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 journalArticlepeer-review

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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