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Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics. [ABSTRACT FROM AUTHOR]

This article discusses the challenges and importance of accurately annotating transporters in metabolic modeling. It emphasizes the need for investment in accurate functional characterization to improve predictive tools and benefit fields such as personalized medicine, metabolic engineering, and microbial ecology. The text also discusses the types of errors that can occur in transporter annotations and the databases and tools available for transporter annotation. The document is a list of references to various scientific articles and databases related to protein subcellular localization, phenotypic testing, gene function, transporter systems, metabolic modeling, and microbial communities. These resources provide valuable information for researchers studying these areas of biology and can be used as references for further exploration. [Extracted from the article]

Artificial intelligence (AI) tools are changing the way we do science. AlphaFold basically solved the conundrum of protein structure prediction; DeepMD greatly improved the efficiency and accuracy of molecular simulations; and the emerging large language models such as ChatGPT are opening up more possibilities for scientific applications. In this panel, five experts from China and the US discussed the concept, development, bottlenecks and opportunities of AI for Science (AI4S), as well as their understanding of the relationship between AI and science. Roberto Car Professor at Department of Chemistry, Princeton University, USA Weinan E Professor at School of Mathematical Sciences, Peking University, China; AI for Science Institute, Beijing, China David Srolovitz Professor at Department of Mechanical Engineering, University of Hong Kong, China Han Wang Professor at the Institute of Applied Physics and Computational Mathematics, Chinese Academy of Sciences, China Linfeng Zhang (Chair) Chief scientific officer of DP Technology, China; AI for Science Institute, Beijing, China [ABSTRACT FROM AUTHOR]

Protein and nucleic acid binding site prediction is a critical computational task that benefits a wide range of biological processes. Previous studies have shown that feature selection holds particular significance for this prediction task, making the generation of more discriminative features a key area of interest for many researchers. Recent progress has shown the power of protein language models in handling protein sequences, in leveraging the strengths of attention networks, and in successful applications to tasks such as protein structure prediction. This naturally raises the question of the applicability of protein language models in predicting protein and nucleic acid binding sites. Various approaches have explored this potential. This paper first describes the development of protein language models. Then, a systematic review of the latest methods for predicting protein and nucleic acid binding sites is conducted by covering benchmark sets, feature generation methods, performance comparisons, and feature ablation studies. These comparisons demonstrate the importance of protein language models for the prediction task. Finally, the paper discusses the challenges of protein and nucleic acid binding site prediction and proposes possible research directions and future trends. The purpose of this survey is to furnish researchers with actionable suggestions for comprehending the methodologies used in predicting protein–nucleic acid binding sites, fostering the creation of protein-centric language models, and tackling real-world obstacles encountered in this field. [ABSTRACT FROM AUTHOR]

Motivation While the release of AlphaFold (AF) represented a breakthrough for the prediction of protein complex structures, its sensitivity, especially when using full length protein sequences, still remains limited. Modeling success rates might increase if AF predictions were guided by likely interacting protein fragments. This approach requires available sets of highly confident protein–protein interface types. Computational resources, such as 3did, infer interacting globular domain types from observed contacts in protein structures. Assessing the accuracy of these predicted interface types is difficult because we lack hand-curated reference sets of verified domain–domain interface (DDI) types. Results To improve protein complex modeling of DDIs by AF, we manually inspected 80 randomly selected DDI types from the 3did resource to generate a first reference set of DDI types. Identified cases of DDI type nonapproval (40%) primarily resulted from inaccurate Pfam domain matches, crystal contacts, and synthetic protein constructs. Using logistic regression, we predicted a subset of 2411 out of 5724 considered DDI types in 3did to be of high confidence, which we subsequently applied to 53 000 human–protein interactions to predict DDIs followed by AF modeling. We obtained highly confident AF models for 604 out of 1129 predicted DDIs. Of note, for 47% of them no confident AF structural model could be obtained using full length protein sequences. Availability and implementation Code is available at https://github.com/KatjaLuckLab/DDI_manuscript. [ABSTRACT FROM AUTHOR]

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein–protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation. [ABSTRACT FROM AUTHOR]

