Model building and validation for cryo-em maps at low resolution
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1 International Workshop of Advanced Image Processing of Cryo-Electron Microscopy 2015 Tsinghua University, Beijing, China June 3-7, 2015 Model building and validation for cryo-em maps at low resolution Leifu Chang MRC Laboratory of Molecular Biology, Cambridge, UK 5 June 2015
2 Introduction Modelling tools for cryo-em at low resolution Model validation An example
3 Macromolecules are the executors of most cellular processes Ribosome: protein synthesis Spliceosome: RNA splicing Chromatin-remodeling complex Kinetochore: spindlechromatin attachment E2 Substrate Ub Nucleosome Nuclear pore complex DNA polymerase RNA polymerase Chaperones (e.g. GroEL/GroES) Respiratory complexes APC/C: protein ubiquitination Peoteasome: protein degradation
4 Challenges in Structure Determination of Macromolecules X-ray crystallography. - Problem1: low yields of sample; - Problem2: difficult to crystalize due to heterogeneity and flexibility. - Phases/quality of crystals (no clear spots; smear; Anisotropy) Cryo-EM - High resolution normally not achieved in ONE step; - Even if we achieved near-atomic resolution, some regions (quite often in important functional regions) are at low resolution due to flexibility or its location at the periphery of the complex. - preferred orientation; disassociation during freezing procedure - limited microscope time Model building at low resolution is still a general problem in cryo-em field.
5 Resolution: X-ray vs cryo-em PDB Resolution reported in PDB and EMDB EMDB Resolution high low X-ray high Cryo-EM intermediate atomic (near-atomic) helical domain low Å Definition Highest resolvable peak in the diffraction pattern Gold-standard Fourier shell correlation (FSC)
6 Resolution landmarks in EM reconstructions Negative stain (~20 Å) Domain resolution Cryo-EM (~15 Å) Domain resolution Cryo-EM (< 9 Å, here 7.4 Å) Alpha-helices as rods Beta-sheets as planar density Cryo-EM (5-6 Å) Pitches come out from helix Cryo-EM (<4.8 Å) Beta-strands separate Cryo-EM (<4.0 Å) Side-chains become clear
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8 Procedure: cryo-em Sample Preparation (Cloning/Expression/Purification) EM grid (Cryo or negative stain) 2D images in micrographs Particles 2D averages Validation EM map Initial model Refine Converged ~3-40Å Interpretation and Model Validation Publish
9 Modelling/Interpretation of cryo-em maps at low resolution Fitting high resolution structures of components into low resolution maps of large complexes. - Stoichiometry/Interactions - Subunit/Domain localization - Rigid-body fitting - Flexible fitting - ab initio modeling
10 Stoichiometry/Interactions
11 Stoichiometry/Interactions
12 Subunit/Domain localization
13 Antibody Labelling -Select a high specificity antibody. (often antibodies against tags, e.g. antimyc, anti-ha or anti-his) - Do negative stain and locate the antibody by visualizing the images. Relatively easy to perform, very useful when starting a new project; Ohi, M.D. et al. Molecular cell 28, (2007)
14 Subunit/Domain localization
15 Tagging Tags on the proteins sometimes can be used as a marker in EM. EGFP at C-terminus of Apc13 Schreiber, A. et al. Nature 470, (2011)
16 Subunit/Domain localization
17 Rigid-body fitting Fitting (or Docking) : fit high-resolution structures of each part (e.g. determined by crystallography) into the EM reconstruction of a large complex. Domain fitting Apc6A Apc6B
18 Rigid-body fitting Visual (manual) fitting using Molecular Graphic software - Chimera - Coot - Pymol - CCP4 - VMD
19 Rigid-body fitting Computational fitting algorithms - UCSF Chimera: fit in map - Situs/Colores (Global 6D search) performs a global translational and rotational search to the best-fit (measured by cross-correlation coefficient). - EMfit - COOT - MultiFit
20 Rigid-body fitting Scoring - Cross-correlation coefficient Accuracy of fitting depends on the resolution of map.
21 Flexible fitting Flexible fitting follows rigid-body fitting. - (Interactive) MDFF: using molecular dynamics simulations The method incorporates the EM density map as a potential so that high density areas in the map correspond to energy minima, so that the atoms in the structure are subject to forces proportional to the gradient of the EM map. (Set secondary structure restraints) - cryo-em map is converted into a potential energy function - maintains the integrity of secondary structure elements.
