evolution comes from a handful of isolates and remains scarce. In this study, we screened hypersaline lakes in Australia and public available databases and uncover novel pleolipoviruses, expanding the current known representatives by 10 fold, including new lineages composed of only uncultivated representatives. Furthermore, we propose a revised taxonomic organization for the Pleolipoviridae family, and shed light on the evolutionary relationships and trajectories between the different clades. Our results show that pleolipoviruses have frequently recombined with other mobile genetic elements and that their replication strategies and genomes can be very flexible. Altogether, this study sets the basis for the discovery and characterization of new pleolipoviruses and their strategies to establish chronic infections in archaea. [ABSTRACT FROM AUTHOR]

Bartonella bacilliformis is a Gram-negative, aerobic bacterium and the known causal agent of Carrion's disease, still considered a neglected disease. There is limited information about the nucleotide sequences of this bacterium in international databases, and few studies have addressed the genetic diversity of B. bacilliformis. We analyzed a total of 20 isolates of B. bacilliformis from the Peruvian regions of Ancash and Cajamarca. Three genes (ialB, gltA, and rpoB) were sequenced in each isolate and nucleotide sequences retrieved from GenBank (16 B. bacilliformis genomes) were also included in the study. All this information was merged in order to obtain clearer evidence of the phylogenetic relationships of B. bacilliformis. In the phylogenetic analysis conducted with the concatenated markers, four isolates (B.b-1, B. b-3, B. b- 7, B.b-8) from the Ancash region were observed to form a subgroup different from B. bacilliformis type strain KC583, showing dissimilarity levels of 5.96% (ialB), 3.69% (gltA) and 3.04% (rpoB). Our results suggest that B. bacilliformis consists of two different subgroups. Future investigations are needed to establish the taxonomic status of these subgroups. Author summary: Bartonella bacilliformis is the causative agent of Carrion's disease, which affects neglected communities of the South American Andes. The available information about the genomic aspect of B. bacilliformis is limited, and there is no information on the ecological and geographical bases of the divergence of this bacterium. In this study, three genes were sequenced in twenty isolates of B. bacilliformis from Ancash and Cajamarca, Peruvian endemic areas. The collected data suggest a specific subgroup of B. bacilliformis present in the Ancash region, it could therefore be assumed that there is a sub-speciation process in the strains of this endemic zone of Peru. Additionally, our findings show non-synonymous mutations that could trigger the alterations of the activity or structure of the proteins codified by the sequence genes. [ABSTRACT FROM AUTHOR]

The stable isotope technique provides the possibility to trace ancient textiles because the technique is associated with advantages such as trace indication, fast detection, and accurate results. Since different cocooning conditions may impact cocoons even under identical habitats, it is important to investigate the effects of different cocooning temperatures and humidity on the isotope incorporation values in the cocoons. In this study, silk fibers were reeled under different conditions of temperature and humidity, followed by analysis of the secondary structure of cocoon proteins and isotope incorporation patterns. We found that the deviations in carbon isotope values of silk under different cocooning conditions could reach up to 0.76‰, while the deviation in carbon isotope values at different locations of a single silk was 2.75‰. Further, during the cocooning process, depletion of the 13C-isotope at different locations of the silk fibers was observed, reducing the δ13C values. We proposed that the changes in carbon isotopes in silk were related to the content of sericin and silk fibroin in silk. Finally, we did not observe a significant difference in isotope ratios in degummed cocoons. In summary, the 13C isotope was enriched in sericin, whereas 15N was enriched in fibroin, and these findings provide basic information for tracing the provenance of silks. [ABSTRACT FROM AUTHOR]

