Automation and assessment of de novo modeling with Pathwalking in near atomic resolution cryoEM density maps.

With the rapidly growing number of macromolecular structures solved to near-atomic resolution using electron cryomicroscopy (cryoEM), map interpretation and model building directly from the density without the use of structural templates has become increasingly important. As part of the 2015/2016 Map and Model Challenge, we attempted to assess our latest de novo modeling tool, Pathwalking, in terms of performance and usability, as well as identify areas for future improvements. In total, we applied Pathwalking to six density maps between 3 and 4.5 Å resolution selected from the challenge data sets. In five of the six cases, Pathwalking was able to accurately determine the protein fold and in three of these cases, the final all atom model had less than 1.6 Å RMSD when compared to the known structure. Model building and refinement was nearly completely automated, used default parameters and took less than 30 min to complete a refined all atom model. A direct outgrowth of this work was a more streamlined automated command line Pathwalking utility, as well as a novel sequence assignment and optimization routine, which attempts to register sidechain density with expected side chain volume. In total, Pathwalking offers a nearly complete, robust and efficient method for constructing atomistic protein structures directly from a density map without the aid of a template.

[1]  A. Bartesaghi,et al.  2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor , 2015, Science.

[2]  M. Baker,et al.  Refinement of protein structures by iterative comparative modeling and CryoEM density fitting. , 2006, Journal of molecular biology.

[3]  Wah Chiu,et al.  Constructing and validating initial Cα models from subnanometer resolution density maps with pathwalking. , 2012, Structure.

[4]  Mindy I. Davis,et al.  Breaking Cryo-EM Resolution Barriers to Facilitate Drug Discovery , 2016, Cell.

[5]  Pierre Tufféry,et al.  SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace , 2006, Nucleic Acids Res..

[6]  D. Agard,et al.  Electron counting and beam-induced motion correction enable near atomic resolution single particle cryoEM , 2013, Nature Methods.

[7]  Frank DiMaio,et al.  RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps , 2017, Nature Methods.

[8]  Thomas D. Goddard,et al.  Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions. , 2010, Journal of structural biology.

[9]  Matthew L. Baker,et al.  An atomic model of brome mosaic virus using direct electron detection and real-space optimization , 2014, Nature Communications.

[10]  D. Kihara,et al.  Protein structure model refinement in CASP12 using short and long molecular dynamics simulations in implicit solvent , 2018, Proteins.

[11]  Matthew L. Baker,et al.  Computing a family of skeletons of volumetric models for shape description , 2007, Comput. Aided Des..

[12]  Keren Lasker,et al.  Finding the right fit: chiseling structures out of cryo-electron microscopy maps. , 2014, Current opinion in structural biology.

[13]  Matthew L. Baker,et al.  Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps. , 2012, Biopolymers.

[14]  Sriram Subramaniam,et al.  Structure of β-galactosidase at 3.2-Å resolution obtained by cryo-electron microscopy , 2014, Proceedings of the National Academy of Sciences.

[15]  Wei Zhang,et al.  Combining X-Ray Crystallography and Electron Microscopy , 2005, Structure.

[16]  D. Julius,et al.  Structure of the TRPV1 ion channel determined by electron cryo-microscopy , 2013, Nature.

[17]  D. Baker,et al.  Refinement of protein structures into low-resolution density maps using rosetta. , 2009, Journal of molecular biology.

[18]  Muyuan Chen,et al.  De Novo modeling in cryo-EM density maps with Pathwalking. , 2016, Journal of structural biology.

[19]  Conrad C. Huang,et al.  Visualizing density maps with UCSF Chimera. , 2007, Journal of structural biology.

[20]  Muyuan Chen,et al.  High resolution single particle refinement in EMAN2.1. , 2016, Methods.

[21]  A. Cheng,et al.  2.8 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome using cryo-electron microscopy , 2015, eLife.

[22]  Yang Zhang,et al.  REMO: A new protocol to refine full atomic protein models from C‐alpha traces by optimizing hydrogen‐bonding networks , 2009, Proteins.

[23]  Guanghui Yang,et al.  Sampling the conformational space of the catalytic subunit of human γ-secretase , 2015, bioRxiv.

[24]  Zaida Luthey-Schulten,et al.  CryoEM-based hybrid modeling approaches for structure determination. , 2018, Current opinion in microbiology.

[25]  Keld Helsgaun,et al.  An effective implementation of the Lin-Kernighan traveling salesman heuristic , 2000, Eur. J. Oper. Res..

[26]  F. Dimaio,et al.  Analyses of subnanometer resolution cryo-EM density maps. , 2010, Methods in enzymology.

[27]  Bartek Wilczynski,et al.  Biopython: freely available Python tools for computational molecular biology and bioinformatics , 2009, Bioinform..

[28]  Tanmay A.M. Bharat,et al.  Seeing tobacco mosaic virus through direct electron detectors , 2015, Journal of structural biology.

[29]  Wah Chiu,et al.  Evaluation system and web infrastructure for the second cryo-EM model challenge. , 2018, Journal of structural biology.

[30]  P. Zwart,et al.  Towards automated crystallographic structure refinement with phenix.refine , 2012, Acta crystallographica. Section D, Biological crystallography.

[31]  Wen Jiang,et al.  Validated near-atomic resolution structure of bacteriophage epsilon15 derived from cryo-EM and modeling , 2013, Proceedings of the National Academy of Sciences.