Search

Background: Familial Meniere's disease (FMD) is a rare polygenic disorder of the inner ear. Mutations in the connexin gene family, which encodes gap junction proteins, can also cause hearing loss, but their role in FMD is largely unknown. Methods: We retrieved exome sequencing data from 94 individuals in 70 Meniere's disease (MD) families. Through gene burden analysis, we calculated the enrichment of rare variants (allele frequency < 0.05) in connexins genes in FMD individuals compared with the reference population. The connexin monomer and the homomeric connexon structural models were predicted using AlphaFold2 and HDOCK. RT-qPCR and immunofluorescence were done in mice cochleae to identify expression of the mouse ortholog candidate gene Gjd3. Results: We found an enrichment of rare missense variants in the GJD3 gene when comparing allelic frequencies in FMD (N = 94) with the Spanish reference population (OR = 3.9[1.92–7.91], FDR = 2.36E-03). In the GJD3 sequence, we identified a rare haplotype (TGAGT) composed of two missense, two synonymous, and one downstream variant. This haplotype was found in five individuals with FMD, segregating in three unrelated families with a total of ten individuals; and in another eight MD individuals. GJD3 encodes the gap junction protein delta 3, also known as human connexin 31.9 (Cx31.9). The protein model predicted that the NP_689343.3:p.(His175Tyr) missense variant could modify the interaction between connexins and the connexon assembly, affecting the homotypic GJD3 gap junction between cells. Our studies in mice revealed that Gjd3—encoding Gjd3 or mouse connexin 30.2 (Cx30.2)—was expressed in the organ of Corti and vestibular organs, particularly in the tectorial membrane, the base of inner and outer hair cells and the nerve fibers. Conclusions: The present results describe a novel association between GJD3 and FMD, providing evidence that FMD is related to changes in the inner ear channels, and supporting a new role of tectorial membrane proteins in MD. [ABSTRACT FROM AUTHOR]

Legionella pneumophila utilizes the Dot/Icm type IVB secretion system to deliver hundreds of effector proteins inside eukaryotic cells to ensure intracellular replication. Our understanding of the molecular functions of the largest pathogenic arsenal known to the bacterial world remains incomplete. By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database (https://pathogens3d.org/legionella-pneumophila). Our analysis identified 157 types of diverse functional domains in 287 effectors, including 159 effectors with no prior functional annotations. Furthermore, we identified 35 cryptic domains in 30 effector models that have no similarity with experimentally structurally characterized proteins, thus, hinting at novel functionalities. Using this analysis, we demonstrate the activity of thirteen functional domains, including three cryptic domains, predicted in L. pneumophila effectors to cause growth defects in the Saccharomyces cerevisiae model system. This illustrates an emerging strategy of exploring synergies between predictions and targeted experimental approaches in elucidating novel effector activities involved in infection. Synopsis: Legionella pneumophila encodes the largest arsenal of eukaryotic host-manipulating proteins, called effectors. Leveraging high-throughput structural prediction tools, coupled with in cellulo assays, revealed previously unrecognized functional and cryptic domains in L. pneumophila effectors. Analysis revealed over 150 structurally diverse domains in 287 effectors, reflective of the diverse mechanisms required to modulate L. pneumophila's eukaryotic hosts ranging from protozoa to human alveolar macrophages. Over half of the predicted functional domains are present in only one effector, while cysteine proteases and kinases are the most prominent domain categories. An even larger presence of so-called tandem repeat structural motifs in effector proteins is suggested to facilitate protein-protein interactions. Over 30 identified domains, some of which are present in other pathogens, showed no structural similarity with characterized proteins, suggestive of novel biochemical activities. Legionella pneumophila encodes the largest arsenal of eukaryotic host-manipulating proteins, called effectors. Leveraging high-throughput structural prediction tools, coupled with in cellulo assays, revealed previously unrecognized functional and cryptic domains in L. pneumophila effectors. [ABSTRACT FROM AUTHOR]

The adenomas in Cushing's disease frequently exhibit mutations in exon 14, within a binding motif for the regulatory protein 14-3-3 located between the catalytic domain (DUB), responsible for ubiquitin hydrolysis, and the WW-like domain that mediates autoinhibition, resulting in constantly active USP8. The exact molecular mechanism of deubiquitinase activity disruption in Cushing's disease remains unclear. To address this, Sanger sequencing of USP8 was performed to identify mutations in corticotropinomas. These mutations were subjected to computational screening, followed by molecular dynamics simulations to assess the structural alterations that might change the biological activity of USP8. Eight different variants of the USP8 gene were identified both within and outside the "hotspot" region. Six of these had previously been reported in Cushing's disease, while two were detected for the first time in our patients with CD. One of the two new variants, initially classified as benign during screening, was found in the neighboring SH3 binding motif at a distance of 20 amino acids. This variant demonstrated pathogenicity patterns similar to those of known pathogenic variants. All USP8 variants identified in our patients caused conformational changes in the USP8 protein in a similar manner. The identified mutations, despite differences in annotation results—including evolutionary conservation assessments, automated predictor data, and variations in localization within exon 14—exhibit similar patterns of protein conformational change. This suggests a pathogenic effect that contributes to the development of CD. [ABSTRACT FROM AUTHOR]

Background: Autosomal recessive primary microcephaly (MCPH) is a rare neurodevelopmental and genetically heterogeneous disorder, characterized by small cranium size (> − 3 SD below mean) and often results in varying degree of intellectual disability. Thirty genes have been identified for the etiology of this disorder due to its clinical and genetic heterogeneity. Methods and Results: Here, we report two consanguineous Pakistani families affected with MCPH exhibiting mutation in WDR62 gene. The investigation approach involved Next Generation Sequencing (NGS) gene panel sequencing coupled with linkage analysis followed by validation of identified variants through automated Sanger sequencing and Barcode-Tagged (BT) sequencing. The molecular genetic analysis revealed one novel splice site variant (NM_001083961.2(WDR62):c.1372-1del) in Family A and one known exonic variant NM_001083961.2(WDR62):c.3936dup (p.Val1313Argfs*18) in Family B. Magnetic Resonance Imaging (MRI) scans were also employed to gain insights into the structural architecture of affected individuals. Neurological assessments showed the reduced gyral and sulcal patterns along with normal corpus callosum in affected individuals harboring novel variant. In silico assessments of the identified variants were conducted using different tools to confirm the pathogenicity of these variants. Through In silico analyses, both variants were identified as disease causing and protein modeling of exonic variant indicates subtle conformational alterations in prophesied protein structure. Conclusion: This study identifies a novel variant (c.1372-1del) and a recurrent pathogenic variant c.3936dup (p.Val1313Argfs*18) in the WDR62 gene among the Pakistani population, expanding the mutation spectrum for MCPH. These findings emphasize the importance of genetic counseling and awareness to reduce consanguinity and address the burden of this disorder. [ABSTRACT FROM AUTHOR]

