AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation

We present a new database called MetalMine that contains the classification of metal-binding sites derived from the structures of protein-metal-ion complexes. Metal-binding sites were automatically extracted from Protein Data Bank (PDB) structures, classified based on the protein domains in which the metal-binding sites are incorporated, and then manually curated. Tentative or artificial metal ion coordinations were excluded during this curation process. On the web pages of the database, the following information about metal-binding sites is presented in a hierarchical manner: the kind of metal ion, metal-binding site typically specified by the name of the protein, and each instance of metal-binding coordinates in the PDB structures. The database search engine currently supports the following two types of queries. First, given the PDB code of a protein–metal–ion complex, it provides a list of metal-binding sites incorporated in the structure file. Second, given an amino acid sequence as a query, it looks for matches with metal-binding residues in the Basic Local Alignment Search Tool (BLAST) sequence alignment. As of October 2009, MetalMine contained 412 classified entries of functional metal-binding sites, which we believe is the largest number of entries in databases available to the public.

Taking a byte out of big data

The Protein Data Bank (PDB) provides open access to >180,000 biological macromolecules that facilitate understanding of the structure and its function in biology. wwPDB collaboration and expert biocuration is a key to provide the best-curated biological data resource. wwPDB biocurators are at the very front line of structural biology who bridge between the scientists who submit PDB structures and the public who use these data. Our ultimate goal is to support research, training, and education worldwide and facilitate public awareness and understanding of these scientific discoveries. The key to long-term success and sustainability is forming the wwPDB partnership. Currently we have 15 biocurators worldwide working at RCSB PDB, PDBe and PDBj. We maintain a single global archive, no matter which PDB site users download data files from, we ensure identical data files are provided. wwPDB biocurators use a unified OneDep system to curate data. This system provides geographical workload distribution. wwPDB Biocurators setup standard procedures and follow these practices to enforce data standardization among PDB partners. We also review PDB archive and bring the data up to modern standards by conducting remediation effort. Biocuration at wwPDB is important. We manage data deposition, quality, and integrity, and provide integral support to the research community worldwide. To ensure data are Findable, Accessible, Interoperable, and Reusable (FAIR), biocuration at wwPDB focuses on data standardization and quality control. For data standardization, each PDB entry is assigned a unique, persistent identifier, data files are brought to semantic integrity and a common format that can enable large-scale data analysis and distribution. For data quality control, we cross-check with external database references such as UniProt, NCBI, or PubMed. Whatever possible, metadata are conformed to controlled vocabularies and range limits are set to catch data outliers. At the deposition time, biocuration is provided by data depositors on the sequences and taxonomy for polymers and chemical information for ligands if ligands do not match to PDB Chemical Component Dictionary. After data submission, wwPDB biocurators cross-check author’s sequence/taxonomy with UNP/NCBI references, assign existing ligands or add new ligand definitions based on author provided information, provide secondary and quaternary structure as value-added annotation. More importantly, wwPDB biocurators validate structures against experimental data adopting community-recommended validation software and provide depositors an official validation report for their manuscript submission which is now required by many scientific journals. Throughout the COVID-19 pandemic, wwPDB biocuration staff have continued to support research, training and education worldwide. All biocurators have been working remotely since March 2019. The use of unified OneDep system makes this possible with easy transition. We have record high on number of depositions with SARS-CoV-2 structures accounted for 7% of 2020 depositions. For the benefit of global health, as the structural biology community stepped up to provide much-needed insights into SARS-CoV-2, wwPDB biocurators have prioritized SARS-CoV-2 structures and working with depositors closely to release their structures immediately. As of August 1 st 2021, more than 1400 SARS-CoV-2 structures are now freely available from the PDB, reflecting enormous efforts made by the structural biology community in fighting the pandemic. The first structure with PDB ID 6LU7 was deposited on January 26 th 2020 and quickly released in about one week. PDB has recently provided coordinate versioning to allow depositors of the record to update their released structures without change of PDB IDs. Occasionally, rapid PDB data deposition and publication of SARS-CoV-2 structures driven by an understandable sense of urgency has resulted errors in public released PDB structures. The wwPDB coordinate versioning feature has enabled rapid correction of SARS-CoV-2 related structures archived in the PDB. To make the data findable, SARS-CoV-2 structures were updated with standard molecular name, taxonomy, and EC number when UNP references became available.

