Alignment-based Protein Mutational Landscape Prediction: Doing More with Less.
Genome Biol Evol
; 15(11)2023 Nov 01.
Article
in En
| MEDLINE
| ID: mdl-37936309
ABSTRACT
The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteins
/
Computational Biology
Limits:
Humans
Language:
En
Journal:
Genome Biol Evol
Journal subject:
BIOLOGIA
/
BIOLOGIA MOLECULAR
Year:
2023
Document type:
Article
Affiliation country:
France