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Integrative Determination of Macromolecular Structures and Networks
Department of Bioengineering and Therapeutic Sciences Department of Pharmaceutical Chemistry
California Institute for Quantitative Biosciences University of California, San Francisco
Andrej Sali http://salilab.org/
UCSF
Integrative Determination of Macromolecular Structures and Networks
Department of Bioengineering and Therapeutic Sciences Department of Pharmaceutical Chemistry
California Institute for Quantitative Biosciences University of California, San Francisco
Andrej Sali http://salilab.org/
UCSF
Composition Stoichiometry Chemical complementarity
X-ray diffraction
Composition Stoichiometry Chemical complementarity
X-ray diffraction
To understand and modulate cellular processes, we need their models.
These models are best generated by considering all available information.
Contents
1. Integrative structure modeling
2. Integrative structure modeling of 26S proteasome
Structural biology: Maximize accuracy, resolution, completeness, and efficiency of the
structural coverage of macromolecular assemblies
Motivation: Models will allow us to understand how machines work, how they evolved, how they can be controlled, modified, and perhaps even designed.
There may be thousands of biologically relevant macromolecular complexes whose structures are yet to be characterized, involved in a few hundred core biological processes.
GroEL chaperonin
flagellar motorHIV virus
nuclear pore complexATP synthase ribosome
tRNA synthetaseRNA polymerase II
02/15/2007
Sali A, Earnest T, Glaeser R, Baumeister W. From words to literature in structural proteomics. Nature 422, 216-225, 2003. Ward A, Sali A, Wilson I. Integrative structural biology. Science 339, 913-915, 2013.
PHYSICS
STATISTICSEXPERIMENT
∫
Integrative Structural Biology for maximizing accuracy, resolution, completeness, and efficiency of structure determination
Use structural information from any source: measurement, first principles, rules; resolution: low or high resolution
to obtain the set of all models that are consistent with it.
INTUITION
Gatheringinformation
Analyzing modelsand information
Samplinggood models
Designing modelrepresentationand evaluation
A description of integrative structure determinationSali et al. Nature 422, 216-225, 2003. Alber et al. Nature 450, 683-694, 2007
Robinson et al. Nature 450, 974-982, 2007 Alber et al. Ann.Rev.Biochem. 77, 11.1–11.35, 2008
Russel et al. PLoS Biology 10, 2012 Ward et al. Science 339, 913-915, 2013
Schneidman et al. Curr.Opin.Str.Biol., 2014.
While it may be hard to live with generalization, it is inconceivable to live without it. Peter Gay, Schnitzler’s Century (2002).
Integrative models from our lab
PCSK9-Fab, Cheng, Agard, Pons
Nup84 complex, Rout, Chait
Nuclear Pore Complex, Rout, Chait
Nuclear Pore Complex transport, Rout,Chait, Aitchison,Chook, Liphardt,Cowburn
26 Proteasome Baumeister
Spindle PoleBody Davis, Muller
Microtubule nucleation Agard
Ribosomes, Frank, Akey
Hsp90 landscape Agard
TRiC/CCC Frydman, Chiu
RyR channel Serysheva, Chiu
Nup84 hub Rout, Chait
Nup82 complex, Rout, Chait
Kin28Ccl1
Tfb3
Ssl2Rad3
Tfb1
Tfb5
Tfb2 Ssl1
Tfb4
40S-eIF1-eIF3 Aebersold,Ban
TFIIH Ranish
PhoQ His kinase DeGrado
Substrate folding by Hsp90 Agard
Prion aggregation Prusiner
PDE6 Chu
Actin Chiu
SEA complex Rout, Chait, Dokudovskaya
Nup133, Rout, Chait
Integrative models from our lab
PCSK9-Fab, Cheng, Agard, Pons
Nup84 complex, Rout, Chait
Nuclear Pore Complex, Rout, Chait
Nuclear Pore Complex transport, Rout,Chait, Aitchison,Chook, Liphardt,Cowburn
26 Proteasome Baumeister
Spindle PoleBody Davis, Muller
Microtubule nucleation Agard
Ribosomes, Frank, Akey
Hsp90 landscape Agard
TRiC/CCC Frydman, Chiu
RyR channel Serysheva, Chiu
Nup84 hub Rout, Chait
Nup82 complex, Rout, Chait
Kin28Ccl1
Tfb3
Ssl2Rad3
Tfb1
Tfb5
Tfb2 Ssl1
Tfb4
40S-eIF1-eIF3 Aebersold,Ban
TFIIH Ranish
PhoQ His kinase DeGrado
Substrate folding by Hsp90 Agard
Prion aggregation Prusiner
PDE6 Chu
Actin Chiu
SEA complex Rout, Chait, Dokudovskaya
Nup133, Rout, Chait
Open source, versions, documentation, wiki, examples, mailing lists, unit testing, bug tracking, ...