【Objective】Spodoptera frugiperda J. E. Smith is an important invasive pest that poses a great threat to food security in China. Basic helix-loop-helix (bHLH) gene family transcription factors play important roles in insect growth and development. Therefore, they may be candidate target genes for RNAi pesticides. In order to determine the relevant targets of S. frugiperda bHLH genes, the information of S. frugiperda bHLH gene family members was identified and analyzed. 【Method】Based on the genome-wide data, the physicochemical properties, subcellular localization, conserved protein domain, phylogenetic evolution, gene chromosome localization and tertiary protein structure prediction of the bHLH gene family members in S. frugiperda were analyzed.【Result】A total of 56 bHLH genes were identified in the genome of S. frugiperda, encoding 54 different protein sequences. The number of amino acids of SfbHLH transcription factors was 84-2557, the isoelectric point was 4.88-10.76, and the relative molecular weight was 9 751.19-285 517.20. The members of the bHLH gene family were divided into six groups from A to F, with 25, 9, 10, 1, 10 and 1 genes encoding 24, 9, 10, 1, 9 and 1 different protein sequences, respectively. Members of the same group had similar types of motif, and most bHLH domains of the same group were grouped together within the same branch in phylogenetic analysis, indicating that they were conserved in evolution. Group A member HLH4C (Sfru006728.1) and Group B member guanylate cyclase Gyc76C (Sfru012226.1) were predicted to contain not only bHLH, but also the conserved domain of genes related to guanylate metabolism, and they had more complex tertiary structure than other genes in the same group. It was speculated that both of them may be derived from the duplication and overlap of guanylate metabolism genes and bHLH genes in evolution, and may have unknown complex physiological functions. 【Conclusion】The 56 bHLH gene family members of S. frugiperda were identified and analyzed to obtain their sequence, physicochemical properties, subcellular location, structure and evolution information, which is helpful to further study the functions of the bHLH transcription factor family members and screen the RNAi pesticide targets of S. frugiperda. [ABSTRACT FROM AUTHOR]

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three‐dimensional (3D) structures of fusion proteins remains a difficult task because the current AI‐based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template‐based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi‐level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr‐Rosetta and D‐I‐TASSER for modelling the 3D structures. Highlights: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein.This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step.This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models.This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures. [ABSTRACT FROM AUTHOR]

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein–protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host–pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance. [ABSTRACT FROM AUTHOR]

Proteins play a crucial role in various biological processes, serving as the building blocks and machines of life. Therefore, understanding their structure and function is paramount for advancing our knowledge of structural biology. The Protein Data Bank (PDB) [3] files have been an integral part of helping researchers decipher the complex workings of proteins. PDB files provide three-dimensional Cartesian coordinates of protein structures which are used as a stepping stone for other protein structure tools, such as protein classification. Efficient protein classification is vital for organizing and categorizing the large number of proteins discovered to date. It enables researchers to identify functional relationships, predict protein functions, and gain insights into their evolutionary history. However, current protein structural classification systems like CATH [22], SCOP [2] and SCOPe [5] have some limitations, such as complicated protein structure domain descriptions, manual and subjective classification, lack of customizability for users with different classification needs, and handling the increasing volume of protein structure data. Recently, image processing has advanced significantly, mainly due to neural networks such as Convolutional Neural Networks (CNNs) and auto-encoders. This work aims to harness the remarkable success of learned representations and CNNs for image processing by proposing a foundation model for the development of new encodings from three-dimensional protein structure information for various classification needs. The protein encodings will be helpful in other protein structure-related problems such as protein structure prediction, protein function prediction, and drug discovery. [ABSTRACT FROM AUTHOR]

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS in EA in ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS in EA in ANN performs significantly better than a canonical genetic algorithm (GA in ANN) and the evolutionary algorithm without reinforcement learning (EA in ANN). Analyses of the parameter's frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS in EA in ANN outperforms other approaches considered the state of the art for the addressed datasets. [ABSTRACT FROM AUTHOR]

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three‐dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D‐AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D‐AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/. [ABSTRACT FROM AUTHOR]

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns. Author summary: Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function, and expand drug discovery and development for this important family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning, like AlphaFold2 and ESMFold, have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the multiple sequence alignment depth used in the AlphaFold2 algorithm can help explore the kinase conformational space more broadly. Through a series of quantitative and visual analyses of the models, we also offer a critique of AlphaFold2-generated models, highlighting their potential utility in drug discovery, but also underscoring the need for further refinement to enhance their suitability for rational drug design. Furthermore, many of the high-quality and high-enriching models we make available may represent starting points for novel drug discovery campaigns. [ABSTRACT FROM AUTHOR]

