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MAVISp: A modular structure-based framework for protein variant effects

Matteo Arnaudi, Mattia Utichi, Kristine Degn, Matteo Tiberti, Ludovica Beltrame, Karolina Krzesińska, Pablo Sánchez Izquierdo Besora, Eleni Kiachaki, Simone Scrima, Laura Bauer, Katrine Meldgård, Anna Melidi, Lorenzo Favaro, Anu Oswal, Guglielmo Tedeschi, Terézia Dorčaková, Alberte Heering Estad, Joachim Breitenstein, Jordan Safer, Paraskevi SaridakiValentina Sora, Francesca Maselli, Philipp Becker, Jérémy Vinhas, Alberto Pettenella, Matteo Lambrughi, Claudia Cava, Anna Rohlin, Mef Nilbert, Sumaiya Iqbal, Peter Wad Sackett, Burcu Aykac Fas, Elena Papaleo*

*Corresponding author for this work
1 Citation (Scopus)

Abstract

The role of genomic variants in disease has expanded significantly with the advent of advanced sequencing techniques. The rapid increase in identified genomic variants has led to many variants being classified as Variants of Uncertain Significance or as having conflicting evidence, posing challenges for their interpretation and characterization. Additionally, current methods for predicting pathogenic variants often lack insights into the underlying molecular mechanisms. Here, we introduce MAVISp (Multi-layered Assessment of VarIants by Structure for proteins), a modular structural framework for variant effects, accompanied by a web server (https://services.healthtech.dtu.dk/services/MAVISp-1.0/) to enhance data accessibility, consultation, and re-usability. MAVISp currently provides data on over 1000 proteins, encompassing more than 10 million variants. A team of biocurators regularly analyzes and updates protein entries using standardized workflows, incorporating free-energy calculations and biomolecular simulations. We illustrate the utility of MAVISp through selected case studies. The framework facilitates the analysis of variant effects at the protein level and has the potential to advance the understanding and application of mutational data in disease research.

Original languageEnglish
Article numbere70548
JournalProtein Science
Volume35
Issue number5
Number of pages29
ISSN0961-8368
DOIs
Publication statusPublished - May 2026

Keywords

  • cancer genomics
  • free energy calculations
  • long-range structural communication
  • protein function
  • protein stability
  • protein structures
  • variant effects

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