High-accuracy refinement using Rosetta in CASP13

Hahnbeom Park, Gyu Rie Lee, David E. Kim, Ivan Anishchenko, Qian Cong, David Baker

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Because proteins generally fold to their lowest free energy states, energy-guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co-evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT-HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone root-mean-square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.

Original languageEnglish (US)
Pages (from-to)1276-1282
Number of pages7
JournalProteins: Structure, Function and Bioinformatics
Volume87
Issue number12
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

Keywords

  • energy function
  • homology modeling
  • protein conformational search
  • rotein structure prediction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology

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