ArticleProceedings of the National Academy of Sciences of the United States of America2019
Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.
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Who cites it
104 citing papers in PubMed.
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- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Factors controlling protein evolvability-at the molecular scale.European biophysics journal : EBJ · 2026Article
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- Accurate predictions of protein mutational effects accelerated with a hybrid-topology free energy protocol.Communications chemistry · 2025Article
- Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut.Protein science : a publication of the Protein Society · 2025Article
- SAGE-prot: scoring-assisted generative exploration for multi-objective protein design.Briefings in bioinformatics · 2025Article
- Benchmarking Free Energy Calculations: Analysis of Single and Double Mutations Across Two Simulation Software Platforms for Two Protein Systems.bioRxiv : the preprint server for biology · 2025Article
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dissecting the effects of single amino acid substitutions in SARS-CoV-2 Mpro.Protein science : a publication of the Protein Society · 2025Article
- Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code?Biomolecules · 2025Review
- Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.Scientific reports · 2025Article
- Predicting Thermodynamic Stability at Protein G Sites with Deleterious Mutations Using λ-Dynamics with Competitive Screening.The journal of physical chemistry letters · 2025Article
- Predicting absolute protein folding stability using generative models.Protein science : a publication of the Protein Society · 2025Article
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost.Protein science : a publication of the Protein Society · 2025Article
44 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools.
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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 Socratic graph.