Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants.
BACKGROUND
Even 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.


RESULTS
We 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.


CONCLUSIONS
Although 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.
Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. lukas.folkman@griffithuni.edu.au
Lukas
L
Folkman
Bela
B
Stantic
Abdul
A
Sattar
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D030562:Databases, Protein; D019143:Evolution, Molecular; D016013:Likelihood Functions; D050505:Mutant Proteins / Q000737:chemistry* / Q000235:genetics; D055550:Protein Stability; D017433:Protein Structure, Secondary
D000596:Amino Acids; D050505:Mutant Proteins