Sedum plumbizincicola is a renowned hyperaccumulator of cadmium (Cd), possesses significant potential for eco-friendly phytoremediation of soil contaminated with Cd. Nevertheless, comprehension of the mechanisms underpinning its Cd stress response remains constrained, primarily due to the absence of a comprehensive genome sequence and an established genetic transformation system. In this study, we successfully identified a novel protein that specifically responds to Cd stress through early comparative iTRAQ proteome and transcriptome analyses under Cd stress conditions. To further investigate its structure, we employed AlphaFold, a powerful tool for protein structure prediction, and found that this newly identified protein shares a similar structure with Arabidopsis AtSIZ1. Therefore, we named it Sedum plumbizincicola SIZ1 (SpSIZ1). Our study revealed that SpSIZ1 plays a crucial role in positively regulating Cd tolerance through its coordination with SpABI5. Overexpression of SpSIZ1 significantly enhanced plant resistance to Cd stress and reduced Cd accumulation. Expression pattern analysis revealed higher levels of SpSIZ1 expression in roots compared to stems and leaves, with up-regulation under Cd stress induction. Importantly, overexpressing SpSIZ1 resulted in lower Cd translocation factors (Tfs) but maintained relatively constant Cd levels in roots under Cd stress, leading to enhanced Cd stress resistance in plants. Protein interaction analysis revealed that SpSIZ1 interacts with SpABI5, and the expression of genes responsive to abscisic acid (ABA) through SpABI5-dependent signaling was significantly up-regulated in SpSIZ1-overexpressing plants with Cd stress treatment. Collectively, our results illustrate that SpSIZ1 interacts with SpABI5, enhancing the expression of ABA downstream stressrelated genes through SpABI5, thereby increasing Cd tolerance in plants. [ABSTRACT FROM AUTHOR]

The article discusses the crystal structure of the MAGEA4 MHD-RAD18 R6BD complex and compares it to the AlphaFold2 prediction. The authors found that the binding mode of the RAD18 R6BD peptide in the crystal structure is flipped compared to the AlphaFold2 model. They also highlight the limitations of AlphaFold2, emphasizing that it is a statistical method based on existing experimental structures and may be biased by those structures. The authors conclude that their structural elucidation provides molecular evidence to clarify the confusion caused by the AlphaFold2 modeling and can aid in the development of MAGEA4-RAD18 antagonists for cancer treatment. [Extracted from the article]

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) and mung bean (Vigna radiata) are key legumes with global importance, but their mechanisms for coping with cold stress—a major challenge in agriculture—have not been thoroughly investigated, especially in a comparative study. This research aimed to fill this gap by examining how these two major legumes respond differently to cold stress and exploring the role of uniconazole, a potential stress mitigator. Our comprehensive approach involved transcriptomic and metabolomic analyses, revealing distinct responses between soybean and mung bean under cold stress conditions. Notably, uniconazole was found to significantly enhance cold tolerance in mung bean by upregulating genes associated with photosynthesis, while its impact on soybean was either negligible or adverse. To further understand the molecular interactions, we utilized advanced machine learning algorithms for protein structure prediction, focusing on photosynthetic pathways. This enabled us to identify LOC106780309 as a direct binding target for uniconazole, confirmed through isothermal titration calorimetry. This research establishes a new comparative approach to explore how soybean and mung bean adapt to cold stress, offers key insights to improve the hardiness of legumes against environmental challenges, and contributes to sustainable agricultural practices and food security. [ABSTRACT FROM AUTHOR]

Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue–residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions. [ABSTRACT FROM AUTHOR]

