ArticleProtein science : a publication of the Protein Society2016
High-throughput identification of protein mutant stability computed from a double mutant fitness landscape.
Article in Protein science : a publication of the Protein Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis.The journal of physical chemistry. B · 2024Article
- Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs.bioRxiv : the preprint server for biology · 2023Article
- funtrp: identifying protein positions for variation driven functional tuning.Nucleic acids research · 2019Article
- Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function.Molecular biology and evolution · 2018Article
- Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas.Human genetics · 2018Review
- Inferring the shape of global epistasis.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease.Molecular biology and evolution · 2017Article
- Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.Current opinion in structural biology · 2017Review
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3 authors.
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Abstract
The effect of a mutation on protein stability is traditionally measured by genetic construction, expression, purification, and physical analysis using low-throughput methods. This process is tedious and limits the number of mutants able to be examined in a single study. In contrast, functional fitness effects can be measured in a high-throughput manner by various deep mutational scanning tools. Using protein GB 1, we have recently demonstrated the feasibility of estimating the mutational stability effect ( ΔΔG) of single-substitution based on the functional fitness profile of all double-substitutions. The principle is to identify genetic backgrounds that have an exhausted stability margin. The functional effect of an additional substitution on these genetic backgrounds can then be used to compute the mutational ΔΔG based on the biophysical relationship between functional fitness and thermodynamic stability. However, to identify such genetic backgrounds, the approach described in our previous study required a benchmark dataset, which is a set of known mutational ΔΔG. In this study, a benchmark-independent approach is developed. The genetic backgrounds of interest are identified using k-means clustering with the integration of structural information. We further demonstrated that a reasonable approximation of ΔΔG can also be obtained without taking structural information into account. In summary, this study describes a novel method for computing ΔΔG from double-substitution functional fitness profiles alone, without relying on any known mutational ΔΔG as a benchmark.
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