Protein threading by learning.
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.
Chang
I
I
Department of Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
Cieplak
M
M
Dima
R I
RI
Maritan
A
A
Banavar
J R
JR
D000595:Amino Acid Sequence; D015195:Drug Design; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins