MAVISp: A Modular Structure-Based Framework for Protein Variant Effects

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 the response to treatment and risks of relapse in different patients. The role of genomics variants on cancer predisposition, progression, and response to treatment continues to be realized. Thanks to advances in sequencing techniques and their introduction in a clinical setting, 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. Applications of bioinformatic-based approaches to characterize the effects of these variants demonstrated their full potential thanks to advances in machine learning, comparisons between predicted effects and cellular readouts, and advances 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 the coding region of genes and impacting on the corresponding protein product (MAVISp, Multi-layered Assessment of VarIants by Structure for proteins) 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 ten different proteins and more than 4000 variants. New protein targets are routinely analyzed in batches 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. Finally, we provide guidelines for new contributors who are interested in contributing to the collection in relation to their research.

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

“…Experimental studies at the cellular level confirm that the T1131A substitution does not affect the protein levels, in agreement with a neutral effect on the folding ΔΔ G s (Wilkes et al, 2017 ) and that the phenotype reflects a functional impairment that has a mild impact on drug sensitivity and the monoubiquitination of another protein (Adachi et al, 2002 ; Wilkes et al, 2017 ). T1131A could be further investigated using our recently proposed multilayered structural framework for variant annotations in proteins, that is, Multilayered Assessment of VarIants by Structure for proteins(MAVISp) (Arnaudi et al, 2022 ).…”

TRAP1S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins

“…For each dataset of cancer mutations in the candidate driver proteins, we retained the ones located in the surrounding of the cysteines as defined above for further analysis. At first, we evaluated if any of the mutations is expected to alter the structural stability of the protein using a structure-based framework recently proposed by our group 55 ( Methods, Tables S5 ). All those mutations that seem to have a neutral or uncertain effect on the structural stability of the candidate protein are interesting candidates for further analyses in connection with the population shift mechanism proposed here.…”

“…We retrieved the classification for the variants of interest as benign, pathogenic, or variants of unknown significance from ClinVar 58 . We used the classification provided by the STABILITY module of MAVISp to retain for further analyses only variants with unknown or neutral effects, as explained in the results 55 .…”

“…We used the simple mode of the MAVISp framework 55 and retrieved and aggregated the missense mutations for GRIN2A, CBFB, CALR, and EGFR in COSMIC 54 , cBioPortal 92 and ClinVar 57,58 . We verified if the selected target proteins contained intra-membrane regions by querying the Orientations of Proteins in Membranes (OPM) database 93 .…”

Decoding Phospho-Regulation and Flanking Regions in Autophagy-Associated Short Linear Motifs: A Case Study of Optineurin-LC3B Interaction

“…We used the data deposited in the MAVISp database 72 where variants found in COSMIC 73 , cBioPortal 74 , and ClinVar 75 have been retrieved for OPTN (Uniprot ID Q96CV9, and RefSeq ID NP_068815).…”