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