Integrative Modeling Platform (IMP) http://integrativemodeling.org
D. Russel, K. Lasker, B. Webb, J. Velazquez-Muriel, E. Tjioe, D. Schneidman, F. Alber, B. Peterson, A. Sali, PLoS Biol, 2012. R. Pellarin, M. Bonomi, B. Raveh, S. Calhoun, C. Greenberg, G.Dong, S.J. Kim, I. Chemmama
IMP C++/Python library
restrainer PMI
Simplicity
Flex
ibili
ty
Domain-specific
Chimera
Model
angle restraint
volume restraint
conjugate gradients
Monte Carlo
harmonic
nonbonded list
particledistance score
IO
connectivity restraint
cross correlation
Domino
rigid bodySAXS
docking
molecule
Representation: Atomic Rigid bodies Coarse-grained Multi-scale Symmetry / periodicity Multi-state systems
Scoring: Density maps EM images Proteomics FRET Chemical and Cys cross-linking Homology-derived restraints SAXS H/D Exchange Native mass spectrometry Genetic interactions Statistical potentials Molecular mechanics forcefields Bayesian scoring Library of functional forms (ambiguity, ...)
Analysis: Clustering Chimera Pymol PDB files Density maps
Sampling: Simplex Conjugate Gradients Monte Carlo Brownian Dynamics Molecular Dynamics Replica Exchange Divide-and-conquer enumeration
Integration across computational resources
Goal: Maximize accuracy, resolution, completeness, and efficiency of the structural coverage of macromolecules Hypothesis
Model
Experiment
We describe the proceedings and conclusions from the first Integrative Methods Task Force
Workshop that was held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in the various experimental fields that are contributing to these integrative studies, experts in integrative modeling, and experts in data archiving addressed a series of central questions. What data should be archived? How should integrative models be represented? How should the data and integrative models be validated? How should the data and models be archived? What information should accompany the publication of integrative models?
Outcome of the First Hybrid / Integrative Methods Task Force Workshop
Andrej Sali, Helen M. Berman, Torsten Schwede, Jill Trewhella, Gerard Kleywegt, Stephen K. Burley, John Markley, Haruki Nakamura, Paul Adams, Alexandre Bonvin, Wah Chiu, Tom Ferrin, Kay Grünewald, Aleksandras Gutmanas, Richard Henderson, Gerhard Hummer, Kenji Iwasaki, Graham Johnson, Cathy Lawson, Frank di Maio, Jens Meiler, Marc Marti-Renom, Guy Montelione, Michael Nilges, Ruth Nussinov, Ardan Patwardhan, Matteo dal Peraro, Juri Rappsilber, Randy Read, Helen Saibil, Gunnar Schröder, Charles Schwieters, Claus Seidel, Dmitri Svergun, Maya Topf, Eldon Ulrich, Sameer Velankar, and John D. Westbrook. Structure, 2015.
Pushing the envelope of structural biology by integration of all available information
• Size
• Static systems in single and multiple states
• Dynamic systems
• Bulk and single molecule views
• Impure samples
• Overlapping with other domains such as systems biology
Contents
1. Integrative structure modeling
2. Integrative structure modeling of 26S proteasome
The 26S proteasome acts at the endof the ubiquitin proteasome pathway
Bohn S. and Förster F. Handbook of Proteolytic Enzymes, 2012
The 26S proteasome architecture
Bohn S. and Förster F. Handbook of Proteolytic Enzymes, 2012
20S Core Particle
19S Regulatory
Particle
19S Regulatory
Particle
How to determine the molecular architecture of the complete 26S proteasome?
The 26S proteasome has been refractive to single methods for many years, presumably because of conformational and compositional heterogeneity:
• dissociation of the 19S particle into heterogeneous subcomplexes during purification and concentration,
• presence of proteasome interacting proteins,
• conformational variability of some 19S subunits.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Gathering information and translation into spatial restraints
Overall shape, component positions
Electron microscopy
Component atomic models
X-ray, NMR, homology modeling
Protein-protein contacts
Protein-protein proximities
ProteomicsChemical
cross-linking
RP components and their representation
Rpn3
Rpn5
Rpn6
Rpn7
Rpn9
Rpn12
Rpn1
Rpn2
Rpn8
Rpn11
Rpn10
Rpn13
PC-repeat containing subunits
PCI containing subunits
MPN containing subunits
Ubiquitin receptors
AAA-ATPase hexamer ring
homology model based on PAN structure (Bohn et al, PNAS, 2010).
