Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Stein A

PROVIDER: S-EPMC539991 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

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

Stein Amelie A Russell Robert B RB Russell Robert B RB Aloy Patrick P

Nucleic acids research 20050101 Database issue

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 int ...[more]

PMID: 15608228

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Project description:Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Dataset Information

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

Publications

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

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