Abstract Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

Flavonoids are secondary metabolites associated with plant seed coat and flower color. These compounds provide health benefits to humans as anti-inflammatory and antioxidant compounds. The expression of the late biosynthetic genes in the flavonoid pathway is controlled by a ternary MBW protein complex consisting of interfacing M YB, b eta-helix–loop–helix (bHLH), and WD 40 R epeat (WDR) proteins. P , the master regulator gene of the flavonoid expression in common bean (Phaseolus vulgaris L.), was recently determined to encode a bHLH protein. The T and Z genes control the distribution of color in bean seeds and flowers and have historically been considered regulators of the flavonoid gene expression. T and Z candidates were identified using reverse genetics based on genetic mapping, phylogenetic analysis, and mutant analysis. Domain and AlphaFold2 structure analyses determined that T encodes a seven-bladed β-propeller WDR protein, while Z encodes a R2R3 MYB protein. Deletions and SNPs in T and Z mutants, respectively, altered the 3D structure of these proteins. Modeling of the Z MYB/P bHLH/T WDR MBW complex identified interfacing sequence domains and motifs in all three genes that are conserved in dicots. One Z MYB motif is a possible beta-molecular recognition feature (β-MoRF) that only appears in a structured state when Z MYB is modeled as a component of a MBW complex. Complexes containing mutant T and Z proteins changed the interaction of members of the complex in ways that would alter their role in regulating the expression of genes in the flavonoid pathway. [ABSTRACT FROM AUTHOR]

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high‐confidence and low‐energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein–protein interactions, and protein‐ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low‐energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state‐of‐the‐art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ‐angle distribution predictions and geometry‐aware invariant point message passing (IPMP). On a test set of ∼1400 high‐quality protein chains, PIPPack is highly competitive with other state‐of‐the‐art PSCP methods in rotamer recovery and per‐residue RMSD but is significantly faster. [ABSTRACT FROM AUTHOR]

Protein structure prediction (PSP) is the prediction of the three‐dimensional (3D) folding of a protein (its tertiary structure) starting from its amino acid sequence (its primary structure). The state of the art in PSP is achieved by deep learning pipelines that require several input features extracted from amino acid sequences. It has been demonstrated that features that grasp the relative orientation of amino acids positively impact the prediction accuracy of the 3D coordinates of atoms in the protein backbone. In this paper, we demonstrate the relevance of geometric algebra (GA) in instantiating orientational features for PSP problems. We do so by proposing two novel GA‐based metrics which contain information on relative orientations of amino acid residues. We then employ these metrics as additional input features to a graph transformer (GT) architecture to aid the prediction of the 3D coordinates of a protein, and compare them to classical angle‐based metrics. We show how our GA features yield comparable results to angle maps in terms of accuracy of the predicted coordinates. This is despite being constructed from less initial information about the protein backbone. The features are also fewer and more informative and can be (i) closely associated to protein secondary structures and (ii) more easily predicted compared to angle maps. We hence deduce that GA can be employed as a tool to simplify the modeling of protein structures and pack orientational information in a more natural and meaningful way. [ABSTRACT FROM AUTHOR]

Chicken Parvovirus (ChPV) belongs to the genus Aveparvovirus and is implicated in enteric diseases like runting–stunting syndrome (RSS) in poultry. In RSS, chicken health is affected by diarrhea, depression, and increased mortality, causing significant economic losses in the poultry industry. This study aimed to characterize the ChPV genomes detected in chickens with RSS through a metagenomic approach and compare the molecular and evolutionary characteristics within the Aveparvovirus galliform1 species. The intestinal content of broiler flocks affected with RSS was submitted to viral metagenomics. The assembled prevalent genomes were identified as ChPV after sequence and phylogenetic analysis, which consistently clustered separately from Turkey Parvovirus (TuPV). The strain USP-574-A presented signs of genomic recombination. The selective pressure analysis indicated that most of the coding genes in A. galliform1 are evolving under diversifying (negative) selection. Protein modeling of ChPV and TuPV viral capsids identified high conservancy over the VP2 region. The prediction of epitopes identified several co-localized antigenic peptides from ChPV and TuPV, especially for T-cell epitopes, highlighting the immunological significance of these sites. However, most of these peptides presented host-specific variability, obeying an adaptive scenario. The results of this study show the evolutionary path of ChPV and TuPV, which are influenced by diversifying events such as genomic recombination and selective pressure, as well as by adaptation processes, and their subsequent immunological impact. [ABSTRACT FROM AUTHOR]

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy. [ABSTRACT FROM AUTHOR]

SUMMARY: Previously, expression of the Arabidopsis thaliana GLABRA3 (GL3) induced trichome formation in Brassica napus. GL3 orthologues were examined from glabrous (B. oleracea), semi‐glabrous (B. napus), moderately hirsute (B. rapa), and very hirsute (B. villosa) Brassica species. Ectopic expression of BnGL3, BrGL3 alleles, or BvGL3 induced trichome formation in glabrous B. napus with the effect on trichome number commensurate with density in the original accessions. Chimeric GL3 proteins in which the B. napus amino terminal region, which interacts with MYB proteins, or the middle region, which interacts with the WD40 protein TTG1, was exchanged with corresponding regions from A. thaliana were as stimulatory to trichome production as AtGL3. Exchange of the carboxy‐terminal region containing a bHLH domain and an ACT domain did not alter the trichome stimulatory activity, although modeling of the ACT domain identified differences that could affect GL3 dimerization. B. napus A‐ and C‐genomes orthologues differed in their abilities to form homo‐ and heterodimers. Modeling of the amino‐terminal region revealed a conserved domain that may represent the MYB factor binding pocket. This region interacted with the MYB factors GL1, CPC, and TRY, as well as with JAZ8, which is involved in jasmonic acid‐mediated regulation of MYC‐like transcription factors. Protein interaction studies indicated that GL1 interaction with GL3 from B. napus and A. thaliana may underlie the difference in their respective abilities to induce trichome formation. Significance Statement: The work compares the ability of GL3 orthologues from glabrous and hirsute Brassica species to initiate trichome formation in Brassica napus. Chimaric GL3 proteins, protein modeling, and protein–protein interaction studies are then used to define the reasons underlying the lack of trichomes in B. napus and to provide new information regarding the function of GL3 subdomains. [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]

