Protein Bioinformatics Research Articles

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.

Bioinformatics, expression, purification, and inflammation-inducing effect of Mycoplasma genitalium GroEL protein

To preliminarily understand the pathogenic mechanism of Mycoplasma genitalium (Mg) GroEL protein, we used bioinformatics tools to predict the structure and function of Mg GroEL protein and then constructed the recombinant plasmid pET-28a-GroEL. The protein expression was induced by 0.2 mmol/L IPTG, and the expressed protein was purified by Ni-iminodicitic acid (IDA) column affinity. Tohoku Hospital Pediatrics-1 (THP-1) cells were exposed to 2 μg/mL Mg rGroEL. The levels of interleukin (IL)-1β and tumor necrosis factor (TNF)-α in the cell supernatant were measured by ELISA, and that of IL-6 was measured by an automatic chemiluminescence instrument. The activation of the nuclear factor-kappa B (NF-κB) signaling pathway was visualized by immunofluorescence and Western blotting. The results showed that Mg GroEL was a stable hydrophilic protein composed of 543 amino acid residues, with the relative molecular mass of 58.44 kDa, an isoelectric point of 5.68, and a molecular formula of C2568H4300N700O825S8. The secondary structure was mainly composed of α-helices and random coils. Mg GroEL contained 12 B-cell dominant epitopes and 10 T-cell dominant epitopes. It exhibited high homology with the GroEL proteins from Mycoplasma pneumoniae, M. agalactiae, M. arthritidis, M. hyopneumoniae, and M. bovis. Mg rGroEL activated the NF-κB signaling pathway and promoted the secretion of IL-1β, IL-6, and TNF-α in THP-1 cells. These results suggest that Mg GroEL exhibits substantial antigenicity and possesses the capability of triggering inflammation in host cells. This study establishes a theoretical basis for future investigations pertaining to the role and pathogenic mechanisms of Mg GroEL.

Protein digestion and amino acid absorption mechanisms along the midgut of Musca domestica larvae

A model of protein digestion and peptide and amino acid absorption along the midgut of Musca domestica larvae was proposed and supported by RNA-Seq analyses, protein bioinformatics, microvillar-membrane-enriched midgut proteomics, and enzymatic activities. Peptidase genes are highly expressed in the posterior midgut (PM), whereas those for cathepsins have expression limited to the middle midgut (MM). MM has the lowest levels of gene expression of almost all peptidases but has high expression of genes for membrane-bound serine endopeptidases. The anterior midgut (AM) has intermediate expression values of serine endopeptidase and aminopeptidase (AP) genes and low expression of carboxypeptidases (CPs). Gene expression and peptidase activities were usually consistent for putative intracellular and membrane-bound enzymes. However, secreted peptidase gene expression and activities have divergent values, especially in the PM, which may be due to the countercurrent water flux causing enzyme recycling, thus decreasing their excretion. Data suggest that Trys and APs act in the AM. In the acidic MM, lysozymes kill microorganisms found in the diet, releasing proteins digested by cathepsins D, which may also digest Trys coming from the AM. Finally, highly active serine endopeptidases, CPs, dipeptidases, and APs complete protein digestion in PM. Absorption of peptides and amino acids coupled to protons may occur along the midgut, especially in PM, as occurs for facilitated amino acid transport. Absorption with sodium ions is probably restricted to AM and PM. Our findings provide valuable insights into the protein digestion and amino acid absorption mechanism in M. domestica larvae.

Restoring protein glycosylation with GlycoShape

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape (https://glycoshape.org), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons.

Improving protein function prediction by learning and integrating representations of protein sequences and function labels.

As fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. We introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms. https://github.com/BioinfoMachineLearning/TransFew.

