TY - JOUR
T1 - Constructing atomic structural models into cryo-EM densities using molecular dynamics – Pros and cons
AU - Wang, Yuhang
AU - Shekhar, Mrinal
AU - Thifault, Darren
AU - Williams, Christopher J.
AU - McGreevy, Ryan
AU - Richardson, Jane
AU - Singharoy, Abhishek
AU - Tajkhorshid, Emad
N1 - Funding Information:
We thank Andriy Kryshtafovych for helpful suggestions on analyzing the competition data. Emad Tajkhorshid's laboratory is supported by grants P41GM104601 and R01GM098243-02 from the National Institutes of Health. The Richardson laboratory acknowledges NIH grants P01-GM063210 and R01-GM073919 for this work. Abhishek Singharoy acknowledges start-up funds from the School of Molecular Sciences and CASD at Arizona State University, and resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The computational resource was partially supported by the Blue Waters supercomputer (award ACI-1713784 to E.T.) and the Extreme Science and Engineering Discovery Environment (XSEDE) (award TG-MCA06N060 to E.T.). Blue Waters sustained-petascale computing project is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. XSEDE is supported by National Science Foundation (award ACI-1548562).
Funding Information:
We thank Andriy Kryshtafovych for helpful suggestions on analyzing the competition data. Emad Tajkhorshid’s laboratory is supported by grants P41GM104601 and R01GM098243-02 from the National Institutes of Health . The Richardson laboratory acknowledges NIH grants P01-GM063210 and R01-GM073919 for this work. Abhishek Singharoy acknowledges start-up funds from the School of Molecular Sciences and CASD at Arizona State University, and resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory , which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The computational resource was partially supported by the Blue Waters supercomputer (award ACI-1713784 to E.T.) and the Extreme Science and Engineering Discovery Environment (XSEDE) (award TG-MCA06N060 to E.T.). Blue Waters sustained-petascale computing project is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. XSEDE is supported by National Science Foundation (award ACI-1548562).
Publisher Copyright:
© 2018
PY - 2018/11
Y1 - 2018/11
N2 - Accurate structure determination from electron density maps at 3–5 Å resolution necessitates a balance between extensive global and local sampling of atomistic models, yet with the stereochemical correctness of backbone and sidechain geometries. Molecular Dynamics Flexible Fitting (MDFF), particularly through a resolution-exchange scheme, ReMDFF, provides a robust way of achieving this balance for hybrid structure determination. Employing two high-resolution density maps, namely that of β-galactosidase at 3.2 Å and TRPV1 at 3.4 Å we showcase the quality of ReMDFF-generated models, comparing them against ones submitted by independent research groups for the 2015–2016 Cryo-EM Model Challenge. This comparison offers a clear evaluation of ReMDFF's strengths and shortcomings, and those of data-guided real-space refinements in general. ReMDFF results scored highly on the various metric for judging the quality-of-fit and quality-of-model. However, some systematic discrepancies are also noted employing a Molprobity analysis, that are reproducible across multiple competition entries. A space of key refinement parameters is explored within ReMDFF to observe their impact within the final model. Choice of force field parameters and initial model seem to have the most significant impact on ReMDFF model-quality. To this end, very recently developed CHARMM36m force field parameters provide now more refined ReMDFF models than the ones originally submitted to the Cryo-EM challenge. Finally, a set of good-practices is prescribed for the community to benefit from the MDFF developments.
AB - Accurate structure determination from electron density maps at 3–5 Å resolution necessitates a balance between extensive global and local sampling of atomistic models, yet with the stereochemical correctness of backbone and sidechain geometries. Molecular Dynamics Flexible Fitting (MDFF), particularly through a resolution-exchange scheme, ReMDFF, provides a robust way of achieving this balance for hybrid structure determination. Employing two high-resolution density maps, namely that of β-galactosidase at 3.2 Å and TRPV1 at 3.4 Å we showcase the quality of ReMDFF-generated models, comparing them against ones submitted by independent research groups for the 2015–2016 Cryo-EM Model Challenge. This comparison offers a clear evaluation of ReMDFF's strengths and shortcomings, and those of data-guided real-space refinements in general. ReMDFF results scored highly on the various metric for judging the quality-of-fit and quality-of-model. However, some systematic discrepancies are also noted employing a Molprobity analysis, that are reproducible across multiple competition entries. A space of key refinement parameters is explored within ReMDFF to observe their impact within the final model. Choice of force field parameters and initial model seem to have the most significant impact on ReMDFF model-quality. To this end, very recently developed CHARMM36m force field parameters provide now more refined ReMDFF models than the ones originally submitted to the Cryo-EM challenge. Finally, a set of good-practices is prescribed for the community to benefit from the MDFF developments.
KW - Cryo-EM data challenge
KW - Force fields
KW - Hybrid modeling
KW - Molecular dynamics flexible fitting
KW - Resolution-exchange
KW - Structure determination
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U2 - 10.1016/j.jsb.2018.08.003
DO - 10.1016/j.jsb.2018.08.003
M3 - Article
C2 - 30092279
AN - SCOPUS:85051684101
SN - 1047-8477
VL - 204
SP - 319
EP - 328
JO - Journal of Structural Biology
JF - Journal of Structural Biology
IS - 2
ER -