Literature DB >> 34156447

mPPI: a database extension to visualize structural interactome in a one-to-many manner.

Yekai Zhou1,2, Hongjun Chen1, Sida Li1, Ming Chen1,3.   

Abstract

Protein-protein interaction (PPI) databases with structural information are useful to investigate biological functions at both systematic and atomic levels. However, most existing PPI databases only curate binary interactome. From the perspective of the display and function of PPI, as well as the structural binding interface, the related database and resources are summarized. We developed a database extension, named mPPI, for PPI structural visualization. Comparing with the existing structural interactomes that curate resolved PPI conformation in pairs, mPPI can visualize target protein and its multiple interactors simultaneously, which facilitates multi-target drug discovery and structure prediction of protein macro-complexes. By employing a protein-protein docking algorithm, mPPI largely extends the coverage of structural interactome from experimentally resolved complexes. mPPI is designed to be a customizable and convenient plugin for PPI databases. It possesses wide potential applications for various PPI databases, and it has been used for a neurodegenerative disease-related PPI database as demonstration. Scripts and implementation guidelines of mPPI are documented at the database tool website. Database URL  http://bis.zju.edu.cn/mppi/. © Crown copyright 2021.

Entities:  

Year:  2021        PMID: 34156447      PMCID: PMC8218707          DOI: 10.1093/database/baab036

Source DB:  PubMed          Journal:  Database (Oxford)        ISSN: 1758-0463            Impact factor:   3.451


  57 in total

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Journal:  Nucleic Acids Res       Date:  2011-12-01       Impact factor: 16.971

7.  3did: a catalog of domain-based interactions of known three-dimensional structure.

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8.  APID database: redefining protein-protein interaction experimental evidences and binary interactomes.

Authors:  Diego Alonso-López; Francisco J Campos-Laborie; Miguel A Gutiérrez; Luke Lambourne; Michael A Calderwood; Marc Vidal; Javier De Las Rivas
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

9.  An empirical framework for binary interactome mapping.

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Journal:  Nat Methods       Date:  2008-12-07       Impact factor: 28.547

10.  PTIR: Predicted Tomato Interactome Resource.

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