Organ laterality of vertebrates is specified by accelerated asymmetric decay of Dand5 mRNA mediated by Bicaudal-C1 (Bicc1) on the left side, but whether binding of this or any other mRNA to Bicc1 can be regulated is unknown. Here, we found that a CRISPR-engineered truncation in ankyrin and sterile alpha motif (SAM)-containing 3 (ANKS3) leads to symmetric mRNA decay mediated by the Bicc1-interacting Dand5 3′ UTR. AlphaFold structure predictions of protein complexes and their biochemical validation by in vitro reconstitution reveal a novel interaction of the C-terminal coiled coil domain of ANKS3 with Bicc1 that inhibits binding of target mRNAs, depending on the conformation of ANKS3 and its regulation by ANKS6. The dual regulation of RNA binding by mutually opposing structured protein domains in this multivalent protein network emerges as a novel mechanism linking associated laterality defects and possibly other ciliopathies to perturbed dynamics in Bicc1 ribonucleoparticle (RNP) formation. Organ laterality of vertebrates is specified by accelerated asymmetric decay of Dand5 mRNA mediated by Bicaudal-C1 (Bicc1) on the left side, but how is this regulated? Structure-function analysis of the ciliopathy proteins ANKS3 and ANKS6 suggests a dual role in either inhibiting or promoting the binding of Bicc1 to its own transcripts and to client mRNAs. [ABSTRACT FROM AUTHOR]

In this paper, a novel bifunctional cellulase gene cel1 was cloned from Thermoascus aurantiacus by PCR and heterologously expressed in Pichia pastoris GS115. Bioinformatics and other related tools were used to compare the nucleotide homology of target genes, and analyze the signal peptide, transmembrane domain, hydrophilicity, secondary and tertiary structure of proteins. It was concluded that cel1 has similar endoglucanase nucleotide sequences and falls under the GH5 family. It was also found that cel1 has nucleotide sequences similar to glucosidase, which can infer that cel1 may have the properties of glucosidase, indicating that cel1 is multifunctional. At the same time, a part of the nucleotide sequence of the gene was removed to obtain a new gene cel2, and after highly efficient heterologous expression, its specific activity was found to be 2.1 times higher. Its enhancement is related to the exposure of the protein's hollow three-dimensional structure. This paper provides good material for exploring the relationship between the structure of bifunctional enzymes and their functions, which lays a solid foundation for further research and applications, and provides useful insight for gene mining of other novel enzymes. [ABSTRACT FROM AUTHOR]

The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than β-strands. Author summary: The study of protein folding mechanism is an important part of basic science and has vital significance in many aspects. The key to the study of protein folding mechanism is to capture the conformational changes from the fast protein folding process. Biological experiments are more difficult to obtain protein metastable structures than computational methods. It is computationally expensive to simulate the complete folding pathway of macromolecular proteins by molecular dynamics methods. Here, we design a protein folding pathway prediction method based on conformational sampling to provide new ideas for existing research. This method obtains the structural information of the intermediate state through large-scale sampling and clustering, combines the resampling algorithm to explore the transition probability of the intermediate state, and predicts the protein folding pathway. The results show that we validate the method on five widely studied proteins, and also reveal the folding mechanism of some proteins. And Pathfinder complements the existing protein folding data from the perspective of computational simulation, which needs to be further verified by biological experiments. Finally, Pathfinder predicts unresolved protein folding pathways, providing insights into unknown folding mechanisms. [ABSTRACT FROM AUTHOR]

Bacterial antibiotic resistance remains an ever-increasing worldwide problem, requiring new approaches and enzyme targets. Acinetobacter baumannii is recognised as one of the most significant antibiotic-resistant bacteria, capable of carrying up to 45 different resistance genes, and new drug discovery targets for this organism is an urgent priority. Short-chain dehydrogenase/reductase enzymes are a large protein family with >60,000 members involved in numerous biosynthesis pathways. Here, we determined the structure of an SDR protein from A. baumannii and assessed the putative co-factor comparisons with previously co-crystalised enzymes and cofactors. This study provides a basis for future studies to examine these potential co-factors in vitro. [ABSTRACT FROM AUTHOR]