IntroductionToll-like receptors 4 (TLR4) recognize lipopolysaccharides (LPS) from bacteria as their conventional ligands and undergo downstream signaling to produce cytokines. They mediate the signaling either by the TIRAP-MyD88 complex or by the TRAM-TRIF complex. The MyD88 pathway is common to all other TLRs, whereas the TRAM-TRIF complex is largely exclusive to TLR4. Here we study the TIR domain of TRAM and TRIF ortholog proteins that are crucial for downstream signaling. Our previous work on pan-genome-wide survey, indicates Callorhincus milli to be the ancestral organism with both TRAM and TRIF proteins.MethodsTo gain a deeper insight into the protein function and to compare them with Homo sapiens adaptor proteins, we modeled the docking of the TRAM–TRIF complex of representative organisms across various taxa. These modeling experiments provide insights to ascertain a possible interaction surface and calculate the energetics and electrostatic potential of the complex. Furthermore, this enables us to employ normal mode analysis (NMA) to examine fluctuating, interacting, and other specific residue clusters that could have a role in protein functioning in both C. milli and H. sapiens. We also performed molecular dynamics simulations of these complexes and cross-validated the functionally important residues using network parameters.ResultsWe compared the stoichiometry of TRAM–TRIF complexes and found that the tetrameric models (TRAM and TRIF dimer) were more stable than the trimeric model (TRAM dimer and TRIF monomer). While the critical residues of TIRAP, TRIF, and MyD88 were preserved, we also found that the important residues of TRAM signaling were not conserved in C. milli.DiscussionThis suggests the presence of functional TIRAP–MyD88-mediated TLR4 signaling and TRIF-mediated TLR3 signaling in the ancestral species. The overall biological function of this signaling domain appears to be gradually acquired through the orchestration of several motifs through an evolutionary scale.

Abstract Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 – CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44–63, 85–93, 126–137, 193–205, and 208–237), (26–46, 52–71, 98–114, 142–155, and 183–200), and (29–45, 58–82, 119–142, 177–198, and 222–251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173–181, 189–197, and 202–210), (61–69, 91–99, 159–167, and 181–189), and (3–11, 24–32, 167–175, and 216–224), while T helper lymphocyte epitopes were displayed at (39–53, 57–65, 150–158, 163–171), (79–87, 95–108, 115–123, 128–142, and 189–197), and (39–47, 109–123, 216–224, and 245–253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (− 16.8744 to − 15.1922), (− 16.0424 to − 14.1645), and (− 14.7566 to − 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella’s proteins.

Bacteriophage endolysins are potential alternatives to conventional antibiotics for treating multidrug-resistant gram-negative bacterial infections. However, their structure–function relationships are poorly understood, hindering their optimization and application. In this study, we focused on the individual functionality of the C-terminal muramidase domain of Gp127, a modular endolysin from E. coli O157:H7 bacteriophage PhaxI. This domain is responsible for the enzymatic activity, whereas the N-terminal domain binds to the bacterial cell wall. Through protein modeling, docking experiments, and molecular dynamics simulations, we investigated the activity, stability, and interactions of the isolated C-terminal domain with its ligand. We also assessed its expression, solubility, toxicity, and lytic activity using the experimental data. Our results revealed that the C-terminal domain exhibits high activity and toxicity when tested individually, and its expression is regulated in different hosts to prevent self-destruction. Furthermore, we validated the muralytic activity of the purified refolded protein by zymography and standardized assays. These findings challenge the need for the N-terminal binding domain to arrange the active site and adjust the gap between crucial residues for peptidoglycan cleavage. Our study shed light on the three-dimensional structure and functionality of muramidase endolysins, thereby enriching the existing knowledge pool and laying a foundation for accurate in silico modeling and the informed design of next-generation enzybiotic treatments. [ABSTRACT FROM AUTHOR]

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 – CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44–63, 85–93, 126–137, 193–205, and 208–237), (26–46, 52–71, 98–114, 142–155, and 183–200), and (29–45, 58–82, 119–142, 177–198, and 222–251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173–181, 189–197, and 202–210), (61–69, 91–99, 159–167, and 181–189), and (3–11, 24–32, 167–175, and 216–224), while T helper lymphocyte epitopes were displayed at (39–53, 57–65, 150–158, 163–171), (79–87, 95–108, 115–123, 128–142, and 189–197), and (39–47, 109–123, 216–224, and 245–253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (− 16.8744 to − 15.1922), (− 16.0424 to − 14.1645), and (− 14.7566 to − 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins. [ABSTRACT FROM AUTHOR]

SUMMARY: Restoring cytonuclear stoichiometry is necessary after whole‐genome duplication (WGD) and interspecific/intergeneric hybridization in plants. We investigated this phenomenon in auto‐ and allopolyploids of the Festuca‐Lolium complex providing insights into the mechanisms governing cytonuclear interactions in early polyploid and hybrid generations. Our study examined the main processes potentially involved in restoring the cytonuclear balance after WGD comparing diploids and new and well‐established autopolyploids. We uncovered that both the number of chloroplasts and the number of chloroplast genome copies were significantly higher in the newly established autopolyploids and grew further in more established autopolyploids. The increase in the copy number of the chloroplast genome exceeded the rise in the number of chloroplasts and fully compensated for the doubling of the nuclear genome. In addition, changes in nuclear and organelle gene expression were insignificant. Allopolyploid Festuca × Lolium hybrids displayed potential structural conflicts in parental protein variants within the cytonuclear complexes. While biased maternal allele expression has been observed in numerous hybrids, our results suggest that its role in cytonuclear stabilization in the Festuca × Lolium hybrids is limited. This study provides insights into the restoration of the cytonuclear stoichiometry, yet it emphasizes the need for future research to explore post‐transcriptional regulation and its impact on cytonuclear gene expression stoichiometry. Our findings may enhance the understanding of polyploid plant evolution, with broader implications for the study of cytonuclear interactions in diverse biological contexts. [ABSTRACT FROM AUTHOR]

The profound impacts of global changes on biodiversity necessitate a more comprehensive documentation, particularly at the microscale level. To achieve precise and rapid insights into this unique diversity, the choice of an ideal species candidate is crucial. Neurospora crassa, a well-established organism in the field of biology, emerges as a promising candidate for this purpose. In our study, we explore the potential of the Carboxypeptidase A1 (CPA1) enzyme as a valuable tool for profiling global diversity. Our investigation has revealed that CPA1 possesses distinctive characteristics, notably its conserved solvent accessibility. This unique feature makes CPA1 an invaluable asset for microscale studies of global changes. The insights presented in our study serve as a practical blueprint, showcasing the application of structural biology in understanding diversity and global changes within microscale environments.

