ArticleThe Journal of biological chemistry2017
Computational tools help improve protein stability but with a solubility tradeoff.
Article in The Journal of biological chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of SKA2 on affective disorder, post-traumatic stress disorder and suicide behavior: systematic review and in silico analysis.Metabolic brain disease · 2024Pooled it
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.Scientific reports · 2026Article
- Toward a systems metabolic engineering approach to improve thermotolerance of Escherichia coli.Microbial cell factories · 2026Review
- Computational optimization of DEK1 calpain domain solubility through integrated structural modelling and data-driven targeted mutagenesis.Scientific reports · 2026Article
- Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut.Protein science : a publication of the Protein Society · 2025Article
- Decoding Protein Stabilization: Impact on Aggregation, Solubility, and Unfolding Mechanisms.Journal of chemical information and modeling · 2025Article
- SOuLMuSiC, a novel tool for predicting the impact of mutations on protein solubility.Scientific reports · 2025Article
- FoldX force field revisited, an improved version.Bioinformatics (Oxford, England) · 2025Article
- Improving the production and stability of nanobodies.Protein engineering, design & selection : PEDS · 2025Article
- ESM-scan-A tool to guide amino acid substitutions.Protein science : a publication of the Protein Society · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Rheostatic contributions to protein stability can obscure a position's functional role.Protein science : a publication of the Protein Society · 2024Article
- Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect.Scientific reports · 2024Article
- Automated optimization of the solubility of a hyper-stable α-amylase.Open biology · 2024Article
- Modeling and Analysis of HIV-1 Pol Polyprotein as a Case Study for Predicting Large Polyprotein Structures.International journal of molecular sciences · 2024Article
- Article
- FireProt 2.0: web-based platform for the fully automated design of thermostable proteins.Briefings in bioinformatics · 2023Article
- Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability.BMC bioinformatics · 2023Article
- High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST.Journal of the American Chemical Society · 2023Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.
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