22 Flexible fitting b Apc6A Fit Apc6A MDFF Apc6B Before and after MDFF MDFF
23 Flexible fitting - Normal mode analysis (imodfit) - Direx (DEN: deformable elastic networks) - Flex-EM References: Trabuco, L.G., Villa, E., Mitra, K., Frank, J. & Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16, (2008). Lopez-Blanco, J.R. & Chacon, P. imodfit: efficient and robust flexible fitting based on vibrational analysis in internal coordinates. Journal of structural biology 184, (2013). Wang, Z. & Schroder, G.F. Real-space refinement with DireX: from global fitting to side-chain improvements. Biopolymers 97, (2012). Topf, M. et al. Protein structure fitting and refinement guided by cryo-em density. Structure 16, (2008).
24 De novo fitting ab initio (secondary) structure prediction - Phyre2 - Tasser - Psipred (Psi-blast based secondary structure prediction) - HHPRED Manual fit in COOT - build alpha-helix as poly-alanine - build beta-sheet
25 Psipred server
26 De novo fitting Computation tools - COOT (find helix) - SSEhunter in EMAN2 - SSEbuilder - Helixhunter - Foldhunter
27 Validation of models Evaluating the reliability of model. At high resolution: all atoms, good rotamers, geometry At low resolution: 1) Is subunits assignment correct? 2) Is crystal structure fitting correct? Biochemical tests. Any other independent data.
28 At low resolution, you will make mistakes. -- CCP4 meeting abstract book Be very careful when publishing cryo-em maps at low resolution.
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32 Model building of spliceosome from a 5-6 Å resolution map - Antibody labelling - Compare surface features - Manual fit in coot - Rigid-body fitting by Kelly Nguyen
33 An example: model building for the anaphase-promoting complex
34 Architecture of APC/C 1) Biochemistry S. cerevisiae Thornton, B.R. et al. Genes Dev 20, (2006). 2) EM and antibody labelling Herzog, F. et al. Science 323, (2009).
35 Stoichiometry by Mass Spec
36 Subunit/Domain Deletion Nature 2011, 470
37 Subunit/Domain Deletion 100 Å Native APC/C 1,160 kda Recombinant APC/C 1,160 kda APC/C ΔApc3-Apc9 996 kda TPR6 (Apc6,Apc3,Apc8,Ap c9,apc13, Apc12) 823 kda SC8 (Apc1,Apc2,Apc4,Ap c5,apc10,apc11,apc 15,Apc8) 700 kda Endogenous APC/C Cdh1 TPR6 APC/C vs APC/C DApc3 Difference density = Apc3 Apc3 TPR 6 Apc6 Recombinant APC/C SC8 APC/C DApc3 vs SC8 Difference density = Apc6+Apc12+Apc13 SC8 Apc8 Common density = Apc8 Schreiber, A. et al. Nature 470, (2011)
38 Subunit/Domain Deletion
39 300 Å Human APC/C at 7.4 Å resolution 200 Å Human APC complex at ~20 Å resolution (by negative stain) Human APC complex at 7.4 Å resolution (by cryoem) 15 proteins (20 subunits)
40 Localizing the coactivator and Apc10
41 Flexible fitting for a TPR subunit: Apc6 a b Apc6A Fit Apc6A MDFF Apc6B Before and after MDFF MDFF
