Literature DB >> 23855658

Protein-protein interactions and prediction: a comprehensive overview.

Gopichandran Sowmya, Shoba Ranganathan1.   

Abstract

Molecular function in cellular processes is governed by protein-protein interactions (PPIs) within biological networks. Selective yet specific association of these protein partners contributes to diverse functionality such as catalysis, regulation, assembly, immunity, and inhibition in a cell. Therefore, understanding the principles of protein-protein association has been of immense interest for several decades. We provide an overview of the experimental methods used to determine PPIs and the key databases archiving this information. Structural and functional information of existing protein complexes confers knowledge on the principles of PPI, based on which a classification scheme for PPIs is then introduced. Obtaining high-quality non-redundant datasets of protein complexes for interaction characterisation is an essential step towards deciphering their underlying binding principles. Analysis of physicochemical features and their documentation has enhanced our understanding of the molecular basis of protein-protein association. We describe the diverse datasets created/collected by various groups and their key findings inferring distinguishing features. The currently available interface databases and prediction servers have also been compiled.

Mesh:

Year:  2014        PMID: 23855658     DOI: 10.2174/09298665113209990056

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN: 0929-8665            Impact factor:   1.890


  6 in total

1.  Linking structural features of protein complexes and biological function.

Authors:  Gopichandran Sowmya; Edmond J Breen; Shoba Ranganathan
Journal:  Protein Sci       Date:  2015-07-14       Impact factor: 6.725

2.  STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Authors:  Damian Szklarczyk; Annika L Gable; David Lyon; Alexander Junge; Stefan Wyder; Jaime Huerta-Cepas; Milan Simonovic; Nadezhda T Doncheva; John H Morris; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

3.  Discrete structural features among interface residue-level classes.

Authors:  Gopichandran Sowmya; Shoba Ranganathan
Journal:  BMC Bioinformatics       Date:  2015-12-09       Impact factor: 3.169

Review 4.  Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients.

Authors:  Makoto Ono; Don E Burgess; Elizabeth A Schroder; Claude S Elayi; Corey L Anderson; Craig T January; Bin Sun; Kalyan Immadisetty; Peter M Kekenes-Huskey; Brian P Delisle
Journal:  Biomolecules       Date:  2020-08-04

5.  Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Authors:  Hang Li; Xiu-Jun Gong; Hua Yu; Chang Zhou
Journal:  Molecules       Date:  2018-08-01       Impact factor: 4.411

6.  Predicting Protein-Protein Interactions Using BiGGER: Case Studies.

Authors:  Rui M Almeida; Simone Dell'Acqua; Ludwig Krippahl; José J G Moura; Sofia R Pauleta
Journal:  Molecules       Date:  2016-08-09       Impact factor: 4.411

  6 in total

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