Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Nicola Barbarini,Sergio Comincini,Luca Simonelli,Riccardo Bellazzi,Alberto Azzalin

doi:10.1155/2010/670125

Nicola Barbarini, Sergio Comincini + Show 3 more

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https://doi.org/10.1155/2010/670125

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Abstract

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

Highlights

Proteins rarely perform their biological functions independently, since they usually interact with each other
Known structures complexes involving Target protein (TP) are searched in public available databases, the Protein Data Bank (PDB) and the Protein Quaternary Structure (PQS)
The proposed procedure was tested by using the growth factor receptor-bound protein 2 (GRB2) as the target protein

Summary

Introduction

Proteins rarely perform their biological functions independently, since they usually interact with each other. Many experimental methods have been developed to study protein interactions, such as the two hybrid system in yeast, the affinity purification followed by mass spectrometry and the phage display libraries [1,2,3,4] The use of these techniques led to the creation of many databases containing a great number of protein-protein interactions, such as the Database of Interacting Proteins (DIPs), the General. Once a list of candidates is obtained, it is necessary to analyze in vivo every possible interactor by expensive, timeconsuming, and labour-intensive experimental techniques in order to validate the in vitro experimental result For this reason, in silico methods for the prediction of protein interactions are considered valid tools to reduce the number of candidates [9]

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