Improving Resolution and Resolvability of Single Particle CryoEM using Gaussian Mixture Models

Cryogenic electron microscopy is widely used in structural biology, but the resolution it achieves is often limited by the dynamics of the macromolecule. Here, we developed a refinement protocol based on Gaussian mixture models that integrate particle orientation and conformation estimation, and improves the alignment for flexible domains of protein structures. We demonstrated this protocol on multiple datasets, resulting in improved resolution and resolvability by visual and quantitative measures.

[1]  Roy R. Lederman,et al.  Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials , 2023, Nature Communications.

[2]  Roy R. Lederman,et al.  Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models. , 2022, Journal of molecular biology.

[3]  R. Dror,et al.  Selective G protein signaling driven by Substance P-Neurokinin Receptor dynamics , 2021, Nature Chemical Biology.

[4]  David J. Fleet,et al.  3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM , 2021, bioRxiv.

[5]  Ellen D. Zhong,et al.  CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks , 2021, Nature Methods.

[6]  W. Chiu,et al.  Resolving individual atoms of protein complex by cryo-electron microscopy , 2020, Cell Research.

[7]  Ashwin Chari,et al.  Atomic-resolution protein structure determination by cryo-EM , 2020, Nature.

[8]  P. Penczek.  Reliable cryo-EM resolution estimation with modified Fourier shell correlation , 2020, IUCrJ.

[9]  Ralf Bartenschlager,et al.  Structures and distributions of SARS-CoV-2 spike proteins on intact virions , 2020, Nature.

[10]  Takanori Nakane,et al.  Single-particle cryo-EM at atomic resolution , 2020, Nature.

[11]  David J. Fleet,et al.  3D Variability Analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM , 2020, bioRxiv.

[12]  Wah Chiu,et al.  Measurement of atom resolvability in cryo-EM maps with Q-scores , 2020, Nature Methods.

[13]  Yifan Cheng,et al.  Single-particle cryo-EM—How did it get here and where will it go , 2018, Science.

[14]  Guillermo Sapiro,et al.  Atomic Resolution Cryo-EM Structure of β-Galactosidase. , 2018, Structure.

[15]  J. Kowal,et al.  Structural basis of small-molecule inhibition of human multidrug transporter ABCG2 , 2018, Nature Structural & Molecular Biology.

[16]  E. Lindahl,et al.  Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION , 2018, bioRxiv.

[17]  Randy J Read,et al.  Real-space refinement in PHENIX for cryo-EM and crystallography , 2018, bioRxiv.

[18]  Conrad C. Huang,et al.  UCSF ChimeraX: Meeting modern challenges in visualization and analysis , 2018, Protein science : a publication of the Protein Society.

[19]  David J. Fleet,et al.  cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination , 2017, Nature Methods.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Sjors H.W. Scheres,et al.  RELION: Implementation of a Bayesian approach to cryo-EM structure determination , 2012, Journal of structural biology.

[22]  Wen Jiang,et al.  EMAN2: an extensible image processing suite for electron microscopy. , 2007, Journal of structural biology.

[23]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..