Back

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

Arnaudi, M.; Beltrame, L.; Degn, K.; Utichi, M.; Scrima, S.; Sackett, P. W.; Lambrughi, M.; Tiberti, M.; Papaleo, E.

2022-10-24 bioinformatics
10.1101/2022.10.22.513328 bioRxiv
Show 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 reusability. MAVISp currently provides data over 1000 proteins, encompassing more than eight million variants. A team of biocurators regularly analyzes and updates protein entries using standardized workflows, incorporating free energy calculations or 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.

Published in Protein Science (predicted rank #9) · training set

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nucleic Acids Research
◐ 1281 papers in training set
Top 1%
12.1%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 17%
10.4%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 7%
7.7%
Similar papers in this journal
4
Communications Biology
○ 993 papers in training set
Top 1.0%
6.6%
Similar papers in this journal
5
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 0.4%
6.6%
Similar papers in this journal
6
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 0.8%
6.1%
7
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
4.7%
50% of probability mass above
8
International Journal of Molecular Sciences
○ 494 papers in training set
Top 2%
4.7%
Protein Science · published here
○ 246 papers in training set
Top 1%
4.2%
10
Proteins: Structure, Function, and Bioinformatics
○ 88 papers in training set
Top 0.3%
3.9%
11
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.1%
12
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.4%
13
Nature Methods
○ 385 papers in training set
Top 4%
1.7%
14
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.5%
15
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 0.8%
1.5%
16
Journal of Molecular Biology
○ 232 papers in training set
Top 2%
1.5%
17
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.4%
18
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.3%
19
iScience
○ 1154 papers in training set
Top 28%
1.1%
20
eLife
● 5828 papers in training set
Top 61%
1.1%
21
Structure
○ 193 papers in training set
Top 2%
1.0%
22
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 3%
0.8%
23
Advanced Science
○ 286 papers in training set
Top 10%
0.8%
24
BMC Bioinformatics
○ 457 papers in training set
Top 6%
0.8%
25
Journal of Proteome Research
● 234 papers in training set
Top 2%
0.8%
26
Nature Computational Science
○ 55 papers in training set
Top 2%
0.8%
27
Life Science Alliance
● 285 papers in training set
Top 8%
0.8%
28
Molecular Biology and Evolution
◐ 542 papers in training set
Top 6%
0.6%
29
Journal of Cheminformatics
○ 29 papers in training set
Top 0.8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.