Protein threading by learning
arXiv:cond-mat/0110198 · doi:10.1073/pnas.241133698
Abstract
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.
14 pages, 4 figures. Accepted to Proc. Nat. Aca. Sci