PPI3D clusters: non-redundant datasets of protein-protein, protein-peptide and protein-nucleic acid complexes, interaction interfaces and binding sites
Confirmed Presenter: Justas Dapkunas, Institute of Biotechnology, Life Sciences Center
Track: 3DSIG: Structural Bioinformatics and Computational Biophysics
Room: 03B
Format: In person
Moderator(s): Gonzalo Parra
Authors List: Show
- Justas Dapkunas, Justas Dapkunas, Institute of Biotechnology
- Kliment Olechnovic, Kliment Olechnovic, Institute of Biotechnology
- Ceslovas Venclovas, Ceslovas Venclovas, Institute of Biotechnology
Presentation Overview:Show
To accomplish their functions in living organisms, proteins usually interact with various biological macromolecules, including other proteins and nucleic acids. Despite recent progress in structure prediction, only part of these interactions can be predicted accurately, and modeling those involving nucleic acids is especially hard. Therefore, improved computational methods for analysis and prediction of biomolecular interactions are in high demand. The development of such methods largely depends on the availability of reliable data. However, the experimental data in the Protein Data Bank (PDB) are noisy and hard to interpret. To facilitate the analysis of the biomolecular interactions, we developed the PPI3D web resource that is based on a database of clustered non-redundant sets of biomolecular complexes, interaction interfaces and binding sites. The structures are clustered based on both sequence and structure similarity, thus retaining the alternative interaction modes. All protein-protein, protein-peptide and protein-nucleic acid interaction interfaces and binding sites are pre-analyzed by means of Voronoi tessellation. The data are updated every week to keep in sync with the PDB. The users can query the data by different criteria, select the interactions of interest, download the desired data subsets in tabular format and as coordinate files, and use them for detailed investigation of protein interactions or for training the machine learning models. We expect that the PPI3D clusters will become a useful resource for researchers working on diverse problems related to biomolecular interactions. PPI3D is available at http://bioinformatics.ibt.lt/ppi3d/.