Improved crystallographic structures using extensive combinatorial refinement.
Identifying errors and alternate conformers and modeling multiple main-chain conformers in poorly ordered regions are overarching problems in crystallographic structure determination that have limited automation efforts and structure quality. Here, we show that implementation of a full factorial designed set of standard refinement approaches, termed ExCoR (Extensive Combinatorial Refinement), significantly improves structural models compared to the traditional linear tree approach, in which individual algorithms are tested linearly and are only incorporated if the model improves. ExCoR markedly improved maps and models and reveals building errors and alternate conformations that were masked by traditional refinement approaches. Surprisingly, an individual algorithm that renders a model worse in isolation could still be necessary to produce the best overall model, suggesting that model distortion allows escape from local minima of optimization target function, here shown to be a hallmark limitation of the traditional approach. ExCoR thus provides a simple approach to improving structure determination.
Department of Cancer Biology, The Scripps Research Institute, 130 Scripps Way, Jupiter, FL 33458, USA.
Jerome C
JC
Nwachukwu
Mark R
MR
Southern
James R
JR
Kiefer
Pavel V
PV
Afonine
Paul D
PD
Adams
Thomas C
TC
Terwilliger
Kendall W
KW
Nettles
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D018360:Crystallography, X-Ray / Q000379:methods; D047628:Estrogen Receptor alpha / Q000737:chemistry; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D012984:Software
D047628:Estrogen Receptor alpha