MAVISp: Multi-layered Assessment of VarIants by Structure for proteins

Abstract

Cancer is a complex group of diseases due to the accumulation of mutations in tumor suppressors or oncogenes in the genome. Cancer alterations can be very heterogeneous, even in tumors from the same tissue, affecting cancer predisposition, response to treatment, and risks of relapse in different patients. The role of genomics variants in this context continues to be realized. Thanks to advances in sequencing techniques and their introduction in the clinics, the number of genomic variants discovered is growing exponentially. Many of these variants are classified as Variants of Uncertain Significance (VUS), while other variants have been reported with conflicting evidence or with a ‘likely’ effect. Applications of bioinformatic-based approaches to characterize protein variants demonstrated their full potential thanks to advances in machine learning, comparisons between predicted effects and cellular readouts, and progresses in the field of structural biology and biomolecular simulations. We here introduce a modular structure-based framework for the annotations and classification of the impact of variants affecting proteins or their interactions and impacting on the corresponding protein product (MAVISp,Multi-layeredAssessment ofVarIants byStructure forproteins) together with a Streamlit-based web application (https://github.com/ELELAB/MAVISp) where the variants and the data generated by the assessment are made available to the community for consultation or further studies. Currently, MAVISp includes information for 127 different proteins and approximately 42000 variants. New protein targets are routinely analyzed in batches by biocurators through standardized Python-based workflows and high-throughput free energy and biomolecular simulations. We also illustrate the potential of the approach for each protein included in the database. New variants will be deposited on a regular base or in connection with future publications where the approach will be applied. We also welcome new contributors who are interested in participating to the collection in relation to their research.