Modeling discrete heterogeneity in X-ray diffraction data by fitting multi-conformers.
The native state of a protein is regarded to be an ensemble of conformers, which allows association with binding partners. While some of this structural heterogeneity is retained upon crystallization, reliably extracting heterogeneous features from diffraction data has remained a challenge. In this study, a new algorithm for the automatic modelling of discrete heterogeneity is presented. At high resolution, the authors' single multi-conformer model, with correlated structural features to represent heterogeneity, shows improved agreement with the diffraction data compared with a single-conformer model. The model appears to be representative of the set of structures present in the crystal. In contrast, below 2 A resolution representing ambiguous electron density by correlated multi-conformers in a single model does not yield better agreement with the experimental data. Consistent with previous studies, this suggests that variability in multi-conformer models at lower resolution levels reflects uncertainty more than coordinated motion.
van den Bedem
Henry
H
Joint Center for Structural Genomics, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.
Dhanik
Ankur
A
Latombe
Jean Claude
JC
Deacon
Ashley M
AM
D000465:Algorithms; D003198:Computer Simulation; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D014961:X-Ray Diffraction
D011506:Proteins