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

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.

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.

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.

Monoclonal antibodies have recently started to deliver on their promise as highly specific and active drugs; however, a more effective, knowledge-based approach to the selection, design, and optimization of potential therapeutic antibodies is currently limited by the surprising lack of detailed structural information for complexes formed with target proteins. Here we show that complexes formed with minimal antigen binding single chain variable fragments (scFv) reliably reflect all the features of the binding interface present in larger Fab fragments, which are commonly used as therapeutics, and report the development of a robust, reliable, and relatively rapid approach to the determination of high resolution models for scFv-target protein complexes. This NMR spectroscopy-based approach combines experimental determination of the interaction surfaces and relative orientations of the scFv and target protein, with NMR restraint-driven, semiflexible docking of the proteins to produce a reliable and highly informative model of the complex. Experience with scFvs and Fabs targeted at a number of secreted regulatory proteins suggests that the approach will be applicable to many therapeutic antibodies targeted at proteins, and its application is illustrated for a potential therapeutic antibody targeted at the cytokine IL-1beta. The detailed structural information that can be obtained by this approach has the potential to have a major impact on the rational design and development of an increasingly important class of biological pharmaceuticals.

Detailed knowledge of the tertiary and quaternary structure of proteins and protein complexes is of immense importance in understanding their functionality. Similarly, variations in the conformational states of proteins form the underlying mechanisms behind many biomolecular processes, numerous of which are disease-related. Thus, the availability of reliable and accurate biophysical techniques that can provide detailed information concerning these issues is of paramount importance. Ion mobility spectrometry (IMS) coupled to mass spectrometry (MS) offers a unique opportunity to separate multi-component biomolecular entities and to measure the molecular mass and collision cross-section of individual components in a single, rapid (</= 2 min) experiment, providing 3D- architectural information directly. Here we report a method of calibrating a commercially available electrospray ionisation (ESI)-travelling wave ion mobility spectrometry (TWIMS)-mass spectrometer using known cross-sectional areas determined for a range of biomolecules by conventional IMS-MS. Using this method of calibration, we have analysed a range of proteins of differing mass and 3D architecture in their native conformations by ESI-TWIMS-MS and found that the cross-sectional areas measured in this way compare extremely favourably with cross-sectional areas calculated using an in-house computing method based on Protein Data Bank NMR-derived co-ordinates. This not only provides a high degree of confidence in the calibration method, but also suggests that the gas phase ESI- TWIMS-MS measurements relate well to solution-based measurements derived from other biophysical techniques. In order to determine which instrumental parameters affect the ESI-TWIMS-MS cross-sectional area calibration, a systematic study of the parameters used to optimise TWIMS drift time separations has been carried out, observing the effect each parameter has on drift times and IMS resolution. Finally, the ESI-TWIMS-MS cross-sectional area calibration has been applied to the analysis of the amyloidogenic protein beta(2)-microglobulin and measurements for three co-populated conformational families, present under denaturing conditions, have been made: the folded, partially unfolded and unfolded states.

An energy potential is constructed and trained to succeed in fold recognition for the general population of proteins as well as an important class which has previously been problematic: small, disulfide-bearing proteins. The potential is modeled on solvation, with the energy a function of side chain burial and the number of disulfide bonds. An accurate disulfide recognition algorithm identifies cysteine pairs which have the appropriate orientation to form a disulfide bridge. The potential has 22 energy parameters which are optimized so the Protein Data Bank (PDB) structure for each sequence in a training set is the lowest in energy out of thousands of alternative structures. One parameter per amino acid type reflects burial preference and a single parameter is used in an overpacking term. Additionally, one optimized parameter provides a favorable contribution for each disulfide identified in a given protein structure. With little training, the potential is >80% accurate in ungapped threading tests using a variety of proteins. The same level of accuracy is observed in a threading test of small proteins which have disulfide bonds. Importantly, the energy potential is also successful with proteins having uncrosslinked cysteines.

We used the crystal structure of prolyl oligopeptidase (POP) with bound Z-pro-prolinal (ZPP) inhibitor (Protein Data Bank (PDB) structure 1QFS) to perform an intensive molecular dynamics study of the POP-ZPP complex. We performed 100 ns of simulation with the hemiacetal bond, through which the ZPP is bound to the POP, removed in order to better investigate the binding cavity environment. From basic analysis, measuring the radius of gyration, root mean square deviation, solvent accessible surface area and definition of the secondary structure of protein, we determined that the protein structure is highly stable and maintains its structure over the entire simulation time. This demonstrates that such long time simulations can be performed without the protein structure losing stability. We found that water bridges and hydrogen bonds play a negligible role in binding the ZPP thus indicating the importance of the hemiacetal bond. The two domains of the protein are bound by a set of approximately 12 hydrogen bonds, specific to the particular POP protein.

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

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

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.

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.

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.

The Protein Data Bank (PDB) provides open access to &gt;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.

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.

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.