Quinine, a bitter compound, can act as an agonist to activate the family of bitter taste G protein-coupled receptor family of proteins. Previous work from our laboratory has demonstrated that quinine causes activation of RalA, a Ras p21-related small G protein. Ral proteins can be activated directly or indirectly through an alternative pathway that requires Ras p21 activation resulting in the recruitment of RalGDS, a guanine nucleotide exchange factor for Ral. Using normal mammary epithelial (MCF-10A) and non-invasive mammary epithelial (MCF-7) cell lines, we investigated the effect of quinine in regulating Ras p21 and RalA activity. Results showed that in the presence of quinine, Ras p21 is activated in both MCF-10A and MCF-7 cells; however, RalA was inhibited in MCF-10A cells, and no effect was observed in the case of MCF-7 cells. MAP kinase, a downstream effector for Ras p21, was activated in both MCF-10A and MCF-7 cells. Western blot analysis confirmed the expression of RalGDS in MCF-10A cells and MCF-7 cells. The expression of RalGDS was higher in MCF-10A cells in comparison to the MCF-7 cells. Although RalGDS was detected in MCF-10A and MCF-7 cells, it did not result in RalA activation upon Ras p21 activation with quinine suggesting that the Ras p21-RalGDS-RalA pathway is not active in the MCF-10A cells. The inhibition of RalA activity in MCF-10A cells due to quinine could be as a result of a direct effect of this bitter compound on RalA. Protein modeling and ligand docking analysis demonstrated that quinine can interact with RalA through the R79 amino acid, which is located in the switch II region loop of the RalA protein. It is possible that quinine causes a conformational change that results in the inhibition of RalA activation even though RalGDS is present in the cell. More studies are needed to elucidate the mechanism(s) that regulate Ral activity in mammary epithelial cells. [ABSTRACT FROM AUTHOR]

In the present study, we used a bubble column photobioreactor to test carbon dioxide using Chlorella vulgaris RDS03 under laboratory conditions. The nutrient concentration of medium was optimized by Box-Behnken design through response surface methodology (RSM), and regression coefficient (R2) value of 0.999 was analyzed by analysis of variance (ANOVA). The microalga Chlorella vulgaris RDS03 was captured—98.86% of CO2 analyzed by CO2 utilization and biofixation kinetics, 310U mL−1 of RuBisCO enzyme, 5.32 mg mL−1 of biomass, 124 mg g−1 of carbohydrate, 247.15 mg g−1 of lipid, 4.1 mL g−1 of bioethanol, and 4.9 mL g−1 of biodiesel produced. The molecular weight of purified RuBisCO enzyme was analyzed as 54 kDa by 15% of SDS PAGE. The 3D homology structure of N-terminal amino acids sequence of RuBisCO was predicted with 415 amino acid residues. The biodiesel was subjected to functional group analysis using Fourier transform infrared spectroscopy (FTIR). The fifty biodiesel (FAME) compounds were identified by gas chromatograph-mass spectroscopy (GC–MS) analysis and major compounds viz., linolenic acid (C18:2), oleic acid (C18:2), stearic acid (C18:0), palmitic acids (C16:1), and myristic acid (C14:0). The produced bioethanol was confirmed using high-performance liquid chromatography (HPLC). [ABSTRACT FROM AUTHOR]

The bacterial cell communicates from one cell to another by binding Auto-Inducers to specific receptors and their virulence factors, which are all products of their expression system. Therefore, this pathogenesis is controlled by disrupting the signal-response system. The current study assesses three maritime red seaweeds, including Portieria hornemannii and Halymenia dilatata, for their anti-Quorum Sensing (QS) activity against four bacteria. Those opportunistic pathogens cause severe QS-dependent biofilm formation and other virulences. In vitro, the study showed that biofilm formation in S. aureus was inhibited with 43.3%, 55% in Acinetobacter sp, 48% in E. coli, and 39. 2 % in K. pneumoniae by red seaweed extracts. The EPS production was also highly inhibited in Acinetobacter sp. with 41 % more than other bacteria. The efflux pump expressions and QS-dependent swimming motility were also effectively reduced. The present study targets the receptor proteins to prevent from binding of QS signals. Correspondingly, the in silico research predicts the binding affinity of bioactive compounds of seaweed extracts to the QS receptor proteins. The Hexamethyl Cyclotrisiloxane, Benzo[h]quinoline, 2,4-dimethyl, and 5-Methyl-2-phenylindolizine compounds from H. dilatata, P. hornemannii, respectively, showed a higher binding affinity with receptor proteins such as AgrC (PDB ID: 4BXI) of S. aureus, SdiA (PDB ID:4LFU) of E. coli, Modelled SdiA protein of K. pneumoniae and Modelled AbaR protein of Acinetobacter sp. This study demonstrates the potential of seaweed against virulence and antibiotic resistance of pathogenic bacteria.

Reproductive processes in fishes are regulated by the hypothalamic-pituitary-gonadal (HPG) axis, much like in tetrapods. Within this system, Gonadotropin-Releasing Hormone (GnRH) is released by the hypothalamus, binding to GnRH receptors in the pituitary gland and stimulating the secretion of gonadotropin hormones. The current study aimed to analyze the GnRH receptor in Oncorhynchus mykiss (rainbow trout) using a computational and structural biology approach. The GnRH receptor gene of O. mykiss comprises a nucleotide sequence of 1707 base pairs with an open reading frame of 1251 base pairs, which is responsible for encoding 416 amino acids. It was found that the GnRH receptor contains leucine (L) as the most abundant amino acid. The secondary structure revealed that alpha helices constitute the largest percentage (36 %) with 153 residues, followed by extended strands with 77 residues (17.51 %). The GnRH receptor contains 26 negatively charged and 37 positively charged amino acid residues. The highest hydrophilicity was observed for lysine (K) at position 310, with a value of −3.900, while the highest hydrophobicity was found for leucine (L) at position 290, with a value of 3.80. Molecular docking analysis showed that the most favorable binding energy was observed for Gestrinone (−7.8 kcal/mol). Gestrinone was found to form hydrogen bonds with MET160, LUE245, LUE62, TYR216, and GLN209 residues of GnRH. Moreover, molecular dynamics revealed that the complexes form robust and enduring connections, indicating their structural integrity throughout the simulation. The results of this study provide insights into the protein modeling, molecular docking, and virtual screening of antagonist ligands against the GnRH receptor. Additionally, they may significantly aid in the advancement and improvement of therapeutic strategies targeted at treating various fish reproductive dysfunctions.

