Protein threading by learning.

ABSTRACT: By using techniques borrowed from statistical physics and neural networks, we determine the parameters, associated with a scoring function, that are chosen optimally to ensure complete success in threading tests in a training set of proteins. These parameters provide a quantitative measure of the propensities of amino acids to be buried or exposed and to be in a given secondary structure and are a good starting point for solving both the threading and design problems.

SUBMITTER: Chang I 

PROVIDER: S-EPMC64685 | biostudies-literature | 2001 Dec

REPOSITORIES: biostudies-literature