A Molecular Mechanics Knowledge Base Applied to Template Based Structure Prediction
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Predicting protein structure using its primary sequence has always been a challenging topic in biochemistry. Although it seems as simple as finding the minimal energy conformation, it has been quite difficult to provide an accurate yet reliable solution for the problem. On the one hand, the lack of understanding of the hydrophobic effect as well as the relationship between different stabilizing forces, such as hydrophobic interaction, hydrogen bonding and electronic static interaction prevent the scientist from developing potential functions to estimate free energy. On the other hand, structure databases are limited with redundant structures, which represent a noncontinuous, sparsely-sampled conformational space, and preventing the development of a method suitable for high-resolution, high-accuracy structure prediction that can be applied for functional annotation of an unknown protein sequence. Thus, in this study, we use molecular dynamics simulation as a tool to sample conformational space. Structures were generated with physically realistic conformations that represented the properties of ensembles of native structures. First, we focused our study on the relationship among different factors that stabilize protein structure. Using a wellcharacterized mutation system of the B-hairpin, a fundamental building block of protein, we were able to identify the effect of terminal ion-pairs (salt-bridges) on the stability of the beta-hairpin, and its relationship with hydrophobic interactions and hydrogen bonds. In the same study, we also correlated our theoretical simulations qualitatively with experimental results. Such analysis provides us a better understanding of beta-hairpin stability and helps us to improve the protein engineering method to design more stable hairpins. Second, with large-scale simulations of different representative protein folds, we were able to conduct a fine-grained analysis by sampling the continuous conformational space to characterize the relationship among backbone conformation, side-chain conformation and side-chain packing. Such information is valuable for improving high-resolution structure prediction. Last, with this information, we developed a new prediction algorithm using packing information derived from the conserved relative packing groups. Based on its performance in CASP7, we were able to draw the conclusion that our simulated dataset as well as our packing-oriented prediction method are useful for template based structure prediction.