Systematic characterization and prediction of coenzyme A-associated proteins using sequence and network information

• Published in: Briefings in bioinformatics 2021-07, Vol.22 (4)
• Main Authors: Fan, Bing-Liang, Jiang, Zheng, Sun, Jun, Liu, Rong
• Format: Article
• Language: English
• Subjects: Algorithms; Amino acid composition; Amino acids; Binding; Biological activity; Classifiers; Coenzyme A; Complementarity; Computer applications; Embedding; Evolutionary conservation; Homology; Hydrophobicity; Prediction models; Proteins
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbaa308

Abstract Coenzyme A-associated proteins (CAPs) are a category of functionally important proteins involved in multiple biological processes through interactions with coenzyme A (CoA). To date, unfortunately, the specific differences between CAPs and other proteins have yet to be systemically investigated. Moreover, there are no computational methods that can be used specifically to predict these proteins. Herein, we characterized CAPs from multifaceted viewpoints and revealed their specific preferences. Compared with other proteins, CAPs were more likely to possess binding regions for CoA and its derivatives, were evolutionarily highly conserved, exhibited ordered and hydrophobic structural conformations, and tended to be densely located in protein–protein interaction networks. Based on these biological insights, we built seven classifiers using predicted CoA-binding residue distributions, word embedding vectors, remote homolog numbers, evolutionary conservation, amino acid composition, predicted structural features and network properties. These classifiers could effectively identify CAPs in Homo sapiens, Mus musculus and Arabidopsis thaliana. The complementarity among the individual classifiers prompted us to build a two-layer stacking model named CAPE for improving prediction performance. We applied CAPE to identify some high-confidence candidates in the three species, which were tightly associated with the known functions of CAPs. Finally, we extended our algorithm to cross-species prediction, thereby developing a generic CAP prediction model. In summary, this work provides a comprehensive survey and an effective predictor for CAPs, which can help uncover the interplay between CoA and functionally relevant proteins.