Modelling Thermal Stability Changes Upon Mutations in Proteins with Artificial Neural Networks
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Summary
The importance of local interactions in predicting thermal stability changes is higher in the protein core than on the surface, although the opposite trend is observed for the prediction of thermodynamic stability changes.
- Type
- article
- Published
- 2010-01-01
- Cited by
- 1
- References
- 26
- OpenAlex
- https://openalex.org/W1985778297
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:93296586
Keywords
Sigmoid function, Thermodynamics, Artificial neural network, Correlation coefficient, Mutation
References
- Prediction of protein backbone conformation based on seven structure assignments. Influence of local interactions.
- Contributions of the large hydrophobic amino acids to the stability of staphylococcal nuclease.
- Average assignment method for predicting the stability of protein mutants
- Knowledge-based potentials for proteins.
- Hydrophobicity of amino acid residues in globular proteins.
- Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
- Protein stability for single substitution mutants and the extent of local compactness in the denatured state.
- Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence.
- Simulation analysis of the stability mutant R96H of T4 lysozyme.
- Modeling backbone flexibility improves protein stability estimation.
- Elucidation of factors responsible for enhanced thermal stability of proteins: a structural genomics based study.
- Response of a protein structure to cavity-creating mutations and its relation to the hydrophobic effect.
- A new generation of statistical potentials for proteins.
- Free energy calculations by computer simulation.
- Prediction of the activity and stability effects of site-directed mutagenesis on a protein core.
- Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.
- Prediction of protein stability changes for single‐site mutations using support vector machines
- Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction
- CUPSAT: prediction of protein stability upon point mutations
- Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis
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