PPIcons: Identification of protein-protein interaction sites in selected organisms

Brijesh K. Sriwastava , Subhadip Basu , Ujjwal Maulik , Dariusz Plewczyński


The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein-protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively. © 2013 The Author(s).

Author Brijesh K. Sriwastava - [Government College of Engineering and Leather Technology, Govt. of West Bengal]
Brijesh K. Sriwastava,,
, Subhadip Basu - [Jadavpur University]
Subhadip Basu,,
, Ujjwal Maulik - [Jadavpur University]
Ujjwal Maulik,,
, Dariusz Plewczyński (FMIS / DIPS)
Dariusz Plewczyński,,
- Department of Information Processing Systems
Journal seriesJournal of Molecular Modeling, ISSN 1610-2940, e-ISSN 0948-5023
Issue year2013
ASJC Classification1604 Inorganic Chemistry; 1703 Computational Theory and Mathematics; 1605 Organic Chemistry; 1606 Physical and Theoretical Chemistry; 1706 Computer Science Applications; 1503 Catalysis
Languageen angielski
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 25.0, 04-06-2020, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 04-06-2020, ArticleFromJournal
Publication indicators Scopus Citations = 18; WoS Citations = 14; Scopus SNIP (Source Normalised Impact per Paper): 2013 = 0.728; WoS Impact Factor: 2013 = 1.867 (2) - 2013=1.911 (5)
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