title: Prediction of protein tertiary structures using MUFOLD.
abstract: 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.
authors: Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Jingfen
authors: J
authors: Zhang
authors: Zhiquan
authors: Z
authors: He
authors: Qingguo
authors: Q
authors: Wang
authors: Bogdan
authors: B
authors: Barz
authors: Ioan
authors: I
authors: Kosztin
authors: Yi
authors: Y
authors: Shang
authors: Dong
authors: D
authors: Xu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D056004:Molecular Dynamics Simulation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins