Premium
High‐accuracy refinement using Rosetta in CASP13
Author(s) -
Park Hahnbeom,
Lee Gyu Rie,
Kim David E.,
Anishchenko Ivan,
Cong Qian,
Baker David
Publication year - 2019
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25784
Subject(s) - curse of dimensionality , computer science , casp , protein structure prediction , function (biology) , energy landscape , energy (signal processing) , root mean square , quality (philosophy) , algorithm , data mining , protein structure , biological system , machine learning , mathematics , chemistry , physics , statistics , biology , biochemistry , quantum mechanics , evolutionary biology
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.