The motor enzymes that belong to the family of RNA helicases catalyze the strand separation of duplex RNA via ATP hydrolysis. Among these enzymes, Escherichia coli DbpA is a unique RNA helicase because it possesses ATPase-specific activity toward the peptidyl transferase center in 23 S ribosomal RNA. For this reason, it has been the subject of numerous biochemical and structure-function studies. The ATP-stimulated unwinding activity of DbpA toward specific and nonspecific RNA duplexes has been demonstrated. However, the underlying molecular and structural basis, which facilitates its helicase activities, is presently not known. We combined time-dependent limited proteolysis digestion, fluorescence spectroscopy, and three-dimensional structural homology modeling techniques to study the structural conformations of DbpA with respect to its binding to stoichiometric ratios of RNA and cofactors. We show that the conformational state of DbpA is markedly different in the ADP-bound state than in any other state (ATP- or RNA-bound). These results, together with structural homology studies, suggest that a hinge region located in the core domain of DbpA mediates such conformational changes.

With over 150,000 entries, the worldwide protein data bank (PDB) is the primary repository for 3D macromolecular structure. Unfortunately, structural, annotational, and ambiguity errors exist for carbohydrates throughout the database due in part to the lack of carbohydrate‐specific tools for checking the quality of structures prior to deposition. Our group has partnered with the PDB Biocuration team to assist in the identification and remediation of carbohydrates in their database and we have developed a user‐friendly web interface called GlyFinder to accurately find, retrieve, and assess these glycan and glycoproteins. Using GlyFinder, we have found that 45,852 of the PDB entries contain carbohydrates (30.1% of the PDB). Nearly 6,000 glycoproteins have been identified, with an average of five N‐linked glycans per glycoprotein. Only 415 glycoproteins contained O‐linked glycans, with an average of three O‐linked glycans each. A surprisingly high number of glycoprotein PDBs (500, 7.45%) contain one or more N ‐linked glycans that are alpha‐linked to the asparagine, illustrating the unfortunate errors that sometimes exist in the data. Because of such errors, and because the glycans in crystal structures are often truncated, we have also developed tools (Glycoprotein Builder) that can build realistic models of the glycoprotein with intact glycans employing the crystal structure of the protein core. These models allow us to predict the impact of glycosylation on protein function, antigenicity, immunogenicity and stability. We illustrate these capabilities for several proteins, including human Erythropoietin, HIV gp120, and influenza A hemagglutinin. Support or Funding Information Support from NIH U01CA221216, U01GM125267, and P41GM103390

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Domains are the units of protein structure, function and evolution. It is therefore essential to utilize knowledge of domains when studying the evolution of function, or when assigning function to genome sequence data. For this purpose, we have developed a database of catalytic domains, SCOPEC, by combining structural domain information from SCOP, full-length sequence information from Swiss-Prot, and verified functional information from the Enzyme Classification (EC) database. Two major problems need to be overcome to create a database of domain-function relationships; (1) for sequences, EC numbers are typically assigned to whole sequences rather than the functional unit, and (2) The Protein Data Bank (PDB) structures elucidated from a larger multi-domain protein will often have EC annotation although the relevant catalytic domain may lie elsewhere. SCOPEC entries have high quality enzyme assignments; having passed both computational and manual checks. SCOPEC currently contains entries for 75% of all EC annotations in the PDB. Overall, EC number is fairly well conserved within a superfamily, even when the proteins are distantly related. Initial analysis is encouraging; suggesting that there is a 50:50 chance of conserved function in distant homologues first detected by a third iteration PSI-BLAST search. Therefore, we envisage that a knowledge-based approach to function assignment using the domain-EC relationships in SCOPEC will gain a marked improvement over this base line. The SCOPEC database is a valuable resource in the analysis and prediction of protein structure and function. It can be obtained or queried at our website http://www.enzome.com

Background: The root smoke of Costus arabicus L. (Qust-e Shirin) has been used in Iranian traditional medicine (ITM) for rhinorrhea and catarrh, especially with infectious origin. Objectives: This study aimed to inspect potential anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activities of chemical compositions of this smoke through a docking study. Materials and Methods: Based on our previous study on the chemical compositions of C. arabicus root smoke, the chemical structure of components was revealed by GC-MS. In this study, these structures were generated in HyperChem software, and an energy-minimization procedure was performed for the molecules. For the docking study, the Hex software was used. Docking was performed using the Protein Data Bank (PDB) structure of the COVID-19 RNA-dependent RNA polymerase (RdRp) (PDB ID: 6M71) enzyme which was obtained from PDB bank, water molecules were removed, Coleman charge was added, and non-polar hydrogens were merged. Then, docking was performed. Results: The result indicated the energy of each interaction between molecules and enzymes. The lower the energy, the higher the probability of interaction will be. The best molecule was 6H-quindoline which has the best total energy among the molecules. The next molecule was 5-methoxy-alpha methyl tryptamine with -210.82 kcal energy. Their similar structure in indole moiety suggests that this fragment can play a crucial role in attachment to the enzyme and can be kept intact in future designs against COVID-19 inhibitors. Conclusion: In conclusion, this study showed that C. arabicus root smoke may be effective in SARS-CoV-2 infection. Further in vitro and in vivo studies on the activity of the smoke and its constituents against COVID-19 would be fruitful.