TIR1/AFB proteins are a class of auxin receptors with key roles in plant development and biotic and abiotic stress responses; several have been identified as targets of the auxin-mimicking herbicide picloram. In this study, we identified five putative TIR1/AFB gene family members in the important vegetable crop Solanum melongena (eggplant) and characterized them using bioinformatics tools and gene expression analyses. Phylogenetic analysis of the TIR1/AFBs classified them into three subgroups based on their Arabidopsis and Solanum lycopersicum homologs. AFB6 homologs were present only in S. melongena and S. lycopersicum, whereas AFB2/3 homologs were found only in Arabidopsis. One pair of S. melongena TIR1 homologs were located in syntenic regions in the genome and appeared to have arisen by segmental duplication. Promoter analysis revealed 898 cis-elements in the TIR1/AFB promoters, 125 of which were related to hormones, stress, light, or growth responses, but only SmAFB5 had a cis-acting regulatory element involved in auxin responsiveness (AuxRR-core). RNA sequencing and expression profiling showed that the TIR1/AFB genes were differentially expressed at different growth stages and in response to light, temperature, and drought. Only SmTIR1A expression was significantly induced by picloram treatment and different growth stages. TIR1/AFB expression is regulated by microRNAs (miRNAs) in other plant species, and we identified 6 or 29 miRNAs that potentially targeted the five TIR1/AFB genes on the basis of comparisons with S. lycopersicum and S. tuberosum miRNAs, respectively. Three-dimensional protein structure predictions revealed that all the TIR1/AFB proteins were very similar in structure, differing only in the numbers of alpha helices and in one angle linking an α helix and a β sheet. For measuring the function of TIR1/AFB genes in response to drought, SmAFB5 was selected, and knockdown by virus-induced gene silence (VIGS) 35S::SmAFB5 lines showed resistance to drought compared to controls. These analyses provide insight into the potential functions of TIR1/AFBs during growth and in response to stress; they highlight differences among the SmTIR1/AFBs that may be useful for eggplant breeding. [ABSTRACT FROM AUTHOR]

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. [ABSTRACT FROM AUTHOR]

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure. [ABSTRACT FROM AUTHOR]

Motivation The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Results Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. Availability and implementation The source code of ProteStAr is available at https://github.com/refresh-bio/protestar. [ABSTRACT FROM AUTHOR]

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs. [ABSTRACT FROM AUTHOR]

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold. [ABSTRACT FROM AUTHOR]

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response. [ABSTRACT FROM AUTHOR]

Background: Deafness autosomal dominant 2A (DFNA2A) is related to non‐syndromic genetic hearing impairment. The KCNQ4 (Potassium Voltage‐Gated Channel Subfamily Q Member 4) can lead to DFNA2A. In this study, we report a case of autosomal dominant non‐syndromic hearing loss with six family members as caused by a novel variant in the KCNQ4 gene. Methods: The whole‐exome sequencing (WES) and pure tone audiometry were performed on the proband of the family. Sanger sequencing was conducted on family members to determine if the novel variant in the KCNQ4 gene was present. Evolutionary conservation analysis and computational tertiary structure protein prediction of the wild‐type KCNQ4 protein and its variant were then performed. In addition, voltage‐gated channel activity of the wild‐type KCNQ4 protein and its variant were tested using whole‐cell patch clamp. Results: It was observed that the proband had inherited autosomal dominant, non‐syndromic sensorineural hearing loss as a trait. A novel co‐segregating heterozygous missense variant (c.902C>A, p.Ala301Asp) of the KCNQ4 gene was identified in the proband and other five affected family members. This variant was predicted to cause an alanine‐to‐aspartic acid substitution at position 301 in the KCNQ4 protein. The alanine at position 301 is well conserved across different species. Whole‐cell patch clamp showed that there was a significant difference between the WT protein currents and the mutant protein currents in the voltage‐gated channel activity. Conclusion: In the present study, performing WES in conjunction with Sanger sequencing enhanced the detection of a novel, potentially causative variant (c301 A>G; p.Ala301Asp) in exon 6 of the KCNQ4 gene. Therefore, our findings contributed to the mutation spectrum of the KCNQ4 gene and may be useful in the diagnosis and gene therapy of deafness autosomal dominant 2A. [ABSTRACT FROM AUTHOR]

