Three dimensional structures provide a wealth of information on the biological function and the evolutionary history of macromolecules. They can be used to examine sequence-structure-function relationships, interactions, active sites, and more.

The purpose of this database is to represent the relationship between protein structural change and ligand binding. We classified protein structural changes into 7 classes, in terms of the ligand binding sites and the location where the dominant motion occurs.

This dataset Protein Chemical Structure Comparison from Three Drug Databases is a selection of a 3-way consensus list from the paper "Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database" (2013) [Abstract]. It includes 352 proteins-in-common between the three drug databases.

PIBASE is a collection of all protein structural interfaces extracted from the Protein Data Bank and PQS structure databases. Both chain-chain and domain-domain (SCOP and CATH definitions) interfaces are detected.

Database of non-redundant sets of protein - small-molecule complexes that are especially suitable for structure-based drug design and protein - small-molecule interaction research. PSMB supports: * Support frequent updates - The number of new structures in the PDB is growing rapidly. In order to utilize these structures, frequent updates are required. In contrast to manual procedures which require significant time and effort per update, generation of the PSMDB database is fully automatic thereby facilitating frequent database updates. * Consider both protein and ligand structural redundancy - In the database, two complexes are considered redundant if they share a similar protein and ligand (the protein - small-molecule non-redundant set). This allows the database to contain structural information for the same protein bound to several different ligands (and vice-versa). Additionally, for completeness, the database contains a set of non-redundant complexes when only protein structural redundancy is considered (our protein non-redundant set). The following images demonstrate the structural redundancy of the protein complexes in the PDB compared to the PSMDB. * Efficient handling of covalent bonds -Many protein complexes contain covalently bound ligands. Typically, protein-ligand databases discard these complexes; however, the PSMDB simply removes the covalently bound ligand from the complex, retaining any non-covalently bound ligands. This increases the number of usable complexes in the database. * Separate complexes into protein and ligand files -The PSMDB contains individual structure files for both the protein and all non-covalently bound ligands. The unbound proteins are in PDB format while the individual ligands are in SDF format (in their native coordinate frame).

Database containing all antibody structures available in the PDB, annotated and presented in consistent fashion.Each structure is annotated with number of properties including experimental details, antibody nomenclature (e.g. heavy-light pairings), curated affinity data and sequence annotations. You can use the database to inspect individual structures, create and download datasets for analysis, search the database for structures with similar sequences to your query, monitor the known structural repetoire of antibodies.

Database of known enzyme structures that have been deposited in the Protein Data Bank (PDB). The enzyme structures are classified by their E.C. number of the ENZYME Data Bank. Browse the classification hierarchy or enter an EC number or search-string. There are currently 45,638 PDB-enzyme entries in the PDB (as at 23 February, 2013) involving 38,109 separate PDB files - some files having more than one E.C. number associated with them.

List of NAR Online Molecular Biology Database Collection resources that utilize PDB data (July 2018)

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose. Access to crystal structure data is a critical step in solving research and applications problems in materials researches, and these data are of interest to analysts in areas such as materials design, property prediction and compound identification. NIST Crystallographic Data Center, in collaboration with partners all over the world, evaluates and disseminates chemical, physical and crystallographic information published on these materials. NIST Standard Reference Database 3: NIST Inorganic Crystal Structure Database (NIST ICSD) is a comprehensive collection of crystal structure data of nonorganic compounds (including inorganics, ceramics, minerals, pure elements, metals, and intermetallic systems) containing over 210,000 entries and covering the literature from 1913. NIST ICSD includes entries that fall into the following categories: full structure data from experimental refinement or derived from their iso-structural structure types, theoretically predicted structures from computer simulations, as well as partially characterized structures. The NIST ICSD web application provides materials researchers with a user-friendly interface to search the database based on bibliographic information, chemistry, unit cell, space group, experimental settings, mineral name/group and other derived data from expert evaluation. In addition, it also provides users with functions to easily create and examine results from various crystallographic computations, such as reduced cell, bond distance/angle, calculated powder diffraction data, and structure standardization.

The Molecular Modeling DataBase (MMDB), also known as Entrez Structure, is a database of experimentally determined structures obtained from the RCSB Protein Data Bank (PDB). MMDB is developed by the Structure Group of the NCBI Computational Biology Branch. The data processing procedure at NCBI results in the addition of a number of useful features that facilitate computation on the data and link them to many other data types in the Entrez system. The structure database is considerably smaller than Entrez''s Protein or Nucleotide databases, but a large fraction of all known protein sequences have homologs in this set, and one may often learn more about a protein by examining 3-D structures of its homologs. These are accessible as Related Structures in the Links menu of Entrez Protein sequence records (illustrated example). It is then possible to align the query protein to the structure-based sequence, as shown in the illustration on this page. Additional resources can be used along with MMDB to interactively view the structures, find similar 3D structures, learn about the types of interactions and bound chemicals that have been found to exist among the similar 3D structures, and more.

CATH Domain Classification List (latest release) - protein structural domains classified into CATH hierarchy.

