Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

3did: identification and classification of domain-based interactions of known three-dimensional structure.

ABSTRACT: The database of three-dimensional interacting domains (3did) is a collection of protein interactions for which high-resolution three-dimensional structures are known. 3did exploits the availability of structural data to provide molecular details on interactions between two globular domains as well as novel domain-peptide interactions, derived using a recently published method from our lab. The interface residues are presented for each interaction type individually, plus global domain interfaces at which one or more partners (domains or peptides) bind. The 3did web server at http://3did.irbbarcelona.org visualizes these interfaces along with atomic details of individual interactions using Jmol. The complete contents are also available for download.

SUBMITTER: Stein A

PROVIDER: S-EPMC3013799 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

3did: identification and classification of domain-based interactions of known three-dimensional structure.

Stein Amelie A Céol Arnaud A Aloy Patrick P

Nucleic acids research 20101021 Database issue

The database of three-dimensional interacting domains (3did) is a collection of protein interactions for which high-resolution three-dimensional structures are known. 3did exploits the availability of structural data to provide molecular details on interactions between two globular domains as well as novel domain-peptide interactions, derived using a recently published method from our lab. The interface residues are presented for each interaction type individually, plus global domain interfaces ...[more]

PMID: 20965963

Similar Datasets

3did: a catalog of domain-based interactions of known three-dimensional structure.

Project description:The database of 3D interacting domains (3did, available online for browsing and bulk download at http://3did.irbbarcelona.org) is a catalog of protein-protein interactions for which a high-resolution 3D structure is known. 3did collects and classifies all structural templates of domain-domain interactions in the Protein Data Bank, providing molecular details for such interactions. The current version also includes a pipeline for the discovery and annotation of novel domain-motif interactions. For every interaction, 3did identifies and groups different binding modes by clustering similar interfaces into 'interaction topologies'. By maintaining a constantly updated collection of domain-based structural interaction templates, 3did is a reference source of information for the structural characterization of protein interaction networks. 3did is updated every 6 months.

| S-EPMC3965002 | biostudies-literature

3did: interacting protein domains of known three-dimensional structure.

Project description:The database of 3D Interacting Domains (3did) is a collection of domain-domain interactions in proteins for which high-resolution three-dimensional structures are known. 3did exploits structural information to provide critical molecular details necessary for understanding how interactions occur. It also offers an overview of how similar in structure are interactions between different members of the same protein family. The database also contains Gene Ontology-based functional annotations and interactions between yeast proteins from large-scale interaction discovery studies. A web-based tool to query 3did is available at http://3did.embl.de.

| S-EPMC539991 | biostudies-literature

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database.

Project description:The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

| S-EPMC8782172 | biostudies-literature

3did Update: domain-domain and peptide-mediated interactions of known 3D structure.

Project description:The database of 3D interacting domains (3did) is a collection of protein interactions for which high-resolution 3D structures are known. 3did exploits structural information to provide the crucial molecular details necessary for understanding how protein interactions occur. Besides interactions between globular domains, the new release of 3did also contains a hand-curated set of transient peptide-mediated interactions. The interactions are grouped in Interaction Types, based on the mode of binding, and the different binding interfaces used in each type are also identified and catalogued. A web-based tool to query 3did is available at http://3did.irbbarcelona.org.

| S-EPMC2686500 | biostudies-literature

Three-dimensional structure of N-terminal domain of DnaB helicase and helicase-primase interactions in Helicobacter pylori.

Project description:Replication initiation is a crucial step in genome duplication and homohexameric DnaB helicase plays a central role in the replication initiation process by unwinding the duplex DNA and interacting with several other proteins during the process of replication. N-terminal domain of DnaB is critical for helicase activity and for DnaG primase interactions. We present here the crystal structure of the N-terminal domain (NTD) of H. pylori DnaB (HpDnaB) helicase at 2.2 A resolution and compare the structural differences among helicases and correlate with the functional differences. The structural details of NTD suggest that the linker region between NTD and C-terminal helicase domain plays a vital role in accurate assembly of NTD dimers. The sequence analysis of the linker regions from several helicases reveals that they should form four helix bundles. We also report the characterization of H. pylori DnaG primase and study the helicase-primase interactions, where HpDnaG primase stimulates DNA unwinding activity of HpDnaB suggesting presence of helicase-primase cohort at the replication fork. The protein-protein interaction study of C-terminal domain of primase and different deletion constructs of helicase suggests that linker is essential for proper conformation of NTD to interact strongly with HpDnaG. The surface charge distribution on the primase binding surface of NTDs of various helicases suggests that DnaB-DnaG interaction and stability of the complex is most probably charge dependent. Structure of the linker and helicase-primase interactions indicate that HpDnaB differs greatly from E.coli DnaB despite both belong to gram negative bacteria.

| S-EPMC2761005 | biostudies-literature

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.

