Automated cryo-EM structure refinement using correlation-driven molecular dynamics

TY - JOUR

T1 - Automated cryo-EM structure refinement using correlation-driven molecular dynamics

AU - Igaev, Maxim

AU - Kutzner, Carsten

AU - Bock, Lars V.

AU - Vaiana, Andrea C.

AU - Grubmüller, Helmut

N1 - Publisher Copyright: © Igaev et al.

PY - 2019/3/4

Y1 - 2019/3/4

N2 - We present a correlation-driven molecular dynamics (CDMD) method for automated refinement of atomistic models into cryo-electron microscopy (cryo-EM) maps at resolutions ranging from near-atomic to subnanometer. It utilizes a chemically accurate force field and thermodynamic sampling to improve the real-space correlation between the modeled structure and the cryo-EM map. Our framework employs a gradual increase in resolution and map-model agreement as well as simulated annealing, and allows fully automated refinement without manual intervention or any additional rotamer- and backbone-specific restraints. Using multiple challenging systems covering a wide range of map resolutions, system sizes, starting model geometries and distances from the target state, we assess the quality of generated models in terms of both model accuracy and potential of overfitting. To provide an objective comparison, we apply several well- established methods across all examples and demonstrate that CDMD performs best in most cases.

AB - We present a correlation-driven molecular dynamics (CDMD) method for automated refinement of atomistic models into cryo-electron microscopy (cryo-EM) maps at resolutions ranging from near-atomic to subnanometer. It utilizes a chemically accurate force field and thermodynamic sampling to improve the real-space correlation between the modeled structure and the cryo-EM map. Our framework employs a gradual increase in resolution and map-model agreement as well as simulated annealing, and allows fully automated refinement without manual intervention or any additional rotamer- and backbone-specific restraints. Using multiple challenging systems covering a wide range of map resolutions, system sizes, starting model geometries and distances from the target state, we assess the quality of generated models in terms of both model accuracy and potential of overfitting. To provide an objective comparison, we apply several well- established methods across all examples and demonstrate that CDMD performs best in most cases.

UR - https://www.scopus.com/pages/publications/85063263810

U2 - 10.7554/eLife.43542

DO - 10.7554/eLife.43542

M3 - Article

C2 - 30829573

AN - SCOPUS:85063263810

SN - 2050-084X

VL - 8

JO - eLife

JF - eLife

M1 - e43542

ER -