Extracting knowledge-based energy functions from protein structures by error rate minimization:Comparison of methods usign lattice model

摘要

We describe a general framework for extracting knowledge-based energy function from a set of native protein structures .In this scheme,the energy funtion is optimal when there is least chance that a random structure has a lower energy than the corresponding native structure. We first show thjat subject to certain approximations, most current database-derived energy functions fall within this framework, including mean-field potentials, Z-score optimization, and constraint satisfaction methods. We then propose a simple method for energy function parametrization derived from our analysis.We go on to compare our method to other methods using a simple lattice model in the context of three different energy function scenarios. We show that our method, which is based on the most stringent criteria, performs best in all cases. The power and limitations of each method for deriving knowledge-based energy function is examined