ArticleBioinformatics (Oxford, England)2012
Predicting folding free energy changes upon single point mutations.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
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- Exploring Free Energies of Specific Protein Conformations Using the Martini Force Field.Journal of chemical theory and computation · 2024Article
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- Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation.PLoS computational biology · 2022Article
- A guide to machine learning for biologists.Nature reviews. Molecular cell biology · 2022Review
- Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis.Computational and structural biotechnology journal · 2022Article
- LYRUS: a machine learning model for predicting the pathogenicity of missense variants.Bioinformatics advances · 2022Article
- Analysis and Interpretation of the Impact of Missense Variants in Cancer.International journal of molecular sciences · 2021Review
- Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus.BMC bioinformatics · 2021Article
- PremPS: Predicting the impact of missense mutations on protein stability.PLoS computational biology · 2020Article
- Whole GenomeVaccines · 2020Article
- Pyrazinamide resistance of novel mutations inRSC advances · 2020Article
- Computational analysis of androgen receptor (AR) variants to decipher the relationship between protein stability and related-diseases.Scientific reports · 2020Article
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6 authors.
Funding
Abstract
motivationThe folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies.
resultsWe report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters. AVAILABILITY: All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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