Beta-barrel outer membrane proteins (OMP) are integral components of Gram-negative bacteria, eukaryotic mitochondria, and chloroplasts. They play essential roles in various cellular processes including nutrient transport, membrane stability, host-pathogen interactions, antibiotic resistance and more. The advent of AlphaFold2 for accurate protein structure predictions transformed structural bioinformatic studies. We previously used a QTY code to convert hydrophobic alpha-helices to hydrophilic alpha-helices in over 50 membrane proteins with all alpha-helices. The QTY code systematically replaces hydrophobic leucine (L), isoleucine (I), valine (V), and phenylalanine (F) with hydrophilic glutamine (Q), threonine (T), and tyrosine (Y). We here present a structural bioinformatic analysis of five outer membrane beta-barrel proteins with known molecular structures, including a) BamA, b) Omp85 (also called Sam50), c) FecA, d) Tsx, and e) OmpC. We superposed the structures of five native beta-barrel outer membrane proteins and their AlphaFold2-predicted corresponding QTY variant structures. The superposed structures of OMPs and their QTY variants exhibit remarkable structural similarity, as evidenced by residue mean square distance (RMSD) values between 0.206Å to 0.414Å despite the replacement of at least 22% (Transmembrane variation) of the amino acids in the transmembrane regions. We also show that native outer membrane proteins and QTY variants have different hydrophobicity patches. Our study provides important insights into the differences between hydrophobic and hydrophilic beta-barrels and validates the QTY code for studying beta-barrel membrane proteins and perhaps other hydrophobic aggregated proteins. Our findings demonstrate that the QTY code can be used as a simple tool for designing hydrophobic proteins in various biological contexts. [ABSTRACT FROM AUTHOR]

Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential—based on relative entropy—in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit. Author summary: Cryo-electron microscopy has gone through a revolution and now regularly produces data with 2Å resolution. However, this data comes in the shape of density maps, and fitting atomic coordinates into these maps can be a labor-intensive and challenging problem. This is particularly valid when there are multiple conformations, flexible regions, or parts of the structure with lower resolution. In many cases it is also desirable to to understand how a molecule moves between such conformations. This can be addressed with molecular dynamics simulations using densities as target restraints, but the refinement potentials commonly used can distort protein structure or get stuck in local minima when the cryo-EM map has high resolution. This work derives new refinement potentials based on models of the cryo-EM scattering process that provide a gentle way to fit protein structures to densities in simulations, and we also suggest an automated heuristic way to balance the influence of the map and simulation force field. [ABSTRACT FROM AUTHOR]

The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises. However, there is the need for models and tools to study the links between the evolution of S protein sequence, structure and functions, and virus transmissibility and the effects of vaccination. As studies on S protein have been generated a large amount of relevant information, we propose in this work to use Protein Contact Networks (PCNs) to relate protein structures with biological properties by means of network topology properties. Topological properties are used to compare the structural changes with sequence changes. We find that both node centrality and community extraction analysis can be used to relate protein stability and functionality with sequence mutations. Starting from this we compare structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. Finally by applying our model to the Omicron variant we report a timeline correlation between Omicron and the vaccination campaign. [ABSTRACT FROM AUTHOR]

The molecular factors and genetic adaptations that contributed to the emergence of Mycobacterium tuberculosis (MTB) from an environmental Mycobacterium canettii-like ancestor, remain poorly investigated. In MTB, the PhoPR two-component regulatory system controls production and secretion of proteins and lipid virulence effectors. Here, we describe that several mutations, present in phoR of M. canettii relative to MTB, impact the expression of the PhoP regulon and the pathogenicity of the strains. First, we establish a molecular model of PhoR and show that some substitutions found in PhoR of M. canettii are likely to impact the structure and activity of this protein. Second, we show that STB-K, the most attenuated available M. canettii strain, displays lower expression of PhoP-induced genes than MTB. Third, we demonstrate that genetic swapping of the phoPR allele from STB-K with the ortholog from MTB H37Rv enhances expression of PhoP-controlled functions and the capacities of the recombinant strain to colonize human macrophages, the MTB target cells, as well as to cause disease in several mouse infection models. Fourth, we extended these observations to other M. canettii strains and confirm that PhoP-controlled functions are expressed at lower levels in most M. canettii strains than in M. tuberculosis. Our findings suggest that distinct PhoR variants have been selected during the evolution of tuberculosis bacilli, contributing to higher pathogenicity and persistence of MTB in the mammalian host. Author summary: Mycobacterium tuberculosis (MTB) likely emerged from an environmental ancestor by adaptation of endogenous pathways enhancing its capacity to colonize and transmit within the human host. The closest extant relatives of the MTB ancestor are the Mycobacterium canettii strains which are opportunist pathogens responsible for rare human tuberculosis cases in East-Africa. Here we found that the sensor protein of the important two-component regulatory system PhoPR displays amino acid substitutions that modulate its activity in M. canettii in comparison to MTB. These mutations impact the production and secretion of virulence factors important for multiplication within the host and cough induction. Therefore, our findings support the hypothesis that variants of the PhoPR regulatory system were selected as key evolutionary adaptations for enhancing the epidemic capacity of the tuberculosis bacilli. [ABSTRACT FROM AUTHOR]

