A coevolution analysis for identifying protein-protein interactions by Fourier transform

• Published in: PloS one 2017-04, Vol.12 (4), p.e0174862
• Main Authors: Yin, Changchuan, Yau, Stephen S-T
• Format: Article
• Language: English
• Subjects: Algorithms; Amino acids; Biology and Life Sciences; Coevolution; Computation; Computer and Information Sciences; Computer applications; Drug development; E coli; Ebola virus; Ebolavirus; Ebolavirus - classification; Ebolavirus - metabolism; Escherichia coli; Escherichia coli Proteins - metabolism; Experimental methods; Experiments; Fourier Analysis; Fourier transforms; Genomes; Glycoproteins; Hydrophobic and Hydrophilic Interactions; Identification methods; Immune response; Immune system; Influenza; Influenza A virus - metabolism; Mathematical models; Medicine and Health Sciences; Methods; Mutation; Phylogenetics; Phylogeny; Predictions; Production methods; Protein interaction; Protein Interaction Mapping; Proteins; Research and Analysis Methods; RNA polymerase; Sequence Alignment; Signal processing; Signal transduction; Structure-function relationships; Studies; Transcription; Transduction; Viral diseases; Viral Proteins - genetics; Viral Proteins - metabolism; Virology; Viruses; VP24 protein; VP40 protein
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0174862

Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).