Molecular Docking of Phytomolecules of Grain Amaranth (Amaranthus hypochondriacus) with AKR1C3Protein Involved in Prostate Cancer in Human Beings

Abstract: This study focuses on exploring how the bioactive compounds found in amaranth— phytol, squalene, and α-tocopherol—could potentially offer medicinal benefits in the context of prostate cancer. The investigation involves a docking study with AKRC13, an important target linked to the control of prostate cancer, aiming to uncover their potential effects against this disease. Costeffective and efficient cancer treatment options are crucial because of the high expenses associated with current cancer therapies as well as their side effects. Amaranth (Amaranthus hypochondriacus) is a pseudocereal crop abundant in squalene, α-tocopherol, and phytol, which shows promising foodbased therapy for various diseases, including cancer. Prostate cancer has been a significant contributor to mortality globally, but the introduction of relugolix has emerged as a crucial therapeutic intervention in its treatment. Hence, this study was conducted to investigate the interactions between grain amaranth bioactive compounds squalene, phytol, and α-tocopherol with AKRC13 protein utilizing a molecular docking approach facilitated by Autodock Vina software. Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB-PDB) (http://www.rcsb.org/) was used for retrieving the 3D crystal structure of the target protein, AKRC13 (PDB ID: 7c7f). The 3D structure of bioactive compounds squalene, phytol, and α-tocopherol were retrieved from the PubChem database, following which Open-Babel was used to change the format from .sdf to .pdb. Furthermore, pharmacokinetics characteristics were also considered along with Lipinski’s rule of five using SwissADME (http://www. Swiss adme.ch/index.php) and pkCSM (http://structure.bioc.cam.ac.uk/pkcsm), indicating their potential as a drug candidate in the initial stage. The potential anticancer properties of the ligands were predicted using PASS software. Following the completion of the docking study, it became evident that α-tocopherol demonstrated the most significant binding energy, followed by squalene and phytol, in comparison to the established drug, relugolix. This implies that the chosen bioactive compounds might possess enzyme-inhibiting properties, indicating their potential for further in vivo anticancer screening using model organisms. The findings serve as stepping stones for advancing the potential use of the discussed bioactive compounds as a potential drug candidate for prostate cancer.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems.To address this issue, we developed AAontology—a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations—using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

CH-π Interactions Are Required for Human Galectin-3 Function.

Glycan-binding proteins, or lectins, recognize distinct structural elements of polysaccharides, to mediate myriad biological functions. Targeting glycan-binding proteins involved in human disease has been challenging due to an incomplete understanding of the molecular mechanisms that govern protein-glycan interactions. Bioinformatics and structural studies of glycan-binding proteins indicate that aromatic residues with the potential for CH-π interactions are prevalent in glycan-binding sites. However, the contributions of these CH-π interactions to glycan binding and their relevance in downstream function remain unclear. An emblematic lectin, human galectin-3, recognizes lactose and N-acetyllactosamine-containing glycans by positioning the electropositive face of a galactose residue over the tryptophan 181 (W181) indole forming a CH-π interaction. We generated a suite of galectin-3 W181 variants to assess the importance of these CH-π interactions to glycan binding and function. As determined experimentally and further validated with computational modeling, variants with smaller or less electron-rich aromatic side chains (W181Y, W181F, W181H) or sterically similar but nonaromatic residues (W181M, W181R) showed poor or undetectable binding to lactose and attenuated ability to bind mucins or agglutinate red blood cells. The latter functions depend on multivalent binding, highlighting that weakened CH-π interactions cannot be overcome by avidity. Two galectin-3 variants with disrupted hydrogen bonding interactions (H158A and E184A) showed similarly impaired lactose binding. Molecular simulations demonstrate that all variants have decreased binding orientation stability relative to native galectin-3. Thus, W181 collaborates with the endogenous hydrogen bonding network to enhance binding affinity for lactose, and abrogation of these CH-π interactions is as deleterious as eliminating key hydrogen bonding interactions. These findings underscore the critical roles of CH-π interactions in carbohydrate binding and lectin function and will aid the development of novel lectin inhibitors.

Electrochemical cofactor recycling of bacterial microcompartments.