This study describes the cloning, expression and functional characterization of α-humulene synthase, responsible for the formation of the key aromatic compound α-humulene in agarwood originating from Aquilaria malaccensis. The partial sesquiterpene synthase gene from the transcriptome data of A. malaccensis was utilized for full-length gene isolation via a 3′ RACE PCR. The complete gene, denoted as AmDG2, has an open reading frame (ORF) of 1671 bp and encodes for a polypeptide of 556 amino acids. In silico analysis of the protein highlighted several conserved motifs typically found in terpene synthases such as Asp-rich substrate binding (DDxxD), metal-binding residues (NSE/DTE), and cytoplasmic ER retention (RxR) motifs at their respective sites. The AmDG2 was successfully expressed in the E. coli:pET-28a(+) expression vector whereby an expected band of about 64 kDa in size was detected in the SDS-PAGE gel. In vitro enzyme assay using substrate farnesyl pyrophosphate (FPP) revealed that AmDG2 gave rise to two sesquiterpenes: α-humulene (major) and β-caryophyllene (minor), affirming its identity as α-humulene synthase. On the other hand, protein modeling performed using AlphaFold2 suggested that AmDG2 consists entirely of α-helices with short connecting loops and turns. Meanwhile, molecular docking via AutoDock Vina (Version 1.5.7) predicted that Asp307 and Asp311 act as catalytic residues in the α-humulene synthase. To our knowledge, this is the first comprehensive report on the cloning, expression and functional characterization of α-humulene synthase from agarwood originating from A. malaccensis species. These findings reveal a deeper understanding of the structure and functional properties of the α-humulene synthase and could be utilized for metabolic engineering work in the future.

ABSTRACTThe incidence of isoniazid (INH) resistant Mycobacterium tuberculosis is increasing globally. This study aimed to identify the molecular mechanisms behind the development of INH resistance in M. tuberculosis strains collected from the same patients during the standard course of treatment. Three M. tuberculosis strains were collected from a patient before and during antituberculosis (anti-TB) therapy. The strains were characterized using phenotypic drug susceptibility tests, Mycobacterial Interspersed Repeated Unit-Variable-Number Tandem Repeats (MIRU-VNTR), and whole-genome sequencing (WGS) to identify mutations associated with INH resistance. To validate the role of the novel mutations in INH resistance, the mutated katG genes were electroporated into a KatG-deleted M. tuberculosis strain (GA03). Three-dimensional structures of mutated KatG were modeled to predict their impact on INH binding. The pre-treatment strain was susceptible to INH. However, two INH-resistant strains were isolated from the patient after anti-TB therapy. MIRU-VNTR and WGS revealed that the three strains were clonally identical. A missense mutation (P232L) and a nonsense mutation (Q461Stop) were identified in the katG of the two post-treatment strains, respectively. Transformation experiments showed that katG of the pre-treatment strain restored INH susceptibility in GA03, whereas the mutated katG genes from the post-treatment strains rendered negative catalase activity and INH resistance. The protein model indicated that P232L reduced INH-KatG binding affinity while Q461Stop truncated gene transcription. Our results showed that the two katG mutations, P232L and Q461Stop, accounted for the co-emergence of INH-resistant clones during anti-TB therapy. The inclusion of these mutations in the design of molecular assays could increase the diagnostic performance.IMPORTANCEThe evolution of drug-resistant strains of Mycobacterium tuberculosis within the lung lesions of a patient has a detrimental impact on treatment outcomes. This is particularly concerning for isoniazid (INH), which is the most potent first-line antimycobacterial drug. However, the precise genetic factors responsible for drug resistance in patients have not been fully elucidated, with approximately 15% of INH-resistant strains harboring unknown genetic factors. This raises concerns about the emergence of drug-resistant clones within patients, further contributing to the global epidemic of resistance. In this study, we revealed the presence of two novel katG mutations, which emerged independently due to the stress exerted by antituberculosis (anti-TB) treatment on a parental strain. Importantly, we experimentally demonstrated the functional significance of both mutations in conferring resistance to INH. Overall, this research sheds light on the genetic mechanisms underlying the evolution of INH resistance within patients and provides valuable insights for improving diagnostic performance by targeting specific mutations.

Background: H/ACA small nucleolar ribonucleoproteins (snoRNP) form a complex with multiple proteins to accomplish the pseudouridylation of rRNA. The assembly of H/ACA small nucleolar ribonucleoproteins (snoRNP) is initiated by H/ACA ribonucleoprotein Assembly factor, that is, SHQ1. Mutations in SHQ1 have been reported to cause two disorders namely, dystonia-35 childhood onset (OMIM*619921) and neurodevelopmental disorder with seizures and dystonia (OMIM*619922), both of which are inherited in an autosomal recessive manner. Considering the high genetic and clinical diversity of SHQ1-related clinical features and the importance of SHQ1 in the assembly of the H/ACA snoRNP complex, it is important to take a systematic approach to delineate the genetic diagnosis and impact of mutations on protein structure and stability. Methods: Whole exome sequencing followed by Sanger validation was performed in an individual with the clinical features of neurodevelopmental disorder with seizures and dystonia (OMIM*619922). Protein modeling studies of all the reported SHQ1 variants to date were performed using freely available web servers Interactive Tree of Life, String, BioGrid, ShinyGO, DAVID, and Pathvix. Protein structures were visualized using Pymol. Results and Discussion: We identified compound heterozygous variants, one known frameshift deletion c. 828_831del, p.(Asp277Serfs*27) and the other novel missense variant c. 1157A>C, p.(Tyr386Ser) found in an individual with neurodevelopmental disorder, seizures, movement disorder, and hypomyelination leukodystrophy on neuroimaging. Protein-interactome studies identified potential genetic interactors that include GAR1, NAF1, TRUB1, UTP15, DKC1, NOP10, NPHOSPH 10, KRR1, NOP58, NOP56, FBL, RRP9, NHP2, RUVBL1, and RUVBL2. Ribosome biogenesis in eukaryotes, RNA polymerase, RNA transport, spliceosome, ribosome, cytosolic DNA-sensing pathway, DNA replication, mismatch repair, base excision repair, nucleotide excision repair, and basal transcription factors process were identified as the linked pathways with the prioritized genes. Conclusion: In conclusion, a sophisticated genotype and phenotype correlation followed by linking the genes to the key biological pathways opens new avenues to understand disease pathology and plan for therapeutic interventions. [ABSTRACT FROM AUTHOR]

