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FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank.

ABSTRACT: This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies. Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/.

SUBMITTER: Toti D

PROVIDER: S-EPMC9239314 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Residue preference mapping of ligand fragments in the Protein Data Bank.

Project description:The interaction between small molecules and proteins is one of the major concerns for structure-based drug design because the principles of protein-ligand interactions and molecular recognition are not thoroughly understood. Fortunately, the analysis of protein-ligand complexes in the Protein Data Bank (PDB) enables unprecedented possibilities for new insights. Herein, we applied molecule-fragmentation algorithms to split the ligands extracted from PDB crystal structures into small fragments. Subsequently, we have developed a ligand fragment and residue preference mapping (LigFrag-RPM) algorithm to map the profiles of the interactions between these fragments and the 20 proteinogenic amino acid residues. A total of 4032 fragments were generated from 71?798 PDB ligands by a ring cleavage (RC) algorithm. Among these ligand fragments, 315 unique fragments were characterized with the corresponding fragment-residue interaction profiles by counting residues close to these fragments. The interaction profiles revealed that these fragments have specific preferences for certain types of residues. The applications of these interaction profiles were also explored and evaluated in case studies, showing great potential for the study of protein-ligand interactions and drug design. Our studies demonstrated that the fragment-residue interaction profiles generated from the PDB ligand fragments can be used to detect whether these fragments are in their favorable or unfavorable environments. The algorithm for a ligand fragment and residue preference mapping (LigFrag-RPM) developed here also has the potential to guide lead chemistry modifications as well as binding residues predictions.

| S-EPMC3081969 | biostudies-literature

SuperLigands - a database of ligand structures derived from the Protein Data Bank.

Project description:BackgroundCurrently, the PDB contains approximately 29,000 protein structures comprising over 70,000 experimentally determined three-dimensional structures of over 5,000 different low molecular weight compounds. Information about these PDB ligands can be very helpful in the field of molecular modelling and prediction, particularly for the prediction of protein binding sites and function.DescriptionHere we present an Internet accessible database delivering PDB ligands in the MDL Mol file format which, in contrast to the PDB format, includes information about bond types. Structural similarity of the compounds can be detected by calculation of Tanimoto coefficients and by three-dimensional superposition. Topological similarity of PDB ligands to known drugs can be assessed via Tanimoto coefficients.ConclusionSuperLigands supplements the set of existing resources of information about small molecules bound to PDB structures. Allowing for three-dimensional comparison of the compounds as a novel feature, this database represents a valuable means of analysis and prediction in the field of biological and medical research.

| S-EPMC1173082 | biostudies-literature

Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB).

Project description:The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein-protein, protein-DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

| S-EPMC7139665 | biostudies-literature

iPfam: a database of protein family and domain interactions found in the Protein Data Bank.

Project description:The database iPfam, available at http://ipfam.org, catalogues Pfam domain interactions based on known 3D structures that are found in the Protein Data Bank, providing interaction data at the molecular level. Previously, the iPfam domain-domain interaction data was integrated within the Pfam database and website, but it has now been migrated to a separate database. This allows for independent development, improving data access and giving clearer separation between the protein family and interactions datasets. In addition to domain-domain interactions, iPfam has been expanded to include interaction data for domain bound small molecule ligands. Functional annotations are provided from source databases, supplemented by the incorporation of Wikipedia articles where available. iPfam (version 1.0) contains >9500 domain-domain and 15 500 domain-ligand interactions. The new website provides access to this data in a variety of ways, including interactive visualizations of the interaction data.

| S-EPMC3965099 | biostudies-literature

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

Project description:BackgroundProtein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).ResultsWe have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section).ConclusionOur method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

| S-EPMC2413245 | biostudies-literature

PDBj Mine: design and implementation of relational database interface for Protein Data Bank Japan.

