ArticleGenome biology and evolution2023
Alignment-based Protein Mutational Landscape Prediction: Doing More with Less.
Article in Genome biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- ProteoCast: a web server to predict, validate, and interpret missense variant effects.Journal of molecular biology · 2026Article
- Proteome-wide prediction of the functional impact of missense variants with ProteoCast.Nature communications · 2026Article
- MutationAssessor in cBioPortal.bioRxiv : the preprint server for biology · 2025Article
- Easy and accurate protein structure prediction using ColabFold.Nature protocols · 2025Review
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions.Nature communications · 2024Article
- Expert-guided protein language models enable accurate and blazingly fast fitness prediction.Bioinformatics (Oxford, England) · 2024Article
- Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.Nature communications · 2024Article
- PanEffect: a pan-genome visualization tool for variant effects in maize.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.