MIPDS: a comprehensive database on the molecular interactions in protein dimer structures

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.

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, &amp; SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

Editorial overview: New protein production tools for structural biology

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.

BackgroundStructural information about epitopes, particularly the three-dimensional (3D) structures of antigens in complex with immune receptors, presents a valuable source of data for immunology. This information is available in the Protein Data Bank (PDB) and provided in curated form by the Immune Epitope Database and Analysis Resource (IEDB). With continued growth in these data and the importance in understanding molecular level interactions of immunological interest there is a need for new specialized molecular visualization and analysis tools.ResultsThe EpitopeViewer is a platform-independent Java application for the visualization of the three-dimensional structure and sequence of epitopes and analyses of their interactions with antigen-specific receptors of the immune system (antibodies, T cell receptors and MHC molecules). The viewer renders both 3D views and two-dimensional plots of intermolecular interactions between the antigen and receptor(s) by reading curated data from the IEDB and/or calculated on-the-fly from atom coordinates from the PDB. The 3D views and associated interactions can be saved for future use and publication. The EpitopeViewer can be accessed from the IEDB Web site through the quick link 'Browse Records by 3D Structure.'ConclusionThe EpitopeViewer is designed and been tested for use by immunologists with little or no training in molecular graphics. The EpitopeViewer can be launched from most popular Web browsers without user intervention. A Java Runtime Environment (RJE) 1.4.2 or higher is required.

Phytocompounds are gaining popularity, due to lesser toxicity, greater bioavailability and high chemodiversity. Phytocompounds are evolving as new leads for the development of a novel drug. Phytocompounds exhibit a wide range of biological properties, and are being used as antioxidants, immunomodulatory, antimicrobial, cardiovascular, and anticancer drugs. However, their identification is still relatively limited. The major hurdle in the discovery of an efficient phytocompound is a complete dependence on time-consuming in vitro and in vivo screening systems. Alternatively, the computational drug discovery approach of using an online database and bioinformatics tools would be a cost-effective and time-saving option. Recent advancements in computational and structural biology have attributed the three-dimensional (3D) structure to many natural drug-like compounds and disease-related target macromolecules, and have been stored into renowned databases like Protein Data Bank (RCSB-PDB), DrugBank, and ZINC. The drug discovery filed is swiftly advancing with bioinformatics applications, and the availability of digitalized molecular data is paving new avenues in Computer-aided drug discovery (CADD) area. Virtual library screening (VLS), a widely recognized technique due to it being cost-effective, time-saving, and less laborious, evaluates drug candidates using computational tools, such as AutoDock Vina, GOLD, and Glide. This chapter provides comprehensive information about the available biological databases and bioinformatics tools that are useful for the analysis of plant-derived bioactive compounds and their molecular interactions in diseases. Applications of advanced bioinformatics tools and methods for designing, optimization, and high-throughput screening of phytocompounds are detailed. In addition, it emphasizes the advantages, limitations, challenges, and future perspectives of the computational approaches in analyzing phytocompound interactions.

Glutathione-S-transferases (GSTs) are ubiquitous detoxification enzymes that catalyse the conjugation of electrophilic substrates to glutathione. Here, we present the crystal structures of Gtt2, a GST of Saccharomyces cerevisiae, in apo and two ligand-bound forms, at 2.23 A, 2.20 A and 2.10 A, respectively. Although Gtt2 has the overall structure of a GST, the absence of the classic catalytic essential residues--tyrosine, serine and cysteine--distinguishes it from all other cytosolic GSTs of known structure. Site-directed mutagenesis in combination with activity assays showed that instead of the classic catalytic residues, a water molecule stabilized by Ser129 and His123 acts as the deprotonator of the glutathione sulphur atom. Furthermore, only glycine and alanine are allowed at the amino-terminus of helix-alpha1 because of stereo-hindrance. Taken together, these results show that yeast Gtt2 is a novel atypical type of cytosolic GST.

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

Inter-residue interactions play an essential role in driving protein folding, and analysis of these interactions increases our understanding of protein folding and stability and facilitates the development of tools for protein structure and function prediction. In this work, we systematically characterized the change of inter-residue interactions at various sequence separation cutoffs using two protein datasets. The first set included 100 diverse, nonredundant and high-resolution soluble protein structures, covering all four major structural classes, all-alpha, alpha/beta, alpha+beta, and all-beta; and the second set included 20 diverse, nonredundant and high-resolution membrane protein structures, representing 19 unique superfamilies. It was shown that the average number of inter-residue interactions in structures of both datasets displays the power-law behavior. Fitting parameters of the power-law function are directly related to the structural classes analyzed. These findings provided further insight into the distribution of short-, medium-, and long-range inter-residue interactions in both soluble and membrane proteins and could be used for protein structure prediction.

Characterizing the variability within an ensemble of protein structures is a common requirement in structural biology and bioinformatics. With the increasing number of protein structures becoming available, there is a need for new tools capable of automating the structural comparison of large ensemble of structures. We present Protein Structural Statistics (PSS), a command-line program written in Perl for Unix-like environments, dedicated to the calculation of structural statistics for a set of proteins. PSS can perform multiple sequence alignments, structure superpositions, calculate Cartesian and dihedral coordinate statistics, and execute cluster analyses. An HTML report that contains a convenient summary of results with figures, tables, and hyperlinks can also be produced. PSS is a new tool providing an automated way to compare multiple structures. It integrates various types of structural analyses through an user-friendly and flexible interface, facilitating the access to powerful but more specialized programs. PSS is easy to modify and extend and is distributed under a free and open source license. The relevance of PSS is illustrated by examples of application to pertinent biological problems.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein-protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a "big data" science requiring more efficient analysis tools. This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html .

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence–structure–property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed.

The principle of three-dimensional protein structure formation is a long-standing conundrum in structural biology. A globular domain of a soluble protein is formed by a network of atomic contacts among amino acid residues, but regions without intramolecular non-local contacts are often observed in the protein structure, especially in loop, linker, and peripheral segments with secondary structures. Although these regions can play key roles for protein function as interfaces for intermolecular interactions, their nature remains unclear. Here, we termed protein segments without non-local contacts as floating segments and sought them in tens of thousands of entries in the Protein Data Bank. As a result, we found that 0.72% of residues are in floating segments. Regarding secondary structural elements, coil structures are enriched in floating segments, especially for long segments. Interactions with polypeptides and polynucleotides, but not chemical compounds, are enriched in floating segments. The amino acid preferences of floating segments are similar to those of surface residues, with exceptions; the small side chain amino acids, Gly and Ala, are preferred, and some charged side chains, Arg and His, are disfavored for floating segments compared to surface residues. Our comprehensive characterization of floating segments may provide insights into understanding protein sequence-structure-function relationships.