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Published March 2018 | Published
Journal Article Open

A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model


Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F_1-ATPase data set and a 4.5 Å resolution SecYEG–SecA complex data set. All of the models were automatically built by the method to R_(free) values of between 28.9 and 39.9% and were free from the initial model bias.

Additional Information

© 2018 The Author(s). This licence is identical to the Creative Commons Attribution (CC-BY) Licence. Edited by J. L. Smith, University of Michigan, USA (Received 15 September 2017; accepted 15 December 2017; online 25 January 2018) We thank Gwyndaf Evans, Eugene Krissinel, Ruslan Sanishvili and Piotr Sliz for critically reading the manuscript. For the diffraction data collection from crystals of F1-ATPase and AAA-ATPase we gratefully acknowledge the provision of beam time on beamlines ID29 and ID14-4 through the ESRF in-house research programme. ARC and AH acknowledge Jens Kaiser and the scientific staff of SSRL beamline 12-2 for their support with X-ray diffraction measurements. The operations at SSRL are supported by the Department of Energy and the National Institutes of Health. Author contributions are as follows. PS and NSP designed the research and analyzed the results. HO, GAL and SL are responsible for data set 1, ARC and AH for data set 2, DA and GS for the GPCR ECR–Mb complex data set, AAM for the AAA-ATPase data set, and MWB, GAL, SM and CM-D for the F1-ATPase data set. All authors wrote the manuscript. PS and NSP thank Toegepaste en Technische Wetenschappen (TTW–NWO) for funding this work (STW grant 13337). The Molecular Observatory at the California Institute of Technology is supported by the Gordon and Betty Moore Foundation, the Beckman Institute and the Sanofi–Aventis Bioengineering Research Program. The research in the Hoelz laboratory was supported by a grant from the National Institutes of Health (R01-GM117360). AH is a Faculty Scholar of the Howard Hughes Medical Institute, an inaugural Principal Investigator of the Heritage Medical Research Institute for the Advancement of Medicine and Science at Caltech, and was supported by the Albert Wyrick V Scholar Award of the V Foundation for Cancer Research, a Kimmel Scholar Award of the Sidney Kimmel Foundation for Cancer Research and a Teacher–Scholar Award of the Camille and Henry Dreyfus Foundation. Data set 2 was solved at the CCP4/APS school for Macromolecular Crystallography (2016) funded by CCP4 and the Science and Technology Facility Council, the National Cancer Institute (AGM-12006) and the National Institute of General Medical Science (ACB-12002).

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August 21, 2023
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