Project description:The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

| S-EPMC9131459 | biostudies-literature

Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information.

Project description:The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions.

| S-EPMC8615982 | biostudies-literature

A structure-based classification and analysis of protein domain family binding sites and their interactions.

Project description:While the number of solved 3D protein structures continues to grow rapidly, the structural rules that distinguish protein-protein interactions between different structural families are still not clear. Here, we classify and analyse the secondary structural features and promiscuity of a comprehensive non-redundant set of domain family binding sites (DFBSs) and hetero domain-domain interactions (DDIs) extracted from our updated KBDOCK resource. We have partitioned 4001 DFBSs into five classes using their propensities for three types of secondary structural elements ("?" for helices, "?" for strands, and "?" for irregular structure) and we have analysed how frequently these classes occur in DDIs. Our results show that ? elements are not highly represented in DFBSs compared to ? and ? elements. At the DDI level, all classes of binding sites tend to preferentially bind to the same class of binding sites and ?/? contacts are significantly disfavored. Very few DFBSs are promiscuous: 80% of them interact with just one Pfam domain. About 50% of our Pfam domains bear only one single-partner DFBS and are therefore monogamous in their interactions with other domains. Conversely, promiscuous Pfam domains bear several DFBSs among which one or two are promiscuous, thereby multiplying the promiscuity of the concerned protein.

| S-EPMC4498303 | biostudies-literature

HNF1B-driven three-dimensional chromatin structure for molecular classification in pancreatic cancers.

Project description:The molecular subtypes of pancreatic cancer (PC), either classical/progenitor-like or basal/squamous-like, are currently a major topic of research because of their direct association with clinical outcomes. Some transcription factors (TFs) have been reported to be associated with these subtypes. However, the mechanisms by which these molecular signatures of PCs are established remain unknown. Epigenetic regulatory processes, supported by dynamic changes in the chromatin structure, are essential for transcriptional profiles. Previously, we reported the importance of open chromatin profiles in the biological features and transcriptional status of PCs. Here, we aimed to analyze the relationships between three-dimensional (3D) genome structures and the molecular subtypes of human PCs using Hi-C analysis. We observed a correlation of the specific elements of 3D genome modules, including compartments, topologically associating domains, and enhancer-promoter loops, with the expression of related genes. We focused on HNF1B, a TF that is implicated in the progenitor subtype. Forced expression of HNF1B in squamous-type PC organoids induced the upregulation and downregulation of genes associated with progenitor and squamous subtypes, respectively. Long-range genomic interactions induced by HNF1B were accompanied by compartment modulation and H3K27ac redistribution. We also found that these HNF1B-induced changes in subtype-related gene expression required an intrinsically disordered region, suggesting a possible involvement of phase separation in compartment modulation. Thus, mapping of 3D structural changes induced by TFs, such as HNF1B, may become a useful resource for further understanding the molecular features of PCs.

| S-EPMC10067390 | biostudies-literature

Classification of protein domains based on their three-dimensional shapes (CPD3DS).

Project description:Protein design has become a powerful method to expand the number of natural proteins and design customized proteins according to demands. Domain-based protein design spares the need to create novel elements from scratch, which makes it a more efficient strategy than scratch-based protein design in designing multi-domain proteins, protein complexes and biomaterials. As the surface shape plays a central role in domain-domain and protein-protein interactions, a global map of the surface shapes of all domains should be very beneficial for domain-based protein design. Therefore, in this study, we characterized the surface shapes of protein domains, collected from CATH and SCOP databases, with their 3D-Zernike descriptors (3DZDs). Then similarities of domain shape features were identified, and all domains were classified accordingly. The preferences of the combinations of domains between different clusters were analyzed in natural proteins from the Protein Data Bank. A user-friendly website, termed CPD3DS, was also developed for storage, retrieval, analyses and visualization of our results. This work not only provides an overall view of protein domain shapes by showing their variety and similarities, but also opens up a new avenue to understand the properties of protein structural domains, and design principles of protein architectures.

| S-EPMC8429105 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data