T L Chiu1, R A Goldstein. 1. Department of Chemistry, University of Michigan, Ann Arbor, MI 48109-1055, USA.
Abstract
BACKGROUND: Success in solving the protein structure prediction problem relies on the choice of an accurate potential energy function. for a single protein sequence, it has been shown that the potential energy function can be optimized for predictive success by maximizing the energy gap between the correct structure and the ensemble of random structures relative to the distribution of the energies of these random structures (the Z-score). Different methods have been described for implementing this procedure for an ensemble of database proteins. Here, we demonstrate a new approach. RESULTS: For a single protein sequence, the probability of success (i.e the probability that the folded state is the lowest energy state) is derived. We then maximize the average probability of success for a set of proteins to obtain the optimal potential energy function. This results in maximum attention being focused on the proteins whose structures are difficult but not impossible to predict. CONCLUSIONS: Using a lattice model of proteins, we show that the optimal interaction potentials obtained by our method are both more accurate and more likely to produce successful predictions than those obtained by other averaging procedures.
BACKGROUND: Success in solving the protein structure prediction problem relies on the choice of an accurate potential energy function. for a single protein sequence, it has been shown that the potential energy function can be optimized for predictive success by maximizing the energy gap between the correct structure and the ensemble of random structures relative to the distribution of the energies of these random structures (the Z-score). Different methods have been described for implementing this procedure for an ensemble of database proteins. Here, we demonstrate a new approach. RESULTS: For a single protein sequence, the probability of success (i.e the probability that the folded state is the lowest energy state) is derived. We then maximize the average probability of success for a set of proteins to obtain the optimal potential energy function. This results in maximum attention being focused on the proteins whose structures are difficult but not impossible to predict. CONCLUSIONS: Using a lattice model of proteins, we show that the optimal interaction potentials obtained by our method are both more accurate and more likely to produce successful predictions than those obtained by other averaging procedures.