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors. Author summary: In recent years, significant progress has been made in the development of experimental and computational methods that predict the structure of biomolecules. As a result, we now have access to an increasing number of high-quality protein structures, which allows us to study not only the functions and mechanisms of individual proteins, but also their complexes or larger aggregates. Therefore, there is an increasing need for new computational tools to investigate larger molecular systems. To effectively model these systems, it is essential to use their simplified representation. In our research, we use the CABS model which utilizes a simplified representation of protein chains to explore two diverse biological processes: the assembly of amyloid protofilaments and the formation of enzyme-substrate complexes. We show that the proposed integrative modeling protocols are capable of predicting the structures of five protofilaments with good accuracy. Furthermore, by relying on the best-scored substrate-enzyme complexes obtained using docking simulations, we are able to identify the positions of the cleavage sites in the peptide substrates of three different proteolytic enzymes. Our results are important for theoretical studies that employ docking strategies, as they can provide valuable insights for scientists carrying out experimental research. [ABSTRACT FROM AUTHOR]

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases. [ABSTRACT FROM AUTHOR]

The mechanisms of energy generation and carbon-source utilization in the syphilis spirochete Treponema pallidum have remained enigmatic despite complete genomic sequence information. Whereas the bacterium harbors enzymes for glycolysis, the apparatus for more efficient use of glucose catabolites, namely the citric-acid cycle, is apparently not present. Yet, the organism's energy needs likely exceed the modest output from glycolysis alone. Recently, building on our structure-function studies of T. pallidum lipoproteins, we proposed a "flavin-centric" metabolic lifestyle for the organism that partially resolves this conundrum. As a part of the hypothesis, we have proposed that T. pallidum contains an acetogenic energy-conservation pathway that catabolizes D-lactate, yielding acetate, reducing equivalents for the generation and maintenance of chemiosmotic potential, and ATP. We already have confirmed the D-lactate dehydrogenase activity in T. pallidum necessary for this pathway to operate. In the current study, we focused on another enzyme ostensibly involved in treponemal acetogenesis, phosphotransacetylase (Pta). This enzyme is putatively identified as TP0094 and, in this study, we determined a high-resolution (1.95 Å) X-ray crystal structure of the protein, finding that its fold comports with other known Pta enzymes. Further studies on its solution behavior and enzyme activity confirmed that it has the properties of a Pta. These results are consistent with the proposed acetogenesis pathway in T. pallidum, and we propose that the protein be referred to henceforth as TpPta. [ABSTRACT FROM AUTHOR]