In bacteria and primitive eukaryotes, sulfonamide antibiotics block the folate pathway by inhibiting dihydropteroate synthase (FolP) that combines para-aminobenzoic acid (pABA) and dihydropterin pyrophosphate (DHPP) to form dihydropteroic acid (DHP), a precursor for tetrahydrofolate synthesis. However, the emergence of resistant strains has severely compromised the use of pABA mimetics as sulfonamide drugs. Salmonella enterica serovar Gallinarum (S. Gallinarum) is a significant source of antibiotic-resistant infections in poultry. Here, a sulfonamide-resistant FolP mutant library of S. Gallinarum was generated through random mutagenesis. Among resistant strains, substitution of amino acid Arginine 171 with Proline (R171P) in the FolP protein conferred the highest resistance against sulfonamide. Substitution of Phe28 with Leu or Ile (F28L/I) led to modest sulfonamide resistance. Structural modeling indicates that R171P and Phenylalanine 28 with leucine or isoleucine (F28L/I) substitution mutations are located far from the substrate-binding site and cause insignificant conformational changes in the FolP protein. Rather, in silico studies suggest that the mutations altered the stability of the protein, potentially resulting in sulfonamide resistance. Identification of specific mutations in FolP that confer resistance to sulfonamide would contribute to our understanding of the molecular mechanisms of antibiotic resistance. [ABSTRACT FROM AUTHOR]

A family of Pakistani origin, segregating polydactyly, and phalangeal synostosis in an autosomal dominant manner, has been investigated and presented in the present report. Whole‐exome sequencing (WES), followed by segregation analysis using Sanger sequencing, revealed a heterozygous missense variant [c.G1696A, p.(Gly566Ser)] in the LRP4 gene located on human chromosome 11p11.2. Homology protein modeling revealed the mutant Ser566 generated new interactions with at least four other amino acids and disrupted protein folding and function. Our findings demonstrated the first direct evidence of involvement of LRP4 in causing polydactyly and phalangeal synostosis in the same family. This study highlighted the importance of inclusion of LRP4 gene in screening individuals presenting polydactyly in hands and feet, and phalangeal synostosis in the same family. [ABSTRACT FROM AUTHOR]

Introduction: Helicobacter pylori (HP) has several important virulence factors including Cytotoxin-Associated Gene A (CagA). Studying the protein structure and physicochemical characteristics of CagA from the sequence data are essential to understand their virulence ability. This study compared the protein structure and physicochemical characteristics of CagA western-type HP29965 and J99. Methods: We retrieved the DNA sequences from GenBank, NCBI (HP26695: NC_000915.1; J99: NC_000921.1. Protein modelling from protein sequences using Swiss-Model was visualized using PyMOL. Physicochemical characteristics were obtained using ExPasy ProtParam. Results: CagA HP26695 has Glu-Pro-Ile-Tyr-Ala (EPIYA)-A, EPIYA-B, and EPIYA-C, while CagA J99 lacks EPIYA-A. Both strains have relatively similar tertiary protein structures. CagA J99 has fewer amino acids and molecular weight than CagA 26695. Both strains have negative GRAVY values, a high aliphatic index (>70), and the same estimated half-life. Conclusion: HP J99 only has EPIYA-B and EPIYA-C. Both strains are thermostable and stable. However, J99 is more thermostable and stable than HP26695.

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins” invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.

The complex metabolism of Escherichia coli has been extensively studied, including its response to oxygen availability. The ArcA/B two-component system (TCS) is the key regulator for the transition between these two environmental conditions and has been thoroughly characterized using genetic and biochemical approaches. Still, to date, limited structural data is available. The breakthrough provided by AlphaFold2 in 2021 has brought a reliable tool to the scientific community for assessing the structural features of complex proteins. In this report, we analyzed the structural aspects of the ArcA/B TCS using AlphaFold2 models. The models are consistent with the experimentally determined structures of ArcB kinase. The predicted structure of the dimeric form of ArcB is consistent with the extensive genetic and biochemical data available regarding mechanistic signal perception and regulation. The predicted interaction of the dimeric form of ArcB with its cognate response regulator (ArcA) is also consistent with both the forward and reverse phosphotransfer mechanisms. The ArcB model was used to detect putative binding cavities to anaerobic metabolites, encouraging testing of these predictions experimentally. Finally, the highly accurate models of other ArcB homologs suggest that different experimental approaches are needed to determine signal perception in kinases lacking the PAS domain. Overall, ArcB is a kinase with features that need further testing, especially in determining its crystal structure under different conditions. [ABSTRACT FROM AUTHOR]

Mastoparan B (MP‐B) is an amphiphilic peptide with a potent antimicrobial activity against most Gram‐negative bacteria. However, there is little information available on the inhibition of the Acinetobacter baumannii resistance‐nodulation‐cell‐division (RND) efflux pump using this antimicrobial peptide. Here, we carried out a series of in‐silico experiments to find the mechanisms underlying the anti‐efflux activity of MP‐B using a multi‐drug resistant (MDR) strain of A. baumannii (AB). According to our findings, MP‐B demonstrated a potent antibacterial activity against an MDR‐AB (minimum inhibitory concentration [MIC] = 1 μg/mL) followed by a 20‐fold reduction in the adeB gene expression in the presence of sub‐MIC of this peptide. Using Groningen Machine for Chemicals Simulation (GROMACS) via PyMOL Graphical User Interface (GUI), (we observed that, the AdeB transporter had conserved helix‐turn‐helix regions and a tight pore rich in Phe and Ala residues. To understand how inhibition of the AdeB is achieved, we generated 20 apo‐MP‐B poses using the InterPep and SiteMap tools. The high‐quality model was created by homology modeling and used for docking via AutoDock/Vina to identify the MP‐B binding sites. We established that the most apo‐MP‐B formed H‐bonds to the backbone of five amino acids in the Helix‐5. As a result, the dihedral angles of the involved amino acids shift by 9.0–9.6 Ǻ, causing a change in the conformation of the AdeB protein. This led to helix conformation stereoisomerization and block the AdeB activity. MP‐B presumably has dual mechanisms. (1) It blocks the AdeB transporter by changing its conformation. (2) MP‐B influences the adeB gene expression by binding to G‐protein which laterally controls efflux regulators like MarA, RamA, SoxS, and Rob proteins. [ABSTRACT FROM AUTHOR]

Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of porA in Campylobacter jejuni using spatiotemporal analysis over 30 years. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This framework, termed µPathML, defined intestinal and extraintestinal groups that have differential allelic porA variants that cause abortion. Divergent variants containing indels that defeated automated annotation were rescued using biological context and knowledge that resulted in defining rare, divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms.