We report the discovery of a satellite-helper phage system with a novel type of dependence on a tail donor. The Acinetobacter baumannii satellite podovirus Aci01-2- Phanie (short name Phanie) uses a phage phi29-like DNA replication and packaging mode. Its linear 11,885 bp dsDNA genome bears 171 bp inverted terminal repeats (ITR). Phanie is related to phage DU-PP-III from Pectobacterium and to members of the Astrithrvirus from Salmonella enterica. Together, they form a new clade of phages with 27% to 30% identity over the whole genome. Detailed 3D protein structure prediction and mass spectrometry analyses demonstrate that Phanie encodes its capsid structural genes and genes necessary to form a short tail. However, our study reveals that Phanie virions are non-infectious unless they associate with the contractile tail of an unrelated phage, Aci01-1, to produce chimeric myoviruses. Following the coinfection of Phanie with myovirus Aci01-1, hybrid viral particles composed of Phanie capsids and Aci01-1 contractile tails are assembled together with Phanie and Aci01-1 particles. IMPORTANCE There are few reported cases of satellite-helper phage interactions but many more may be yet undiscovered. Here we describe a new mode of satellite phage dependence on a helper phage. Phanie, like phage phi29, replicates its linear dsDNA by a protein primed-mechanism and protects it inside podovirus-like particles. However, these particles are defective, requiring the acquisition of the tail from a myovirus helper for production of infectious virions. The formation of chimeras between a phi29-like podovirus and a helper contractile tail reveals an unexpected association between very different bacterial viruses. [ABSTRACT FROM AUTHOR]

Hereditary spastic paraplegia (HSP) is a rare neurodegenerative disease prominently characterized by slowly progressive lower limb weakness and spasticity. The significant genotypic and phenotypic heterogeneity of this disease makes its accurate diagnosis challenging. In this study, we identified the NM_001168272: c.2714A  >  G (chr3.hg19: g.4716912A  >  G, N905S) variant in the ITPR1 gene in a three-generation Chinese family with multiple individuals affected by HSP, which we believed to be associated with HSP pathogenesis. To confirm, we performed whole exome sequencing, copy number variant assays, dynamic mutation analysis of the entire family, and protein structure prediction. The variant identified in this study was in the coupling domain, and this is the first corroborated report assigning ITPR1 variants to HSP. These findings expand the clinical and genetic spectrum of HSP and provide important data for its genetic analysis and diagnosis. [ABSTRACT FROM AUTHOR]

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology, for its groundbreaking contribution to solving the "protein folding problem". In this perspective, we explore the connection between protein structure studies and environmental research, delving into the potential for addressing specific environmental challenges. Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity. However, structural studies on proteins with environmental significance remain scarce. Here, we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods. Specifically, the latest progress in environmental research via cryogenic electron microscopy is highlighted. It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution, revealing environmentally-associated structural dynamics. With the remarkable advancements in computational power and experimental resolution, the study of protein structure and dynamics has reached unprecedented depth and accuracy. These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases. Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications, such as the degradation of persistent contaminants, and the recovery of valuable metals as well as rare earth elements. [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]

Occult hepatitis B virus infection (OBI) is characterized by the presence of HBV DNA in the absence of detectable HBsAg. OBI is an important risk factor for cirrhosis and hepatocellular carcinoma, but its pathogenesis has not been fully elucidated. Mutations in the HBV preS/S genes can lead to impaired secretion of either HBsAg or S-protein resulting in the accumulation of defective viruses or S protein in cells. In our previous work, the M133S mutation was present in the HBV S gene of maintenance hemodialysis (MHD) patients with OBI. In this study, we investigated the potential role of amino acid substitutions in S proteins in S protein production and secretion through the construction of mutant S gene plasmids, structural prediction, transcriptome sequencing analysis, and in vitro functional studies. Protein structure prediction showed that the S protein M133S mutant exhibited hydrophilic modifications, with greater aggregation and accumulation of the entire structure within the membrane phospholipid bilayer. Differential gene enrichment analysis of transcriptome sequencing data showed that differentially expressed genes were mainly concentrated in protein processing in the endoplasmic reticulum (ER). The expression of heat shock family proteins and ER chaperone molecules was significantly increased in the wild-type and mutant groups, whereas the expression of mitochondria-associated proteins was decreased. Immunofluorescence staining and protein blotting showed that the endoplasmic reticulum-associated protein PDI, the autophagy marker LC3, and the lysosome-associated protein LAMP2 co-localized with the S proteins in the wild-type and mutant strains, and their expression was increased. The mitochondria-associated TOMM20 protein was also co-expressed with the S protein, but expression was significantly reduced in the mutant. The M133S mutation in the S gene is expressed as a defective and misfolded protein that accumulates in the endoplasmic reticulum causing secretion-impaired endoplasmic reticulum stress, which in turn triggers mitochondrial autophagy and recruits lysosomes to fuse with the autophagosome, leading to mitochondrial clearance. This study preliminarily demonstrated that the mutation of M133S in the S gene can cause OBI and is associated with disease progression, providing a theoretical basis for the diagnosis and treatment of OBI. [ABSTRACT FROM AUTHOR]

