ArticleProteins2012
The linear interaction energy method for the prediction of protein stability changes upon mutation.
Article in Proteins, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning.ACS catalysis · 2023Article
- Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.Protein science : a publication of the Protein Society · 2020Article
- Impact of Rett Syndrome Mutations on MeCP2 MBD Stability.Biochemistry · 2015Article
- Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics.International journal of molecular sciences · 2014Review
- Modeling catalytic promiscuity in the alkaline phosphatase superfamily.Physical chemistry chemical physics : PCCP · 2013Article
- Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study.Journal of chemical theory and computation · 2013Article
- Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process.Journal of chemical theory and computation · 2013Article
- Structural dynamics flexibility informs function and evolution at a proteome scale.Evolutionary applications · 2013Article
- Computational protein engineering: bridging the gap between rational design and laboratory evolution.International journal of molecular sciences · 2012Review
- A rational free energy-based approach to understanding and targeting disease-causing missense mutations.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.
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Registered trials
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