Abstract

Models in computational biology, such as those used in binding, docking and folding, are often empirical and have adjustable parameters. Since few of these models are yet fully predictive, the problem may be nonoptimal choices of parameters. We describe an algorithm called ENPOP (ENergy function Parameter OPtimization) that improves, and sometimes optimizes, the parameters for any given model and for any given search strategy that identies the stable state of that model. ENPOP iteratively adjusts the parameters simultaneously to move the model global minimum energy conformation for each of m dierent molecules as close as possible to the true native conformations, based on some appropriate measure of structural error. A proof of principle is given for two very dierent test problems. The rst involves 3 dierent 2-dimensional model protein molecules having 12 or 13 monomers, and 4 parameters in common. The parameters converge to the values used to design the model native structures.

Citations