LEAP: Highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains

Shide Liang, Chi Zhang, Yaoqi Zhou

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org.

Original languageEnglish (US)
Pages (from-to)335-341
Number of pages7
JournalJournal of Computational Chemistry
Volume35
Issue number4
DOIs
StatePublished - Feb 5 2014

Keywords

  • loop modeling • coarse-grained energy function • energy minimization • Monte Carlo simulation • force field development

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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