Feature-based multiple models improve classification of mutation-induced stability changes.

ABSTRACT:

Background

Reliable prediction of stability changes in protein variants is an important aspect of computational protein design. A number of machine learning methods that allow a classification of stability changes knowing only the sequence of the protein emerged. However, their performance on amino acid substitutions of previously unseen non-homologous proteins is rather limited. Moreover, the performance varies for different types of mutations based on the secondary structure or accessible surface area of the mutation site.

Results

We proposed feature-based multiple models with each model designed for a specific type of mutations. The new method is composed of five models trained for mutations in exposed, buried, helical, sheet, and coil residues. The classification of a mu

SUBMITTER: Folkman L 

PROVIDER: S-EPMC4083411 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature