Issue 12, 2017

A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data

Abstract

Accurate elucidation of genome wide protein–protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.

Graphical abstract: A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data

Supplementary files

Article information

Article type
Paper
Submitted
02 Aug 2017
Accepted
05 Oct 2017
First published
05 Oct 2017

Mol. BioSyst., 2017,13, 2592-2602

A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data

H. Ur Rehman, I. Bari, A. Ali and H. Mahmood, Mol. BioSyst., 2017, 13, 2592 DOI: 10.1039/C7MB00484B

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