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Protein Loop Structure Prediction Using Conformational Space Annealing

Cited 9 time in Web of Science Cited 11 time in Scopus

Heo, Seung Ryong; Lee, Ju Yong; Joo, Kee Hyoung; Shin, Hang Cheol; Lee, Joo Young

Issue Date
American Chemical Society
Journal of Chemical Information and Modeling, Vol.57 No.5, pp.1068-1078
We have developed a protein loop structure prediction method by combining a new energy function, which we call E-PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E-PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E-PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E-PLM outperformed E-DFIRE for both decoy sets. This new approach equipped with E-PLM and CSA can serve as the state-of-the-art de novo loop modeling method.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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