sp5 improving protein fold recognition by using torsion angle profiles and profile-based gap penalty modelsp5改善蛋白质折叠识别利用扭转角的概要文件和点球profile-based差距模型.pdf

5 SP : Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model 1,2 3,4,5 1 Wei Zhang , Song Liu , Yaoqi Zhou * 1 Indiana University School of Informatics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America, 2 Institute of Applied Physics and Computational Mathematics, Beijing, People’s Republic of China, 3 Department of Biostatistics, Center of Excellence in Bioinformatics Life Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States of America, 4 Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, New York, United States of America, 5 Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology Biophysics, University at Buffalo, State University of New York, Buffalo, New York, United States of America Abstract How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2 3 4 , SP , SP ) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP metho