  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.