Bacterial microcompartments (BMCs) are prokaryotic organelles that consist of a protein shell which sequesters metabolic reactions in its interior. While most of the substrates and products are relatively small and can permeate the shell, many of the encapsulated enzymes require cofactors that must be regenerated inside. We have analyzed the occurrence of an enzyme previously assigned as a cobalamin (vitamin B12) reductase and, curiously, found it in many unrelated BMC types that do not employ B12 cofactors. We propose NAD+ regeneration as a new function of this enzyme and name it MNdh, for Metabolosome NADH dehydrogenase. Its partner shell protein BMC-TSE assists in passing the generated electrons to the outside. We support this hypothesis with bioinformatic analysis, functional assays, EPR spectroscopy, protein voltammetry and structural modeling verified with X-ray footprinting. This discovery represents a new paradigm for the BMC field, identifying a new, widely occurring route for cofactor recycling and a new function for the shell as separating redox environments.

Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

Composition and abundance of midgut plasma membrane proteins in two major hemipteran vectors of plant viruses, Bemisia tabaci and Myzus persicae.

Multiple species within the order Hemiptera cause severe agricultural losses on a global scale. Aphids and whiteflies are of particular importance due to their role as vectors for hundreds of plant viruses, many of which enter the insect via the gut. To facilitate the identification of novel targets for disruption of plant virus transmission, we compared the relative abundance and composition of the gut plasma membrane proteomes of adult Bemisia tabaci (Hemiptera: Aleyrodidae) and Myzus persicae (Hemiptera: Aphididae), representing the first study comparing the gut plasma membrane proteomes of two different insect species. Brush border membrane vesicles were prepared from dissected guts, and proteins extracted, identified and quantified from triplicate samples via timsTOF mass spectrometry. A total of 1699 B. tabaci and 1175 M. persicae proteins were identified. Following bioinformatics analysis and manual curation, 151 B. tabaci and 115 M. persicae proteins were predicted to localize to the plasma membrane of the gut microvilli. These proteins were further categorized based on molecular function and biological process according to Gene Ontology terms. The most abundant gut plasma membrane proteins were identified. The ten plasma membrane proteins that differed in abundance between the two insect species were associated with the terms "protein binding" and "viral processes." In addition to providing insight into the gut physiology of hemipteran insects, these gut plasma membrane proteomes provide context for appropriate identification of plant virus receptors based on a combination of bioinformatic prediction and protein localization on the surface of the insect gut.

Quantum Mechanics-Based Fast and Reliable Prediction of Binding Pose Structures.

Predicting the binding poses of docking with an accurate estimation of binding energies is highly important but very challenging in computational drug design. A quantum mechanics (QM) calculation-based docking approach considering multiple conformations and orientations of the ligand is introduced here to tackle the problem. This QM docking consists of three steps: generating an ensemble of binding poses with a conventional docking simulation, computing the binding energies with self-consistent charge density functional theory tightly binding with dispersion correction (DFTB-D) to selecting the 10 top binding modes, and optimizing the selected binding mode structures using the ONIOM(DFTB:PM7) technique to determine the binding poses. The ONIOM(DFTB-D:PM6) docking approach is tested on 121 ligand-receptor biocomplexes with the crystal structures obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). The result shows that the new method is highly satisfactory for the accurate prediction of the binding poses. The new docking method should be beneficial to structure-based drug design.

Genome-Wide Identification and Hormone Response Analysis of the COBL Gene Family in Barley.

Barley (Hordeum vulgare L.), a diverse cereal crop, exhibits remarkable versatility in its applications, ranging from food and fodder to industrial uses. The content of cellulose in barley is significantly influenced by the COBRA genes, which encode the plant glycosylphosphatidylinositol (GPI)-anchored protein (GAP) that plays a pivotal role in the deposition of cellulose within the cell wall. The COBL (COBRA-Like) gene family has been discovered across numerous species, yet the specific members of this family in barley remain undetermined. In this study, we discovered 13 COBL genes within the barley genome using bioinformatics methods, subcellular localization, and protein structure analysis, finding that most of the barley COBL proteins have a signal peptide structure and are localized on the plasma membrane. Simultaneously, we constructed a phylogenetic tree and undertook a comprehensive analysis of the evolutionary relationships. Other characteristics of HvCOBL family members, including intraspecific collinearity, gene structure, conserved motifs, and cis-acting elements, were thoroughly characterized in detail. The assessment of HvCOBL gene expression in barley under various hormone treatments was conducted through qRT-PCR analysis, revealing jasmonic acid (JA) as the predominant hormonal regulator of HvCOBL gene expression. In summary, this study comprehensively identified and analyzed the barley COBL gene family, aiming to provide basic information for exploring the members of the HvCOBL gene family and to propose directions for further research.

