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A computational tool for identifying minimotifs in protein–protein interactions and improving the accuracy of minimotif predictions
Author(s) -
Rajasekaran Sanguthevar,
Merlin Jerlin Camilus,
Kundeti Vamsi,
Mi Tian,
Oommen Aaron,
Vyas Jay,
Alaniz Izua,
Chung Keith,
Chowdhury Farah,
Deverasatty Sandeep,
Irvey Tenisha M.,
Lacambacal David,
Lara Darlene,
Panchangam Subhasree,
Rathnayake Viraj,
Watts Paula,
Schiller Martin R.
Publication year - 2011
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22868
Subject(s) - computer science , context (archaeology) , protein–protein interaction , set (abstract data type) , computational biology , data mining , machine learning , biology , genetics , paleontology , programming language
Protein–protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well‐accepted physical and chemical forces that drive protein–protein interactions and the large collections of identified protein–protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein–protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein–protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false‐positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis ( P = 0.001). Proteins 2010. © 2010 Wiley‐Liss, Inc.