Awoniyi, L. O. | bioRxiv Science

bioRxiv2020

APEX2 proximity biotinylation reveals protein dynamics triggered by B cell receptor activation

Successful B cell activation, critical for high-affinity antibody production, is controlled by the B cell antigen receptor (BCR). However, we still lack a comprehensive protein-level view of the very dynamic multi-branched cellular events triggered by antigen binding. Here, we employed APEX2 proximity biotinylation to study antigen-induced changes, 5-15 min after receptor activation, at the vicinity of the plasma membrane lipid rafts, wherein BCR enriches upon activation. The data reveals dynamics of signaling proteins, as well as various players linked to the subsequent processes, such as actin cytoskeleton remodelling and endocytosis. Interestingly, our differential expression analysis identified dynamic responses in various proteins previously not linked to early B cell activation. We demonstrate active SUMOylation at the sites of BCR activation in various conditions and report its functional role in BCR signaling through Akt and MAPK axes.

immunology↗

bioRxiv2020

AutoCoEv: a high-throughput in silico pipeline for revealing novel protein-protein interactions

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.

bioinformatics↗