Genetic analysis of a fetus with cryptophthalmos due to variants of FREM2 gene

To explore the genetic etiology of a fetus with cryptophthalmos detected by prenatal ultrasonography. A fetus undergoing induced labor at 32nd gestational week due to absence of bilateral eye fissures detected by prenatal ultrasonography in January 2017 was selected as the study subject. Umbilical cord blood sample from the fetus and peripheral blood samples from its parents were collected for the extraction of genomic DNA. Pathogenic variants were screened through whole exome sequencing (WES) and verified by Sanger sequencing. Pathogenicity of candidate variants was verified by bioinformatic analysis and protein structure simulation. Based on the results of genetic testing, prenatal diagnosis was provided to the couple upon their subsequent pregnancy. The couple had four adverse pregnancies previously. The aborted fetus was the fifth, with fused bilateral upper and lower eyelids, poorly developed eyeballs, adhesion of the cornea with the upper eyelid, low-set ears, and abnormal plantar creases, and was diagnosed with cryptophthalmos. WES and Sanger sequencing revealed that the fetus has harbored compound heterozygous variants of the FREM2 gene, namely c.4537G>A (p.D1513N) and c.7292C>T (p.T2431M). Both variants were unreported associated with cryptophthalmos previously. Protein structure simulation showed that they may lead to loss of hydrogen bonds in the protein product. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), both variants were predicted to be likely pathogenic (PM1_Supporting+PM2_Supporting+PM5+PP3+PP4; PM2_Supporting+PM3+PP3+PP4). The mother was performed prenatal diagnosis in her sixth pregnancy based on the variants detected in this family, and delivered a daughter with normal phenotype. The FREM2: c.4537G>A and c.7292C>T compound heterozygous variants probably underlay the pathogenesis of cryptophthalmos in this fetus. Above finding has enriched the mutational spectrum of the FREM2 gene.

MOLECULAR DOCKING DYNAMICS OF SELECTED BENZYLIDENE AMINO PHENYL ACETAMIDES AS TMK INHIBITORS USING HIGH THROUGHPUT VIRTUAL SCREENING (HTVS)

Objective: Thymidylate kinase (TMK) plays a crucial role in bacterial DNA synthesis by catalyzing the phosphorylation of deoxythymidine monophosphate (dTMP) to form deoxythymidine diphosphate (dTDP). Consequently, this enzyme emerges as a promising target for developing novel antibacterial drugs. However, no antibiotics were reported for this target, especially active against Staphylococcus aureus thymidylate kinase. Methods: Benzylidene acetamide-based ligands were examined for their potency using the in silico method. These novel ligand structures were built using ChemDraw software. The protein was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) website. The molecular docking and binding free energy calculation by prime Molecular Mechanics in Generalized Bond Surface Area (MM-GBSA) was performed for selected ligands. A 100 ns molecular dynamic simulation was also performed to assess the stability of the potential ligand as TMK inhibitors. Results: All ten molecules have shown good glide scores and hydrophobic and hydrogen hydrophobic hydrogen bonding interactions with Arg48, Arg36, and π-π stacking Phe66 in the TMK enzyme (PDB: 4HLC). Among them, N-(2-ethylphenyl)-2-(4-((4-nitrobenzylidene) amino) phenoxy) acetamide molecule had high XP-docking scores of-3.27 kcal/mol based on extra-precision data. Prime Molecular Mechanics in Generalized Bond Surface Area study (MM-GBSA) studies also showed promising binding affinities that are ΔBind (-65.80), ΔLipo (-28.55), and ΔVdW (-55.10). Phe66 amino acid residue maintained continuous connections with the ligand during MD simulation. This ligand showed promising binding affinity with the SaTMK target. Conclusion: The N-(2-ethylphenyl)-2-(4-((4-nitrobenzylidene) amino) phenoxy) acetamide ligand at the position of the benzene ring displayed nitrogen and oxygen group, thus indicating good potential activity as the inhibitor of TMK to treat antibacterial agents.