42 Fitting for all TPR subunits
43 Localizing TPR stabilizing subunits
44 Mapping Apc15 (121 aa) by the deletion approach d APC/C, apo (WT), 8.7 Å APC/C, apo ( Apc11-RING), 8.0 Å APC/C, apo ( Apc15), 9.6 Å APC/C, apo (WT), 8.7 Å APC/C, apo ( Apc11-RING), 8.0 Å APC/C, apo ( Ap APC/C, apo (WT) e g d APC/C-Cdh1-Hsl1 complex Apo (WT) APC/C-Cdh1-Hsl1 complex Apo ( RING) e g Apc5 Apc8 NTD Apo ( RING) 10B 10A 9B Apc15 Apc5 NTD Apc5 etpr Apo (WT) Apo ( Apc15) ii 9B iii 9B v 9B vi 9B vii ii 9B iii 9B v 9B 9B vi 9B vii 9A Apc8 10B 10A APC/C, apo ( Apc15) 9B Apc15 9A Apc5 etpr Apo ( Apc15) Apc8 9A 9A 9A Chang L*, Zhang 9A Z*, et 9Aal. (Manuscript under review) 9A f Apc6 Apc8 Apc3 A A A Apc7 A vi iv v vii ii f Apc6 i iii Apc3 B B A A A Apc7 B BA Apc12 vi Apc13 9A iv v vii ii Apc16 i
45 Visualize a small subunit by negative stain Å Wu, S. et al. Fabs enable single particle cryoem studies of small proteins. Structure 20, (2012).
46 Localize Apc5-TPR by recognizing TPR folds
47 Fitting Apc4 and Apc5
48 Apc15 Four-helix bundle 202aa (without loops) Apc4 Apc5-N 9 helices: 135aa (without loops) Apc4 WD40 domain N-terminal domain Bottom view TPR domain Apc5
49 Apc4 (843aa): secondary structure prediction Helices: 300aa Four-helix bundle domain
50 Apc5 N-terminal domain (208aa): secondary structure prediction TPR domain
51
52 Catalytic Subunits: Apc2, Apc11
53 Catalytic Subunits: Apc2, Apc11
54 Identifying Apc1 At subnanometer resolution, some protein folds (e.g. PC and WD40 domains) can be identified.
55 Identifying Cdh1-NTD by comparing with the map of APC/C in apo state. Apc1 PC 6 Cdh1 WD Apc1 PC α3 Cdh1 NTD α4 α2 α5 α1 L1 Flexible by itself. Apc8B C Apc1 WD40 HsAPC in apo state C Apc8B Apc1 WD40 HsAPC-Cdh1-Hsl1 complex
56 Localization of 20 subunits of human APC/C Apc7 Apc16 Apc3 Apc10 Cdh1 D box Apc1 Apc6 Apc2 NTD Apc12 Apc13 Apc4 Apc8 Apc15 Apc5 Subunit organization of human APC/C
57 Model test Apc3 IR tail B IR tail A C Apc10 C L4 R1 L7 N10 S8 N9 S88 N147 Cdh1 Apc10 Cdh1 Apc1 PC D box Mutation Analysis
58 UbcH10 interaction with APC/C: position of target lysines relative to the D box degron By Ziguo Zhang
59 Accuracy of subunits assignment Apc7 Apc16 Apc3 Apc10 Cdh1 D box Apc1 Apc6 Apc2 NTD Apc12 Apc13 Apc4 Apc8 Apc15 Apc5 Subunit organization of human APC/C
60 Summary Challenges in Structure Determination of Macromolecules Integrative modeling Modeling tools for low-resolution cryo-em maps - Stoichiometry/Interactions - Subunit/Domain localization - Rigid-body fitting - Flexible fitting - ab initio modeling Examples - A lot manual work! Be very careful and avoid making mistakes.
61 Acknowledgements David Barford Ziguo Zhang Jing Yang Stephen H. McLaughlin David Barford Ziguo Zhang Andreas Boland Xiaochen Bai Alan Brown Jing Yang Stephen H. McLaughlin
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