AutoCoEv -- a high-throughput in silico pipeline for predicting inter-protein co-evolution

Abstract

Protein-protein communications govern cellular processes via complex regulatory networks, that are still far from being understood. Thus, identifying novel interactions between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Co-evolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, evolutionary analysis of large arrays of proteins, in silico, is a highly time-consuming effort, which has limited the usage of this method to protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly computational pipeline for the search of co-evolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting co-evolution, AutoCoEv achieves seamless automation and parallelization of the workflow. Importantly, we provide a patch to CAPS2 source code to strengthen its statistical output, allowing for multiple comparisons correction and enhanced analysis of the results. We apply the pipeline to inspect co-evolution among 324 proteins identified to locate at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple strong coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, available at https://github.com/mattilalab/autocoev, can be used to predict functional interactions from large datasets in a time and cost-efficient manner.