This research investigates the utility of the AlphaFold technique, a deep gaining knowledge of-primarily based method, for the perfect prediction of protein tertiary structures from their corresponding amino acid sequences. The alpha-fold method is a deep learning-based approach that has been shown to accurately predict the 3D structure of proteins with a high degree of accuracy. This involve training the AlphaFold model on a large dataset of known protein structures and their corresponding amino acid sequences, and then using the trained model to predict the structures of novel proteins. The accuracy of the predictions will be evaluated using a variety of metrics, by comparing the reference and predicted structures and Global Distance Test (GDT). The advantages of using the alpha-Fold method, including its high accuracy and wide range of protein targets, as well as its limitations and challenges, such as the need for high computational resources and the limited availability of experimental validation data. The potential applications of accurate protein structure prediction are vast, including aiding in drug discovery, understanding protein function, and guiding experimental studies. The use of the alpha-fold method has the potential to significantly improve the accuracy of protein structure predictions and advance our understanding of the complex world of proteins. [ABSTRACT FROM AUTHOR]

The polymorphic membrane proteins (Pmps) are a family of autotransporters that play an important role in infection, adhesion and immunity in Chlamydia trachomatis. Here we show that the characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence, which correspond to the coils of a large beta-helical domain in high quality structure predictions. Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins. While the tetrapeptide motifs themselves are predicted to play a structural role in folding and close stacking of the beta-helical backbone of the passenger domain, we found many of the interesting features of Pmps are localized to the side loops jutting out from the beta helix including protease cleavage, host cell adhesion, and B-cell epitopes; while T-cell epitopes are predominantly found in the beta-helix itself. This analysis more accurately defines the Pmp family of Chlamydia and may better inform rational vaccine design and functional studies. [ABSTRACT FROM AUTHOR]

Grain shape is one of the most important factors that affects rice yield. Cloning novel grain shape genes and analyzing their genetic mechanisms are crucial for high yield breeding. In this study, a slender grain CSSL-Z485 with 3-segments substitution in the genetic background of Nipponbare was constructed in rice. Cytological analysis showed that the longer grain length of Z485 was related to the increase in glume cell numbers, while the narrower grain width was associated with the decrease in cell width. Three grain shape-related quantitative trait locus (QTLs), including qGL12, qGW12, and qRLW12, were identified through the F2 population constructed from a cross between Nipponbare and Z485. Furthermore, four single segment substitution lines (SSSLs, S1-S4) carrying the target QTLs were dissected from Z485 by MAS. Finally, three candidate genes of qGL12 for grain length and qGW12 for grain width located in S3 were confirmed by DNA sequencing, RT-qPCR, and protein structure prediction. Specifically, candidate gene 1 encodes a ubiquitin family protein, while candidate genes 2 and 3 encode zinc finger proteins. The results provide valuable germplasm resources for cloning novel grain shape genes and molecular breeding by design. [ABSTRACT FROM AUTHOR]

Aims: Colony stimulating factor 1 receptor (CSF1R)‐related leukoencephalopathy is a rapidly progressing neurodegenerative disease caused by CSF1R gene mutations. This study aimed to identify and investigate the effect of a novel intronic mutation (c.1754‐3C>G) of CSF1R on splicing. Methods: A novel intronic mutation was identified using whole‐exome sequencing. To investigate the impact of this mutation, we employed various bioinformatics tools to analyze the transcription of the CSF1R gene and the three‐dimensional structure of its encoded protein. Furthermore, reverse transcription polymerase chain reaction (RT‐PCR) was performed to validate the findings. Results: A novel mutation (c.1754‐3C>G) in CSF1R was identified, which results in exon 13 skipping due to the disruption of the 3′ splice site consensus sequence NYAG/G. This exon skipping event was further validated in the peripheral blood of the mutation carrier through RT‐PCR and Sanger sequencing. Protein structure prediction indicated a disruption in the tyrosine kinase domain, with the truncated protein showing significant structural alterations. Conclusions: Our findings underscore the importance of intronic mis‐splicing mutations in the diagnosis and management of CSF1R‐related leukoencephalopathy. [ABSTRACT FROM AUTHOR]

