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

Arnaudi, Matteo ; Utichi, Mattia ; Degn, Kristine ; Tiberti, Matteo ; Beltrame, Ludovica ; Krzesińska, Karolina ; Besora, Pablo Sánchez Izquierdo ; Kiachaki, Eleni ; Scrima, Simone and Bauer, Laura , et al. (2026) In Protein Science 35(5).
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,... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
cancer genomics, free energy calculations, long-range structural communication, protein function, protein stability, protein structures, variant effects
in
Protein Science
volume
35
issue
5
article number
e70548
publisher
The Protein Society
external identifiers
  • scopus:105035036203
  • pmid:41944585
ISSN
0961-8368
DOI
10.1002/pro.70548
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2026 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.
id
f7178817-2bbd-4b07-901c-afbe417ca764
date added to LUP
2026-05-20 14:53:31
date last changed
2026-07-31 02:40:06
@article{f7178817-2bbd-4b07-901c-afbe417ca764,
  abstract     = {{<p>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.</p>}},
  author       = {{Arnaudi, Matteo and Utichi, Mattia and Degn, Kristine and Tiberti, Matteo and Beltrame, Ludovica and Krzesińska, Karolina and Besora, Pablo Sánchez Izquierdo and Kiachaki, Eleni and Scrima, Simone and Bauer, Laura and Meldgård, Katrine and Melidi, Anna and Favaro, Lorenzo and Oswal, Anu and Tedeschi, Guglielmo and Dorčaková, Terézia and Estad, Alberte Heering and Breitenstein, Joachim and Safer, Jordan and Saridaki, Paraskevi and Sora, Valentina and Maselli, Francesca and Becker, Philipp and Vinhas, Jérémy and Pettenella, Alberto and Lambrughi, Matteo and Cava, Claudia and Rohlin, Anna and Nilbert, Mef and Iqbal, Sumaiya and Sackett, Peter Wad and Fas, Burcu Aykac and Papaleo, Elena}},
  issn         = {{0961-8368}},
  keywords     = {{cancer genomics; free energy calculations; long-range structural communication; protein function; protein stability; protein structures; variant effects}},
  language     = {{eng}},
  number       = {{5}},
  publisher    = {{The Protein Society}},
  series       = {{Protein Science}},
  title        = {{MAVISp : A modular structure-based framework for protein variant effects}},
  url          = {{http://dx.doi.org/10.1002/pro.70548}},
  doi          = {{10.1002/pro.70548}},
  volume       = {{35}},
  year         = {{2026}},
}