A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.

Authors

Sanguthevar Rajasekaran, University of Connecticut - StorrsFollow
Jerlin Camilus Merlin, University of Connecticut - Storrs
Vamsi Kundeti, University of Connecticut - Storrs
Tian Mi, University of Connecticut - Storrs
Aaron Oommen, University of Connecticut - Storrs
Jay Vyas, University of Connecticut Heath Center,
Izua Alaniz, University of Nevada, Las VegasFollow
Keith Chung, University of Nevada, Las Vegas
Farah Chowdhury, University of Nevada, Las Vegas
Sandeep Deverasetty, University of Nevada, Las Vegas
Tenisha M. Irvey, University of Nevada, Las Vegas
David Lacambacal, University of Nevada, Las Vegas
Darlene Lara, University of Nevada, Las Vegas
Subhasree Panchangam, University of Nevada, Las Vegas
Viraj Rathnayake, University of Nevada, Las Vegas
Paula Watts, University of Nevada Las Vegas
Martin R. Schiller, University of Nevada, Las VegasFollow

Document Type

Article

Publication Date

1-2011

Publication Title

Proteins

Volume

79

Issue

1

First page number:

153

Last page number:

164

Abstract

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).

Keywords

Algorithms; Amino acid sequence; Animals; Cell physiology; Computer simulation; Databases; Protein; GRB2 Adaptor Protein/chemistry; HomoloGene; Humans; Insect Proteins/chemistry; Mice; Minimotif Miner; Models; Molecular; Protein binding; Protein Interaction Domains and Motifs; Protein Interaction Mapping; Proteins/chemistry; Protein-protein interactions; Rats; SLiM; Short Linear Motifs; Software

Disciplines

Computer Sciences | Life Sciences | Molecular Biology | Structural Biology

Language

English

Repository Citation

Rajasekaran, S., Merlin, J. C., Kundeti, V., Mi, T., Oommen, A., Vyas, J., Alaniz, I., Chung, K., Chowdhury, F., Deverasetty, S., Irvey, T. M., Lacambacal, D., Lara, D., Panchangam, S., Rathnayake, V., Watts, P., Schiller, M. R. (2011). A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.. Proteins, 79(1), 153-164.
http://dx.doi.org/10.1002/prot.22868