The complex metabolism of Escherichia coli has been extensively studied, including its response to oxygen availability. The ArcA/B two-component system (TCS) is the key regulator for the transition between these two environmental conditions and has been thoroughly characterized using genetic and biochemical approaches. Still, to date, limited structural data is available. The breakthrough provided by AlphaFold2 in 2021 has brought a reliable tool to the scientific community for assessing the structural features of complex proteins. In this report, we analyzed the structural aspects of the ArcA/B TCS using AlphaFold2 models. The models are consistent with the experimentally determined structures of ArcB kinase. The predicted structure of the dimeric form of ArcB is consistent with the extensive genetic and biochemical data available regarding mechanistic signal perception and regulation. The predicted interaction of the dimeric form of ArcB with its cognate response regulator (ArcA) is also consistent with both the forward and reverse phosphotransfer mechanisms. The ArcB model was used to detect putative binding cavities to anaerobic metabolites, encouraging testing of these predictions experimentally. Finally, the highly accurate models of other ArcB homologs suggest that different experimental approaches are needed to determine signal perception in kinases lacking the PAS domain. Overall, ArcB is a kinase with features that need further testing, especially in determining its crystal structure under different conditions.

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14 and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D and DeepAccNet, etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2 and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation. [ABSTRACT FROM AUTHOR]

The 6.12 kDa human epidermal growth factor (hEGF) protein is expressed in the body and plays a role in cell proliferation and differentiation. This research aims to isolate the hEGF genes, determine the phylogenetic relationship between organisms based on the hEGF gene, construct the 3D structure of the hEGF protein, and identify the precise binding position between the hEGF protein and the tested Myosin9. DNA was isolated from blood of healthy individuals followed by amplification using specified primers. DNA sequencing was utilized to identify the amplification results before generating the phylogenetic trees. The retrieved sequences were then modeled using the Swiss model and docking proteins in silico. In three samples, DNA was successfully amplified to 700 bp and the phylogenetic analysis revealed that the three hEGF gene samples belonged to the same clades. With a 30 % identity seq, aligned result sequences modeled using the Swiss model created proteins which were not homologous. The outcomes of this modeling were compared to the hEGF protein from the database, which had a sequence identity of greater than 90 %. The i-RMSD value of the target protein (5.4 +/- 0.4), the Van der Waals energy (-29.3 +/- 1.7), and the Z-score were determined by simulation (-1.7). These results indicate a potential interaction between the isolated EGF protein and the myosin-9 tested. [ABSTRACT FROM AUTHOR]

Acinetobacter baumannii (A. baumannii) is an example of an opportunistic pathogen that is generally harmless to healthy individuals but can cause serious infections, such as ventilator-associated pneumonia, wound infections, and bacteremia, in critically ill hospital patients. A. baumannii produces many proteins within its genome. By analyzing its structural and functional interpretations, bioinformatics techniques can make it easier to understand this organism. The protein is still unclear, though. As a result, this study developed an in-silico method for functional and structural characterization of the uncharacterized protein (accession ID: SSI32830.1). These provide many characteristics in silico viewpoints, such as the protein's physiochemical qualities, subcellular localization, three-dimensional structure, and protein-protein interactions. Protein-protein interactions are explained using the STRING software. The projected tertiary structure evaluation was conducted using the Swiss Model. The best materials are chosen utilizing structural analyses based on Ramachandran plot analysis. This research sought to understand the function of A. baumannii. Therefore, this investigation will increase our understanding of pathophysiology and allow us to target the protein complex specifically. [ABSTRACT FROM AUTHOR]

Orthogonal aminoacyl tRNA synthetase (aaRS)/tRNA pairs are an efficient tool for the site-specific introduction of para-azido-L-phenylalanine (pAzF), a non-canonical amino acid, into the amber codon of proteins. In the present study, to improve amber suppression by aaRS, random mutagenesis using error-prone polymerase chain reaction method was carried out, and mutants with enhanced pAzF-incorporation ability were selected using fluorescence-activated cell sorting (FACS). For screening, the mutant superfolder green fluorescent protein with an amber codon was used as the reporter. Furthermore, the predicted structure of resulting aaRS mutants were analyzed. Three aaRS mutants — K776, K801, and M320 — showed 2.9-, 1.5-, and 3.7-times greater pAzF-incorporation ability, respectively, than did the control. The mutants also exhibited increased selectivity for pAzF introduction. The predicted protein models for the aaRS mutants showed that mutations in the anticodon and acceptor stem of the tRNA recognition region affected the tRNA binding affinity. These results demonstrate that random mutagenesis can cover the missed possibility of rational design or site directed mutagenesis, and screening with FACS is an efficient and rapid method for the evolution of aaRS. It can be also utilized for the development of other proteins with a variety of non-canonical amino acids. [ABSTRACT FROM AUTHOR]

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies. [ABSTRACT FROM AUTHOR]

The cancer syndrome polymerase proofreading-associated polyposis results from germline mutations in the POLE and POLD1 genes. Mutations in the exonuclease domain of these genes are associated with hyper- and ultra-mutated tumors with a predominance of base substitutions resulting from faulty proofreading during DNA replication. When a new variant is identified by gene testing of POLE and POLD1, it is important to verify whether the variant is associated with PPAP or not, to guide genetic counseling of mutation carriers. In 2015, we reported the likely pathogenic (class 4) germline POLE c.1373A > T p.(Tyr458Phe) variant and we have now characterized this variant to verify that it is a class 5 pathogenic variant. For this purpose, we investigated (1) mutator phenotype in tumors from two carriers, (2) mutation frequency in cell-based mutagenesis assays, and (3) structural consequences based on protein modeling. Whole-exome sequencing of two tumors identified an ultra-mutator phenotype with a predominance of base substitutions, the majority of which are C > T. A SupF mutagenesis assay revealed increased mutation frequency in cells overexpressing the variant of interest as well as in isogenic cells encoding the variant. Moreover, exonuclease repair yeast-based assay supported defect in proofreading activity. Lastly, we present a homology model of human POLE to demonstrate structural consequences leading to pathogenic impact of the p.(Tyr458Phe) mutation. The three lines of evidence, taken together with updated co-segregation and previously published data, allow the germline variant POLE c.1373A > T p.(Tyr458Phe) to be reclassified as a class 5 variant. That means the variant is associated with PPAP. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) and nitric oxide (NO) are two relevant signal molecules that can affect protein function throughout post-translational modifications (PTMs) such as persulfidation, S-nitrosation, metal-nitrosylation, and nitration. Lipoxygenases (LOXs) are a group of non-heme iron enzymes involved in a wide range of plant physiological functions including seed germination, plant growth and development, and fruit ripening and senescence. Likewise, LOXs are also involved in the mechanisms of response to diverse environmental stresses. Using purified soybean (Glycine max L.) lipoxygenase type 1 (LOX 1) and nitrosocysteine (CysNO) and sodium hydrosulfide (NaHS) as NO and H2S donors, respectively, the present study reveals that both compounds negatively affect LOX activity, suggesting that S-nitrosation and persulfidation are involved. Mass spectrometric analysis of nitrated soybean LOX 1 using a peroxynitrite (ONOO−) donor enabled us to identify that, among the thirty-five tyrosine residues present in this enzyme, only Y214 was exclusively nitrated by ONOO−. The nitration of Y214 seems to affect its interaction with W500, a residue involved in the substrate binding site. The analysis of the structure 3PZW demonstrates the existence of several tunnels that directly communicate the surface of the protein with different internal cysteines, thus making feasible their potential persulfidation, especially C429 and C127. On the other hand, the CysNO molecule, which is hydrophilic and bulkier than H2S, can somehow be accommodated throughout the tunnel until it reaches C127, thus facilitating its nitrosation. Overall, a large number of potential persulfidation targets and the ease by which H2S can reach them through the diffuse tunneling network could be behind their efficient inhibition. [ABSTRACT FROM AUTHOR]