Protein Structure Initiative - TargetTrack protein target registration database (795 MB, gzipped tarball)

The Protein Structure Initiative was a high-throughput structural genomics effort from 2000-2015 focused on developing technologies to enable greater coverage of protein structure space. Over its 15-year tenure, over 100 investigators at 35 centers (see ContributingCenters.xls) declared over 350,000 protein sequences (targets) that they would study using state-of-the-art protein production and structure determination methods. Many of these targets were selected through bioinformatics-based methods to serve as representatives for sequence and structure clusters.

From 2003-2010, these selected sequences and some basic identifying metadata were kept in a database called TargetDB, created at the Research Collaboratory for Structural Bioinformatics at Rutgers University. In 2008, a second database named PepcDB was created to track detailed experimental trial history and the standard protocols used by the PSI centers. These two databases became the principal structural genomics target databases, and were rolled into the PSI Structural Biology Knowledgebase in 2008.

As part of the third phase of the PSI, TargetDB and PepcDB were merged into a single resource, TargetTrack, to facilitate one-stop access to the data as well as expanding the schema to include new required data items. Participating centers deposited the latest status on their active targets and the protocols that were used (along with any deviations) on a weekly or quarterly basis. TargetTrack provided a variety of pre-computed data downloads on a weekly basis as well.

In July 2017, the Structural Biology Knowledgebase ceased operations. The files provided in this tarball represent the final datafiles generated by TargetTrack (timestamp June 30, 2017). Please read the README included in this dataset for descriptions of each file.

The entire TargetTrack datafile in XML format can be found in /TargetTrack XML files/tt.xml.gz

Key documentation can be found in the /Documentation folder.
TargetTrack schema: targetTrack-v1.4.1.pdf
Spreadsheet with TargetTrack enumerations for relevant fields: targetTrackEnumeratedDataItems-v1.4.1-1.xls
Image depicted the XML data schema: targetTrack-v1.4.1.jpg

These files are 868 MB in total size, uncompressed.
To open the tarball, use the command 'tar -zxvf TargetTrack-1Jul2017.tar.gz'

-- created by the PSI Structural Biology Knowledgebase, July 5, 2017

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases.

Database of apo and holo structure pairs of proteins before and after binding. Various protein functions have been shown directly associated with conformational transitions triggered by binding other molecules. Tertiary structures determined in the unbound and bound state are usually named apo and holo structures, respectively. AH-DB is the largest database of apo-holo structure pairs and provides a sophisticated interface to search and view the collected data. It contains 746314 apo-holo pairs of 3638 proteins from 702 organisms.

The Protein Ensemble Database is an open access database for the deposition of structural ensembles, including intrinsically disordered proteins.

The AlphaFold Protein Structure Database is a collection of protein structure predictions made using the machine learning model AlphaFold. AlphaFold was developed by DeepMind , and this database was created in partnership with EMBL-EBI . For information on how to interpret, download and query the data, as well as on which proteins are included / excluded, and change log, please see our main dataset guide and FAQs . To interactively view individual entries or to download proteomes / Swiss-Prot please visit https://alphafold.ebi.ac.uk/ . The current release aims to cover most of the over 200M sequences in UniProt (a commonly used reference set of annotated proteins). The files provided for each entry include the structure plus two model confidence metrics (pLDDT and PAE). The files can be found in the Google Cloud Storage bucket gs://public-datasets-deepmind-alphafold-v4 with metadata in the BigQuery table bigquery-public-data.deepmind_alphafold.metadata . If you use this data, please cite: Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021) Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021) This public dataset is hosted in Google Cloud Storage and is available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

HSSP (homology-derived structures of proteins) is a derived database merging structural (2-D and 3-D) and sequence information (1-D). For each protein of known 3D structure from the Protein Data Bank, the database has a file with all sequence homologues, properly aligned to the PDB protein.

This database is a collection of protein-protein homo- and hetero-complexes as well as domain-domain structures. This issue of the database contains 17.024 entries (as of October 2007) of which 1350 are two-chain protein hetero-complexes, 7773 homodimers and 1589 are one-chain proteins parsed into two domains (domain structures).

A curated database of structure-based alignments for homologous protein families. All known protein structure are clustered into homologous families (i.e., common ancestry), and the sequences of representative members of each family are aligned on the basis of their 3D structures using the programs MNYFIT, STAMP and COMPARER. These structure-based alignments are annotated with JOY and examined individually.

BioGRID had 337 interactions between the set of 56 proteins. Of the 5 databases, BioGRID contained the most edges, with high coverage of interactions in the other 4 databases. The interactions missing from BioGRID did not arise due to missed references (except for 3 studies of functional rather than physical associations) but due to missed interactions in the same references. The other 4 databases contained a total of 69 interactions not present in BioGRID, and 52 not present in our original database that had been augmented by added edges and through curation of the SH3/PRD and kinase references. Of these 52 interactions, three were removed for erroneous citations, 20 were found only through functional association, and therefore removed, and the remainder were observed in only a single probe of physical interactions.