Project description:This article is a tutorial for PDBj Mine, a new database and its interface for Protein Data Bank Japan (PDBj). In PDBj Mine, data are loaded from files in the PDBMLplus format (an extension of PDBML, PDB's canonical XML format, enriched with annotations), which are then served for the user of PDBj via the worldwide web (WWW). We describe the basic design of the relational database (RDB) and web interfaces of PDBj Mine. The contents of PDBMLplus files are first broken into XPath entities, and these paths and data are indexed in the way that reflects the hierarchical structure of the XML files. The data for each XPath type are saved into the corresponding relational table that is named as the XPath itself. The generation of table definitions from the PDBMLplus XML schema is fully automated. For efficient search, frequently queried terms are compiled into a brief summary table. Casual users can perform simple keyword search, and 'Advanced Search' which can specify various conditions on the entries. More experienced users can query the database using SQL statements which can be constructed in a uniform manner. Thus, PDBj Mine achieves a combination of the flexibility of XML documents and the robustness of the RDB. Database URL: http://www.pdbj.org/

| S-EPMC2997606 | biostudies-literature

A comprehensive database of verified experimental data on protein folding kinetics.

Project description:Insights into protein folding rely increasingly on the synergy between experimental and theoretical approaches. Developing successful computational models requires access to experimental data of sufficient quantity and high quality. We compiled folding rate constants for what initially appeared to be 184 proteins from 15 published collections/web databases. To generate the highest confidence in the dataset, we verified the reported lnkf value and exact experimental construct and conditions from the original experimental report(s). The resulting comprehensive database of 126 verified entries, ACPro, will serve as a freely accessible resource (https://www.ats.amherst.edu/protein/) for the protein folding community to enable confident testing of predictive models. In addition, we provide a streamlined submission form for researchers to add new folding kinetics results, requiring specification of all the relevant experimental information according to the standards proposed in 2005 by the protein folding consortium organized by Plaxco. As the number and diversity of proteins whose folding kinetics are studied expands, our curated database will enable efficient and confident incorporation of new experimental results into a standardized collection. This database will support a more robust symbiosis between experiment and theory, leading ultimately to more rapid and accurate insights into protein folding, stability, and dynamics.

| S-EPMC4253821 | biostudies-other

Ligand-centered assessment of SARS-CoV-2 drug target models in the Protein Data Bank.

Project description:A bright spot in the SARS-CoV-2 (CoV-2) coronavirus pandemic has been the immediate mobilization of the biomedical community, working to develop treatments and vaccines for COVID-19. Rational drug design against emerging threats depends on well-established methodology, mainly utilizing X-ray crystallography, to provide accurate structure models of the macromolecular drug targets and of their complexes with candidates for drug development. In the current crisis, the structural biological community has responded by presenting structure models of CoV-2 proteins and depositing them in the Protein Data Bank (PDB), usually without time embargo and before publication. Since the structures from the first-line research are produced in an accelerated mode, there is an elevated chance of mistakes and errors, with the ultimate risk of hindering, rather than speeding up, drug development. In the present work, we have used model-validation metrics and examined the electron density maps for the deposited models of CoV-2 proteins and a sample of related proteins available in the PDB as of April 1, 2020. We present these results with the aim of helping the biomedical community establish a better-validated pool of data. The proteins are divided into groups according to their structure and function. In most cases, no major corrections were necessary. However, in several cases significant revisions in the functionally sensitive area of protein-inhibitor complexes or for bound ions justified correction, re-refinement, and eventually reversioning in the PDB. The re-refined coordinate files and a tool for facilitating model comparisons are available at https://covid-19.bioreproducibility.org. DATABASE: Validated models of CoV-2 proteins are available in a dedicated, publicly accessible web service https://covid-19.bioreproducibility.org.

| S-EPMC7276724 | biostudies-literature

The Membrane Protein Data Bank.

Project description:The Membrane Protein Data Bank (MPDB) is an online, searchable, relational database of structural and functional information on integral, anchored and peripheral membrane proteins and peptides. Data originates from the Protein Data Bank and other databases, and from the literature. Structures are based on X-ray and electron diffraction, nuclear magnetic resonance and cryoelectron microscopy. The MPDB is searchable online by protein characteristic, structure determination method, crystallization technique, detergent, temperature, pH, author, etc. Record entries are hyperlinked to the PDB and Pfam for viewing sequence, three-dimensional structure and domain architecture, and for downloading coordinates. Links to PubMed are also provided. The MPDB is updated weekly in parallel with the Protein Data Bank. Statistical analysis of MPDB records can be performed and viewed online. A summary of the statistics as applied to entries in the MPDB is presented. The data suggest conditions appropriate for crystallization trials with novel membrane proteins.

| S-EPMC2792347 | biostudies-literature

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

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| S-EPMC5408918 | biostudies-literature

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