The pathogenic yeast Pichia kudriavzevii, previously known as Candida krusei, is more distantly related to Candida albicans than clinically relevant CTG-clade Candida species. Its cell wall, a dynamic organelle that is the first point of interaction between pathogen and host, is relatively understudied, and its wall proteome remains unidentified to date. Here, we present an integrated study of the cell wall in P. kudriavzevii. Our comparative genomic studies and experimental data indicate that the general structure of the cell wall in P. kudriavzevii is similar to Saccharomyces cerevisiae and C. albicans and is comprised of β-1,3-glucan, β-1,6-glucan, chitin, and mannoproteins. However, some pronounced differences with C. albicans walls were observed, for instance, higher mannan and protein levels and altered protein mannosylation patterns. Further, despite absence of proteins with high sequence similarity to Candida adhesins, protein structure modeling identified eleven proteins related to flocculins/adhesins in S. cerevisiae or C. albicans. To obtain a proteomic comparison of biofilm and planktonic cells, P. kudriavzevii cells were grown to exponential phase and in static 24-h cultures. Interestingly, the 24-h static cultures of P. kudriavzevii yielded formation of floating biofilm (flor) rather than adherence to polystyrene at the bottom. The proteomic analysis of both conditions identified a total of 33 cell wall proteins. In line with a possible role in flor formation, increased abundance of flocculins, in particular Flo110, was observed in the floating biofilm compared to exponential cells. This study is the first to provide a detailed description of the cell wall in P. kudriavzevii including its cell wall proteome, and paves the way for further investigations on the importance of flor formation and flocculins in the pathogenesis of P. kudriavzevii. Author summary: The yeast P. kudriavzevii (ex-Candida krusei) is among the five most prevalent causal agents of candidiasis but its mechanisms underlying pathogenicity have been scarcely studied. This is also true for its cell wall structure, an essential organelle that governs primary host-pathogen interactions and host immune responses. Solid knowledge about cell wall synthesis and dynamics is crucial for the development of novel antifungal strategies against this pathogenic yeast. Here, through a combination of comparative genomics, protein structure modeling, and biochemical and proteomic analysis of purified walls, we present a detailed study of the cell wall composition in P. kudriavzevii and identify important architectural differences compared to C. albicans cell walls. Cell walls of P. kudriavzevii contain higher mannan and protein levels with altered mannan branching patterns, governed by expansions and reductions in gene families encoding mannosyltransferases. We also show that, in contrast to other Candida species, static cultures produce floating biofilms. Comparative wall proteomic studies of these biofilms show increased abundance of flocculins and hydrolytic enzymes, protein classes implicated in biofilm formation and primary host-pathogen interactions leading to tissue colonization. In conclusion, our study uncovers important keys towards a better molecular understanding of the virulence mechanisms of the important pathogen P. kudriavzevii. [ABSTRACT FROM AUTHOR]

Gene- burden analyses have lately become a very successful way for the identification of genes carrying risk variants underlying the analysed disease. This approach is also suitable for complex disorders like autism spectrum disorder (ASD). The gene-burden analysis using Testing Rare Variants with Public Data (TRAPD) software was conducted on whole exome sequencing data of Slovenian patients with ASD to determine potentially novel disease risk variants in known ASD-associated genes as well as in others. To choose the right control group for testing, principal component analysis based on the 1000 Genomes and ASD cohort samples was conducted. The subsequent protein structure and ligand binding analysis usingI-TASSER package were performed to detect changes in protein structure and ligand binding to determine a potential pathogenic consequence of observed mutation. The obtained results demonstrate an association of two variants–p.Glu198Lys (PPP2R5D:c.592G>A) and p.Arg253Gln (PPP2R5D:c.758G>A) with the ASD. Substitution p.Glu198Lys (PPP2R5D:c.592G>A) is a variant, previously described as pathogenic in association with ASD combined with intellectual disability, whereas p.Arg253Gln (PPP2R5D:c.758G>A) has not been described as an ASD-associated pathogenic variant yet. The results indicate that the filtering process was suitable and could be used in the future for detection of novel pathogenic variants when analysing groups of ASD patients. [ABSTRACT FROM AUTHOR]

