Prediction of Protein Tertiary Structures Using MUFOLD
There have been steady improvements in protein structure prediction during the past two decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. To address this challenge, we developed MUFOLD, a hybrid method of using whole and partial template information along with new computational techniques for protein tertiary structure prediction. MUFOLD covers both template-based and ab initio predictions using the same framework and aims to achieve high accuracy and fast computing. Two major novel contributions of MUFOLD are graph-based model generation and molecular dynamics ranking (MDR). By formulating a prediction as a graph realization problem, we apply an efficient optimization approach of Multidimensional Scaling (MDS) to speed up the prediction dramatically. In addition, under this framework, we enhance the predictions consistently by iteratively using the information from generated models. MDR, in contrast to widely used static scoring functions, exploits dynamics properties of structures to evaluate their qualities, which can often identify best structures from a pool more effectively.
Key wordsProtein structure prediction Multidimensional scaling Molecular dynamics simulation
This work has been supported by National Institutes of Health Grant R21/R33-GM078601. Major computing resource was provided by the University of Missouri Bioinformatics Consortium. We like to thank Jianlin Cheng, Yang Zhang, and Joel L. Sussman for helpful discussions.
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