AutoCoEv – a high-throughput in silico pipeline for predicting novel protein-protein interactions

Protein-protein interactions govern the cellular decision-making and various functions. Thus, identifying new interactions between proteins can significantly facilitate our understanding on the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and as such provides a powerful approach to search for novel molecular partners. However, evolutionary analyses of large arrays of proteins, in silico, is a highly time-consuming effort, which has prevented the usage of this method beyond protein pairs or narrow protein families, for example. Here, we developed AutoCoEv, a user-friendly computational pipeline for the search of coevolution between hundreds and even thousands of proteins. Driving over 10 individual programs, with CAPS2 as a key software for coevolution detection, AutoCoEv achieves seamless automation and parallelization of the workflow. In addition, we provide a patch to CAPS2 source code to improve its statistical output, allowing for multiple comparisons correction. We apply the method to inspect coevolution among 346 individual proteins identified to be in close proximity to the B cell receptor (BCR) before and after receptor activation. We successfully detected coevolutionary relations between the proteins, predicting novel partners and clusters of interacting molecules. We conclude that AutoCoEv can be used to predict protein interaction from large datasets of hundreds, and with aid of super-computing recourses even thousands of proteins in a time and cost-efficient manner.