One of the main characteristics of prokaryotic genomes is the ratio in which guanine-cytosine bases are used in their DNA sequences. This is known as the genomic GC content and varies widely, from values below 20% to values greater than 74%. It has been demonstrated that the genomic GC content varies in accordance with the phylogenetic distribution of organisms and influences the amino acid composition of their corresponding proteomes. This bias is particularly important for amino acids that are coded by GC content-rich codons such as alanine, glycine, and proline, as well as amino acids that are coded by AT-rich codons, such as lysine, asparagine, and isoleucine. In our study, we extend these results by considering the effect of the genomic GC content on the secondary structure of proteins. On a set of 192 representative prokaryotic genomes and proteome sequences, we identified through a bioinformatic study that the composition of the secondary structures of the proteomes varies in relation to the genomic GC content; random coils increase as the genomic GC content increases, while alpha-helices and beta-sheets present an inverse relationship. In addition, we found that the tendency of an amino acid to form part of a secondary structure of proteins is not ubiquitous, as previously expected, but varies according to the genomic GC content. Finally, we discovered that for some specific groups of orthologous proteins, the GC content of genes biases the composition of secondary structures of the proteins for which they code. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations, at different scales, have been exploited for investigating complex mechanisms ruling biologically inspired systems. Nonetheless, with recent advances and unprecedented achievements, the analysis of molecular dynamics simulations requires customized workflows. In 2018, we developed Morphoscanner to retrieve structural relations within self-assembling peptide systems. In particular, we conceived Morphoscanner for tracking the emergence of β-structured domains in self-assembling peptide systems. Here, we introduce Morphoscanner2.0. Morphoscanner2.0 is an object-oriented library for structural and temporal analysis of atomistic and coarse-grained molecular dynamics (CG-MD) simulations written in Python. The library leverages MDAnalysis, PyTorch and NetworkX to perform the pattern recognition of secondary structure patterns, and interfaces with Pandas, Numpy and Matplotlib to make the results accessible to the user. We used Morphoscanner2.0 on both simulation trajectories and protein structures. Because of its dependencies on the MDAnalysis package, Morphoscanner2.0 can read several file formats generated by widely-used molecular simulation packages such as NAMD, Gromacs, OpenMM. Morphoscanner2.0 also includes a routine for tracking the alpha-helix domain formation. [ABSTRACT FROM AUTHOR]

Mitochondrial dysfunction is implicated in a wide array of human diseases ranging from neurodegenerative disorders to cardiovascular defects. The coordinated localization and import of proteins into mitochondria are essential processes that ensure mitochondrial homeostasis. The localization and import of most mitochondrial proteins are driven by N-terminal mitochondrial targeting sequences (MTS's), which interact with import machinery and are removed by the mitochondrial processing peptidase (MPP). The recent discovery of internal MTS's—those which are distributed throughout a protein and act as import regulators or secondary MPP cleavage sites–has expanded the role of both MTS's and MPP beyond conventional N-terminal regulatory pathways. Still, the global mutational landscape of MTS's remains poorly characterized, both from genetic and structural perspectives. To this end, we have integrated a variety of tools into one harmonized R/Shiny database called MTSviewer (https://neurobioinfo.github.io/MTSvieweR/), which combines MTS predictions, cleavage sites, genetic variants, pathogenicity predictions, and N-terminomics data with structural visualization using AlphaFold models of human and yeast mitochondrial proteomes. Using MTSviewer, we profiled all MTS-containing proteins across human and yeast mitochondrial proteomes and provide multiple case studies to highlight the utility of this database. [ABSTRACT FROM AUTHOR]

Compartmentalization of RNAs and proteins into membraneless structures called granules is a ubiquitous mechanism for organizing and regulating cohorts of RNAs. Germ granules are ribonucleoprotein (RNP) assemblies required for germline development across the animal kingdom, but their regulatory roles in germ cells are not fully understood. We show that after germ cell specification, Drosophila germ granules enlarge through fusion and this growth is accompanied by a shift in function. Whereas germ granules initially protect their constituent mRNAs from degradation, they subsequently target a subset of these mRNAs for degradation while maintaining protection of others. This functional shift occurs through the recruitment of decapping and degradation factors to the germ granules, which is promoted by decapping activators and renders these structures P body-like. Disrupting either the mRNA protection or degradation function results in germ cell migration defects. Our findings reveal plasticity in germ granule function that allows them to be repurposed at different stages of development to ensure population of the gonad by germ cells. Additionally, these results reveal an unexpected level of functional complexity whereby constituent RNAs within the same granule type can be differentially regulated. Condensates of RNAs and proteins called "germ granules" are ubiquitous among animal germ cells. This study shows that Drosophila germ granules, which initially protect RNAs, subsequently become sites of selective mRNA degradation; this plasticity supports germ cell development. [ABSTRACT FROM AUTHOR]