The statistical analysis of aromatic rings program allows for an automated search for anion-π interactions between phenylalanine residues and carboxylic acid moieties of neighboring aspartic acid or glutamic acid residues in protein data bank (PDB) structures. The program is written in C++ and is available both as a standalone code and through a web implementation that allows users to upload and analyze biomolecular structures in PDB format. The program outputs lists of Phe/Glu or Phe/Asp pairs involved in potential anion-π interactions, together with geometrical (distance and angle between the Phe's center of mass and Glu or Asp's center of charge) and energetic (quantum mechanical Kitaura-Morokuma interaction energy between the residues) descriptions of each anion-π interaction. Application of the program on the latest content of the PDB shows that anion-π interactions are present in thousands of protein structures and can possess strong energies, as low as -8.72 kcal/mol.

BackgroundIn the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB) that consists of the protein of interest and its interacting partner(s) and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice.ResultsIn this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP . For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues.ConclusionThe developed tool is very useful for the research on protein binding site analysis and prediction.

BackgroundShort fragments of proteins are fundamental starting points in various structure prediction applications, such as in fragment based loop modeling methods but also in various full structure build-up procedures. The applicability and performance of these approaches depend on the availability of short fragments in structure databanks.ResultsWe studied the representation of protein loop fragments up to 14 residues in length. All possible query fragments found in sequence databases (Sequence Space) were clustered and cross referenced with available structural fragments in Protein Data Bank (Structure Space). We found that the expansion of PDB in the last few years resulted in a dense coverage of loop conformational fragments. For each loops of length 8 in the current Sequence Space there is at least one loop in Structure Space with 50% or higher sequence identity. By correlating sequence and structure clusters of loops we found that a 50% sequence identity generally guarantees structural similarity. These percentages of coverage at 50% sequence cutoff drop to 96, 94, 68, 53, 33 and 13% for loops of length 9, 10, 11, 12, 13, and 14, respectively. There is not a single loop in the current Sequence Space at any length up to 14 residues that is not matched with a conformational segment that shares at least 20% sequence identity. This minimum observed identity is 40% for loops of 12 residues or shorter and is as high as 50% for 10 residue or shorter loops. We also assessed the impact of rapidly growing sequence databanks on the estimated number of new loop conformations and found that while the number of sequentially unique sequence segments increased about six folds during the last five years there are almost no unique conformational segments among these up to 12 residues long fragments.ConclusionThe results suggest that fragment based prediction approaches are not limited any more by the completeness of fragments in databanks but rather by the effective scoring and search algorithms to locate them. The current favorable coverage and trends observed will be further accentuated with the progress of Protein Structure Initiative that targets new protein folds and ultimately aims at providing an exhaustive coverage of the structure space.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

Objective: To analyze the physicochemical properties of HIV-1 gp120, and investigate the interaction with the human CD4 receptor, providing structural insights for antibody and vaccine design. Methods: The PDB files of gp120 and CD4 were obtained from the Protein Data Bank and AlphaFold Protein Structure Database. The Expasy-ProtParam tool was utilized to analyze the physicochemical properties of HIV-1 gp120. ClusPro 2.0 was employed to perform protein-protein docking between gp120 and CD4, and PyMOL was used to visualize binding sites and analyze interacting residues. Results: Physicochemical analysis revealed that the gp120 core (molecular weight 39,168.53 Da) has a theoretical isoelectric point of 6.84 and an instability index of 39.09, classifying it as a stable protein. The docking models identified key amino acid residues involved in the gp120-CD4 interaction, including GLN-39, ASP-35, THR-28, HIS-18, CYS-31, and GLU-44, primarily driven by hydrogen bonds and electrostatic interactions. Conclusion: These results demonstrate that the uncleaved gp120 core maintains a native-like stable structure with specific hydrophilic and charged interaction sites for CD4. This structural stability and the identified binding interfaces support the potential of gp120 as a candidate for antibody-mediated immune response targeting and vaccine development.

This paper describes a three-dimensional (3D) structural feature search, or motif search, of proteins using SS3D-P. Our present approach is based upon the use of a substructure searching program for small organic molecules, SS3D, reported in our previous work. In SS3D-P, the 3D structure of a protein is approximated with the coordinates of α-carbon atoms of the main chain, and treated as a molecular graph that is represented using a distance matrix. The program allows us to specify the tolerance value of the inter-residual distances to be compared. Several characterization schemes are also available for the different amino acid residues that can be distinguished. The program we have developed was validated by search trials with 3D query patterns of known protein motif structures: the P-loop, the EF-hand, and the Zinc finger motif using Protein Data Bank (PDB) structure files.

Advances in template-based protein docking by utilizing interfaces towards completing structural interactome