Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
Open accessPublished version of a preprint
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.
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Cites
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Flagged first, then by citations
- Authors
- Abakarova, M., Marquet, C., Rera, M., Rost, B., & Laine, E.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Technical University of Munich (DE)
- Université Paris Cité , INSERM UMR U1284 , 75004 Paris, France
- CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université , UMR 7238 , Paris 75005, France
- Venue
- Genome Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-11-01
- Volume
- 15
- Issue
- 11
- Pages
- evad201
- ISSN
- 1759-6653 (ISSN-L)
- PMC
- PMC10653582
- OpenAlex
- W4388486405
- Cited by
- 14
- References
- 12
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2012: 1
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome Biology and Evolution (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche (ANR-20-CE44-0010)
- Sorbonne Université
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- missense mutations, protein structure prediction, multiple sequence alignment, MMseqs2, homology search, protein families, human proteome, computational efficiency, phenotype prediction
- MeSH terms
- Humans, Proteins, Sequence Alignment, Computational Biology, Mutation, Missense, Genomics
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