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

The epithelial cell adhesion molecule gene (EPCAM, previously known as TACSTD1 or TROP1) encodes a membrane-bound protein that is localized to the basolateral membrane of epithelial cells and is overexpressed in some tumors. Biallelic mutations in EPCAM cause congenital tufting enteropathy (CTE), which is a rare chronic diarrheal disorder presenting in infancy. Monoallelic deletions of the 3' end of EPCAM that silence the downstream gene, MSH2, cause a form of Lynch syndrome, which is a cancer predisposition syndrome associated with loss of DNA mismatch repair. Here, we report 13 novel EPCAM mutations from 17 CTE patients from two separate centers, review EPCAM mutations associated with CTE and Lynch syndrome, and structurally model pathogenic missense mutations. Statistical analyses indicate that the c.499dupC (previously reported as c.498insC) frameshift mutation was associated with more severe treatment regimens and greater mortality in CTE, whereas the c.556-14A>G and c.491+1G>A splice site mutations were not correlated with treatments or outcomes significantly different than random simulation. These findings suggest that genotype-phenotype correlations may be useful in contributing to management decisions of CTE patients. Depending on the type and nature of EPCAM mutation, one of two unrelated diseases may occur, CTE or Lynch syndrome.

Introduction: In the field of bioinformatics and computational biology, protein structure modelling and analysis is a crucial aspect. However, most existing tools require a high degree of technical expertise and lack a user-friendly interface. To address this problem, we developed a protein workstation called PROFASA.Methods: PROFASA is an innovative protein workstation that combines state-of-the-art protein structure visualisation techniques with cutting-edge tools and algorithms for protein analysis. Our goal is to provide users with a comprehensive platform for all protein sequence and structure analyses. PROFASA is designed with the idea of simplifying complex protein analysis workflows into one-click operations, while providing powerful customisation options to meet the needs of professional users.Results: PROFASA provides a one-stop solution that enables users to perform protein structure evaluation, parametric analysis and protein visualisation. Users can use I-TASSER or AlphaFold2 to construct protein models with one click, generate new protein sequences, models, and calculate protein parameters. In addition, PROFASA offers features such as real-time collaboration, note sharing, and shared projects, making it an ideal tool for researchers and teaching professionals.Discussion: PROFASA’s innovation lies in its user-friendly interface and one-stop solution. It not only lowers the barrier to entry for protein computation, analysis and visualisation tools, but also opens up new possibilities for protein research and education. We expect PROFASA to advance the study of protein design and engineering and open up new research areas.

BackgroundInflammatory bowel disease (IBD) is a chronic autoimmune disorder characterized by severe inflammation and mucosal destruction of the intestine. The specific, complex molecular processes underlying IBD pathogenesis are not well understood. Therefore, this study is aimed at identifying and uncovering the role of key genetic factors in IBD.MethodThe whole exome sequences (WESs) of three consanguineous Saudi families having many siblings with IBD were analyzed to discover the causal genetic defect. Then, we used a combination of artificial intelligence approaches, such as functional enrichment analysis using immune pathways and a set of computational functional validation tools for gene expression, immune cell expression analyses, phenotype aggregation, and the system biology of innate immunity, to highlight potential IBD genes that play an important role in its pathobiology.ResultsOur findings have shown a causal group of extremely rare variants in the LILRB1 (Q53L, Y99N, W351G, D365A, and Q376H) and PRSS3 (F4L and V25I) genes in IBD-affected siblings. Findings from amino acids in conserved domains, tertiary-level structural deviations, and stability analysis have confirmed that these variants have a negative impact on structural features in the corresponding proteins. Intensive computational structural analysis shows that both genes have very high expression in the gastrointestinal tract and immune organs and are involved in a variety of innate immune system pathways. Since the innate immune system detects microbial infections, any defect in this system could lead to immune functional impairment contributing to IBD.ConclusionThe present study proposes a novel strategy for unraveling the complex genetic architecture of IBD by integrating WES data of familial cases, with computational analysis.

The retina-specific ATP-binding cassette transporter protein ABCA4 is responsible for properly continuing the visual cycle by removing toxic retinoid byproducts of phototransduction. Functional impairment caused by ABCA4 sequence variations is the leading cause of autosomal recessive inherited retinal disorders, including Stargardt disease, retinitis pigmentosa, and cone-rod dystrophy. To date, more than 3000 ABCA4 genetic variants have been identified, approximately 40 percent of which have not been able to be classified for pathogenicity assessments. This study examined 30 missense ABCA4 variants using AlphaFold2 protein modeling and computational structure analysis for pathogenicity prediction. All variants classified as pathogenic (n = 10) were found to have deleterious structural consequences. Eight of the ten benign variants were structurally neutral, while the remaining two resulted in mild structural changes. This study's results provided multiple lines of computational pathogenicity evidence for eight ABCA4 variants of uncertain clinical significance. Overall, in silico analyses of ABCA4 can provide a valuable tool for understanding the molecular mechanisms of retinal degeneration and their pathogenic impact. [ABSTRACT FROM AUTHOR]

The VPS13 protein family constitutes a novel class of bridge‐like lipid transferases. Autosomal recessive inheritance of mutations in VPS13 genes is associated with the development of neurodegenerative diseases in humans. Bioinformatic approaches previously recognized the domain architecture of these proteins. In this study, we model the first ever full‐length structures of the four human homologs VPS13A, VPS13B, VPS13C, and VPS13D in association with model membranes, to investigate their lipid transfer ability and potential structural association with membrane leaflets. We analyze the evolutionary conservation and physicochemical properties of these proteins, focusing on conserved C‐terminal amphipathic helices that disturb organelle surfaces and that, adjoined, resemble a traditional Venetian gondola. The gondola domains share significant structural homology with lipid droplet surface‐binding proteins. We introduce in silico protein‐membrane models displaying the mode of association of VPS13A, VPS13B, VPS13C, and VPS13D to donor and target membranes, and present potential models of action for protein‐mediated lipid transfer. [ABSTRACT FROM AUTHOR]