SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map |
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| Authors: | Yufeng Cai Xiongjun Li Zhe Sun Yutong Lu Huiying Zhao Jack Hanson Kuldip Paliwal Thomas Litfin Yaoqi Zhou Yuedong Yang |
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| Affiliation: | 1. School of Data and Computer Science, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006 China;2. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510000 China;3. Signal Processing Laboratory, Griffith University, Brisbane, Queensland, 4122 Australia;4. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, 4222 Australia |
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| Abstract: | Protein structure determination has long been one of the most challenging problems in molecular biology for the past 60 years. Here we present an ab initio protein tertiary-structure prediction method assisted by predicted contact maps from SPOT-Contact and predicted dihedral angles from SPIDER 3. These predicted properties were then fed to the crystallography and NMR system (CNS) for restrained structure modeling. The resulted structures are first evaluated by the potential energy calculated by CNS, followed by dDFIRE energy function for model selections. The method called SPOT-Fold has been tested on 241 CASP targets between 67 and 670 amino acid residues, 60 randomly selected globular proteins under 100 amino acids. The method has a comparable accuracy to other contact-map-based modeling techniques. © 2019 Wiley Periodicals, Inc. |
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| Keywords: | protein structure prediction template-free modeling molecular dynamics contact map deep learning |
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