ArticleBMC bioinformatics2013
Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Computational approaches for predicting mutant protein stability.Journal of computer-aided molecular design · 2016Review
- Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.PLoS computational biology · 2015Article
- Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics.International journal of molecular sciences · 2014Review
- Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins.BMC genomics · 2014Article
- Feature-based multiple models improve classification of mutation-induced stability changes.BMC genomics · 2014Article
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3 authors.
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Abstract
backgroundEven a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored.
resultsWe proposed a number of evolutionary and predicted structural features for the prediction of stability changes and analysed which of them capture the determinants of protein stability the best. We trained and evaluated our machine learning method on a non-redundant data set of experimentally measured stability changes. When only the direction of the stability change was predicted, we found that the best performance improvement can be achieved by the combination of the evolutionary features mutation likelihood and SIFT score in conjunction with the predicted structural feature secondary structure. The same two evolutionary features in the combination with the predicted structural feature accessible surface area achieved the lowest error when the prediction of actual values of stability changes was assessed. Compared to similar studies, our method achieved improvements in prediction performance.
conclusionAlthough the strongest feature for the prediction of stability changes appears to be the vector of amino acid identities in the sequential neighbourhood of the mutation, the most relevant combination of evolutionary and predicted structural features further improves prediction performance. Even the predicted structural features, which did not perform well on their own, turn out to be beneficial when appropriately combined with evolutionary features. We conclude that a high prediction accuracy can be achieved knowing only the sequence of a protein when the right combination of both structural and evolutionary features is used.
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