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.
@article{arxiv.cond-mat/0110198,
title = {Protein threading by learning},
author = {Iksoo Chang and Marek Cieplak and Ruxandra I. Dima and Amos Maritan and Jayanth R. Banavar},
journal= {arXiv preprint arXiv:cond-mat/0110198},
year = {2009}
}
Comments
14 pages, 4 figures. Accepted to Proc. Nat. Aca. Sci