Recent cryo-electron microscopy (cryo-EM) structures have offered critical new insights into the molecular mechanisms governing tRNA biogenesis. Structural and functional studies along with new advances in tRNA sequencing have revealed new details on the processing of tRNAs into tRNA-derived fragments (tRFs) by RNase A family members, RNase L, and Dicer. Recent structural advances and reconstitution experiments have provided new insights into the mechanism of surveillance and decay of tRNAs by exoribonucleases, including the Xrn nucleases and the exosome. Ribonucleases (RNases) such as the SLFN family, ANKZF1, and SAMD9 have new links with tRNA quality control and cellular defense mechanisms. Defining the cellular cues and molecular mechanisms that regulate tRNA processing and decay will enhance our understanding of human diseases associated with tRNA dysregulation. Ribonucleases (RNases) play important roles in supporting canonical and non-canonical roles of tRNAs by catalyzing the cleavage of the tRNA phosphodiester backbone. Here, we highlight how recent advances in cryo-electron microscopy (cryo-EM), protein structure prediction, reconstitution experiments, tRNA sequencing, and other studies have revealed new insight into the nucleases that process tRNA. This represents a very diverse group of nucleases that utilize distinct mechanisms to recognize and cleave tRNA during different stages of a tRNA's life cycle including biogenesis, fragmentation, surveillance, and decay. In this review, we provide a synthesis of the structure, mechanism, regulation, and modes of tRNA recognition by tRNA nucleases, along with open questions for future investigation. [ABSTRACT FROM AUTHOR]

Motivation Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry. Results In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information. Availability and implementation MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use. [ABSTRACT FROM AUTHOR]

Cysteines (Cys) are chemically reactive amino acids containing sulfur that play diverse roles in plant biology. Recent proteomics investigations in Arabidopsis thaliana have revealed the presence of thiol post-translational modifications (PTMs) in several Cys residues. These PTMs are presumed to impact protein structure and function, yet mechanistic data regarding the specific Cys susceptible to modification and their biochemical relevance remain limited. To help address these limitations, we have conducted a wide-ranging analysis by integrating published datasets encompassing PTM proteomics (comparing S-sulfenylation, persulfidation, S-nitrosylation and S-acylation), genomics and protein structures, with a specific focus on proteins involved in plant lipid metabolism. The prevalence and distribution of modified Cys residues across all analyzed proteins is diverse and multifaceted. Nevertheless, by combining an evaluation of sequence conservation across 100+ plant genomes with AlphaFold-generated protein structures and physicochemical predictions, we have unveiled structural propensities associated with Cys modifications. Furthermore, we have identified discernible patterns in lipid biochemical pathways enriched with Cys PTMs, notably involving beta-oxidation, jasmonic acid biosynthesis, fatty acid biosynthesis and wax biosynthesis. These collective findings provide valuable insights for future investigations targeting the mechanistic foundations of Cys modifications and the regulation of modified proteins in lipid metabolism and other metabolic pathways. [ABSTRACT FROM AUTHOR]

Google DeepMind's protein structure prediction algorithm, AlphaFold, has received an update that expands its predictive capabilities to include a broad range of biomolecular interactions. The update has sparked controversy among researchers due to the lack of open-source code accompanying the publication in Nature. Many scientists have expressed frustration at the limited accessibility of AlphaFold 3, as it is only available through a web server with restrictions on inputs and usage. The scientific community is calling for greater transparency and reproducibility in the field of protein structure prediction. [Extracted from the article]

The article discusses the development of an AI-based algorithm called DMFold for predicting the structure of protein-protein complexes. Proteins carry out important biological functions through their interactions with other proteins, known as protein-protein interactions (PPIs). Determining the three-dimensional structures of PPI complexes is crucial for understanding their functions and developing drugs that target PPIs. The DMFold algorithm constructs precise multiple sequence alignments (MSAs) and utilizes co-evolutionary information to predict the structure of PPI complexes. In blind tests, DMFold outperformed other methods, including the widely acclaimed AlphaFold2, in predicting the structures of protein complexes. The algorithm has the potential to enhance protein function annotations and aid in the discovery of drugs targeting PPI-related diseases. [Extracted from the article]

