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Motivation: A basic question in protein science is to which
extent mutations affect protein thermostability. This knowledge
would be particularly relevant for engineering thermostable
enzymes. In several experimental approaches, this issue has
been serendipitously addressed. It would be therefore convenient
providing a computational method that predicts when a given protein
mutant is more thermostable than its corresponding wild-type.
Results: We present a new method based on support vector
machines that is able to predict whether a set of mutations (including
insertion and deletions) can enhance the thermostability of a given
protein sequence. When trained and tested on a redundancy reduced
dataset, our predictor achieves 88% accuracy and a
correlation coefficient equal to 0.75. Our predictor also correctly
classifies 12 out of 14 experimentally characterized protein mutants
with enhanced thermostability. Finally, it correctly detects all the 11
mutated proteins whose increase in stability temperature is >100◦C.
Availability: The dataset and the list of protein clusters adopted
for the SVM cross-validation are available at the web site
http://lipid.biocomp.unibo.it//∼ludovica/thermo-meso-MUT
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