NaProGraph: Network Analyzer for Interactions between Nucleic Acids and Proteins

Background: Interactions of RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. Methods: This study proposes an online network provider tool (NaProGraph) that offers an intuitive and user-friendly interface for studying interactions between nucleic acids (NA) and proteins. NaPro- Graph utilizes a comprehensive and curated dataset encompassing nearly all interacting macromolecules in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Results: Researchers can employ this online tool to focus on a specific portion of the PDB, investigate its associated relationships, and visualize and extract pertinent information. This tool provides insights into the frequency of atoms and residues between proteins and nucleic acids (NAs) and the similarity of the macromolecules' primary structures. Conclusion: Furthermore, the functional similarity of proteins can be inferred using protein families and clans from Pfam.

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

Salicylic acid inducing the expression of maize anti-insect gene SPI: a potential control strategy for Ostrinia furnacalis

BackgroundDue to being rooted in the ground, maize (Zea mays L.) is unable to actively escape the attacks of herbivorous insects such as the Asian corn borer (Ostrinia furnacalis). In contrast to the passive damage, plants have evolved defense mechanisms to protect themselves from herbivores. Salicylic acid, a widely present endogenous hormone in plants, has been found to play an important role in inducing plant resistance to insects. In this study, we screened and identified the insect resistance gene SPI, which is simultaneously induced by SA and O. furnacalis feeding, through preliminary transcriptome data analysis. The functional validation of SPI was carried out using bioinformatics, RT-qPCR, and heterologous expression protein feeding assays.ResultsBoth SA and O. furnacalis treatment increased the expression abundance of SA-synthesis pathway genes and SPI in three maize strains, and the upregulation of SPI was observed strongly at 6 hours post-treatment. The expression of SPI showed a temporal relationship with SA pathway genes, indicating that SPI is a downstream defense gene regulated by SA. Protein feeding assays using two different expression vectors demonstrated that the variation in SPI protein activity among different strains is mainly due to protein modifications.ConclusionsOur research results indicate that SPI, as a downstream defense gene regulated by SA, is induced by SA and participates in maize's insect resistance. The differential expression levels of SPI gene and protein modifications among different maize strains are one of the reasons for the variation in insect resistance. This study provides new insights into ecological pest control in maize and valuable insights into plant responses to SA-induced insect resistance.

AutoRNC: An automated modeling program for building atomic models of ribosome-nascent chain complexes

The interpretation of experimental studies of co-translational protein folding often benefits from the use of computational methods that seek to model or simulate the nascent chain and its interactions with the ribosome. Building realistic 3D models of ribosome-nascent chain (RNC) constructs often requires expert knowledge, so to circumvent this issue, we describe here AutoRNC, an automated modeling program capable of constructing large numbers of plausible atomic models of RNCs within minutes. AutoRNC takes input from the user specifying any regions of the nascent chain that contain secondary or tertiary structure and attempts to build conformations compatible with those specifications-and with the constraints imposed by the ribosome-by sampling and progressively piecing together dipeptide conformations extracted from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB). Despite using only modest computational resources, we show here that AutoRNC can build plausible conformations for a wide range of RNC constructs for which experimental data have already been reported.

Protein structure accuracy estimation using geometry-complete perceptron networks.

Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally-predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry-complete perceptron network for protein structure EMA (GCPNet-EMA), where we demonstrate through rigorous computational benchmarks that GCPNet-EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground-truth measures of per-residue (per-target) structural accuracy compared to baseline state-of-the-art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet-EMA are available on GitHub, and a public web server implementation is freely available.