Drug-resistant Morganella morganii, a rod-shaped, Gram-negative, facultatively anaerobic bacillus belonging to the Enterobacteriaceae family, is a growing worldwide health concern due to its association with high morbidity and mortality rates. Recent advancements in machine learning, particularly Alphafold 2's protein structure prediction using local physics and pattern recognition, have aided research efforts. This study focuses on the enzymatic activity of aminoglycoside N6′-acetyltransferase (aacA7), a critical transferase enzyme in bacteria that confers resistance to aminoglycosides. AacA7 modifies aminoglycoside molecules by catalyzing the acetylation of their 6′-amino group using acetyl-CoA, rendering antibiotics like kanamycin, neomycin, tobramycin, and amikacin inactive. We propose that Doripenem and OncoglabrinolC can interact with aacA7, potentially modifying its enzymatic activity. Molecular docking analysis of aacA7 with 22 drug targets revealed OncoglabrinolC as the most promising candidate, exhibiting a binding energy of −12.82 kcal/mol. These two top candidates, OncoglabrinolC and Doripenem, were then subjected to 100 ns of molecular dynamic simulations to assess their dynamic conformational features. Furthermore, the PredictSNP consensus classifier was used to predict the impact of mutations on aacA7 protein functionality. The study also investigated the interaction of wild-type and mutant aacA7 proteins with both Doripenem and OncoglabrinolC. These findings provide valuable insights into the binding behavior of OncoglabrinolC and Doripenem as potential lead molecules for repurposing against aacA7, potentially reducing the pathogenicity of Morganella morganii. [ABSTRACT FROM AUTHOR]

Throughout history, vector-borne diseases have consistently posed significant challenges to human health. Among the strategies for vector control, chemical insecticides have seen widespread use since their inception. Nevertheless, their effectiveness is continually undermined by the steady growth of insecticide resistance within these vector populations. As such, the demand for more robust, efficient, and cost-effective natural insecticides has become increasingly pressing. One promising avenue of research focuses on chitin, a crucial structural component of mosquitoes' exoskeletons and other insects. Chitin not only provides protection and rigidity but also lends flexibility to the insect body. It undergoes substantial transformations during insect molting, a process known as ecdysis. Crucially, the production of chitin is facilitated by an enzyme known as chitin synthase, making it an attractive target for potential novel insecticides. Our recent study delved into the impacts of curcumin, a natural derivative of turmeric, on chitin synthesis and larval development in Aedes aegypti, a mosquito species known to transmit dengue and yellow fever. Our findings demonstrate that even sub-lethal amounts of curcumin can significantly reduce overall chitin content and disrupt the cuticle development in the 4th instar larvae of Aedes aegypti. Further to this, we utilized computational analyses to investigate how curcumin interacts with chitin synthase. Techniques such as molecular docking, pharmacophore feature mapping, and molecular dynamics (MD) simulations helped to illustrate that curcumin binds to the same site as polyoxin D, a recognized inhibitor of chitin synthase. These findings point to curcumin's potential as a natural, bioactive larvicide that targets chitin synthase in mosquitoes and potentially other insects. [ABSTRACT FROM AUTHOR]

Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study's main objective is to assess the accuracy of the model's predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available. [ABSTRACT FROM AUTHOR]

IntroductionHomology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical for understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise and computational resources, presenting a barrier for many researchers.MethodsProstruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. Integrating Biopython for sequence alignment, BLAST for template identification, and ProMod3 for structure generation, Prostruc streamlines complex workflows into a user-friendly interface. The tool enables researchers to input protein sequences, identify homologous templates from databases such as the Protein Data Bank (PDB), and generate high-quality 3D structures with minimal computational expertise. Prostruc implements a two-stage vSquarealidation process: first, it uses TM-align for structural comparison, assessing Root Mean Deviations (RMSD) and TM scores against reference models. Second, it evaluates model quality via QMEANDisCo to ensure high accuracy.ResultsThe top five models are selected based on these metrics and provided to the user. Prostruc stands out by offering scalability, flexibility, and ease of use. It is accessible via a cloud-based web interface or as a Python package for local use, ensuring adaptability across research environments. Benchmarking against existing tools like SWISS-MODEL,I-TASSER and Phyre2 demonstrates Prostruc's competitive performance in terms of structural accuracy and job runtime, while its open-source nature encourages community-driven innovation.DiscussionProstruc is positioned as a significant advancement in homology modeling, making high-quality protein structure prediction more accessible to the scientific community.