ArticleScientific reports2016
Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of the activity, stability, and mutational characteristics of temperature-adapted enzymes.Bioscience reports · 2021Pooled it
- Substrate-Specific Inactivation of Human AAG by Tumor-Associated Single Nucleotide Polymorphic Variants.International journal of molecular sciences · 2026Article
- TP53 CpG Site Mutations Predict the Survival of First-Line Chemoimmunotherapy in Advanced Lung Adenocarcinoma.Thoracic cancer · 2026Observational
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- Integrated computation-guided thermostabilization of the fungal sweet protein Honey Truffle Sweetener.Current research in food science · 2026Article
- Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- SOuLMuSiC, a novel tool for predicting the impact of mutations on protein solubility.Scientific reports · 2025Article
- Prediction and design of thermostable proteins with a desired melting temperature.Scientific reports · 2025Article
- Role of charges in a dynamic disordered complex between an IDP and a folded domain.Nature communications · 2025Article
- Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges.Human genetics · 2025Article
- Integrative analysis of KCNQ1 variants reveals molecular mechanisms of type 1 long QT syndrome pathogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.Database : the journal of biological databases and curation · 2025Article
- Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations.Molecules (Basel, Switzerland) · 2025Article
- Structural Dynamics of Neutral Amino Acid Transporter SLC6A19 in Simple and Complex Lipid Bilayers.Journal of cellular biochemistry · 2025Article
- Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations.Nature genetics · 2025Article
- In situ characterization of amine-forming enzymes shows altered oligomeric state.Protein science : a publication of the Protein Society · 2025Article
- PON-Tm: A Sequence-Based Method for Prediction of Missense Mutation Effects on Protein Thermal Stability Changes.International journal of molecular sciences · 2024Article
- Solvent concentration at 50% protein unfolding may reform enzyme stability ranking and process window identification.Nature communications · 2024Article
- High-throughput system for the thermostability analysis of proteins.Protein science : a publication of the Protein Society · 2024Article
- Cancer-Related Mutations in the Sam Domains of EphA2 Receptor and Ship2 Lipid Phosphatase: A Computational Study.Molecules (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔTm upon point mutations, given the protein structure and, when available, the melting temperature Tm of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔTm. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔTm values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.
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