| Summary: | 碩士 === 國立交通大學 === 生物資訊研究所 === 93 === One strategy for protein structure prediction is to generate a large number of possible structures (decoys) and select the most fitting ones based on a scoring or energy function. The conformational spaces of a protein are huge, and chances are rare that any heuristically generated structures will directly fall in the neighborhood of the native structure. It is desirable that the unfitting decoy structures can provide insights into native structures, so prediction can be made progressively. In this thesis, we develop two simple energy functions for protein folding and show that their good performance with popular benchmarks. One is MOLSIM, a physics-based energy function; another is GEMSCORE, an empirical energy function based on physical mechanisms with simplified model. Instead of hundreds or thousands parameters used in other physics-based energy functions by previous authors, we adopt only few overall weights and use an evolutionary algorithm to optimize the parameters of these two energy functions. Here we present a systematic comparison of our results with the works based on physics-based energy functions by previous authors. Six testing decoy sets, including 96 protein sequences with more 70,000 structures were evaluated. There are 70 and 73 native proteins that identified from these decoy sets with MOLSIM and GEMSCORE, respectively. We believe that our energy functions are fast and simple to discriminate between native and nonnative structures for protein structure prediction.
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