RP components and their representation
Atomic
Rpn3
Rpn5
Rpn6
Rpn7
Rpn9
Rpn12
Rpn1
Rpn2
Rpn8
Rpn11
Rpn10
Rpn13
PC-repeat containing subunits
PCI containing subunits
MPN containing subunits
Ubiquitin receptors
Fixed coarse
Flexible coarse
Hybrid
AAA-ATPase hexamer ring
homology model based on PAN structure (Bohn et al, PNAS, 2010).
precision, efficiency, availability
RP components and their representation
Atomic
Rpn3
Rpn5
Rpn6
Rpn7
Rpn9
Rpn12
Rpn1
Rpn2
Rpn8
Rpn11
Rpn10
Rpn13
PC-repeat containing subunits
PCI containing subunits
MPN containing subunits
Ubiquitin receptors
Fixed coarse
Flexible coarse
Hybrid
Restraints: Geometric complementarity Excluded volume
Restraints: Excluded volume
Restraints: Chain connectivity Radius of gyration Excluded volume
AAA-ATPase hexamer ring
homology model based on PAN structure (Bohn et al, PNAS, 2010).
precision, efficiency, availability
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Particles Symmetry Increment FSC @ 0.5 FSC @ 0.3
375,000 C2 0.5°
8.4 Å 7.1 Å
Cryo-EM map of the S. pombe 26S proteasome
Å
F. Foerster, S. Bohn, W. Baumeister
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Particles Symmetry Increment FSC @ 0.5 FSC @ 0.3
375,000 C2 0.5°
8.4 Å 7.1 Å
Cryo-EM map of the S. pombe 26S proteasome
Restraints: Cross-correlation between a model and the map
Å
F. Foerster, S. Bohn, W. Baumeister
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Cryo-EM of knockout mutants localizes Rpn10 and Rpn13
Sakata S, Bohn S, Mihalache O, Kiss P, Beck F, Nagy I, Nickell S, Tanaka K, Saeki Y, Förster F, Baumeister W, PNAS, 2012.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Cryo-EM of knockout mutants localizes Rpn10 and Rpn13
Restraints: Positions of Rpn10 and Rpn13 are fixed while sampling other subunits.
Sakata S, Bohn S, Mihalache O, Kiss P, Beck F, Nagy I, Nickell S, Tanaka K, Saeki Y, Förster F, Baumeister W, PNAS, 2012.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6
Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6
Structure - map cross-correlation
Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6
Structure - map cross-correlation
Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6
Structure - map cross-correlation
Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.
Restraints: Position of Rpn6 is fixed while sampling other subunits.
MappingthePhaseSpaceofModelsforTransportthroughtheNPC
Fitting of D. melanogaster Rpn6 X-ray structure into the cryo-EM map localizes Rpn6
Structure - map cross-correlation
Pathare GR, Nagy I, Bohn S, Unverdorben P, Hubert A, Körner R, Nickell S, Lasker K, Sali A, Tamura T, Nishioka T, Förster F, Baumeister W & Bracher A., PNAS, 2012.
Restraints: Position of Rpn6 is fixed while sampling other subunits.
Similarly, for the AAA-ATPase Rtp1-6 heteromeric ring (Bohn et al, PNAS, 2010).
Cross-linking / mass spectrometry data
Leitner, Walzthoeni, Kahraman, Herzog, Rinner, Beck, Aebersold. MCP, 2010
Disuccinimidyl suberate (DSS)
T. Walzthoni, A. Leitner, M. Beck, R. Aebersold
Cross-linking / mass spectrometry data
Inter-molecular cross-linking of exposed Lys residues:
• 12 Rpt-Rpn residue-specific crosslinks (S.p.) • 3 Rpn-Rpn residue-specific crosslinks (S.p.)
Leitner, Walzthoeni, Kahraman, Herzog, Rinner, Beck, Aebersold. MCP, 2010
Disuccinimidyl suberate (DSS)
T. Walzthoni, A. Leitner, M. Beck, R. Aebersold
Cross-linking / mass spectrometry data
Inter-molecular cross-linking of exposed Lys residues:
• 12 Rpt-Rpn residue-specific crosslinks (S.p.) • 3 Rpn-Rpn residue-specific crosslinks (S.p.)
Leitner, Walzthoeni, Kahraman, Herzog, Rinner, Beck, Aebersold. MCP, 2010
Disuccinimidyl suberate (DSS)
Restraints: upper distance bounds on cross-linked atoms or beads.
Sampling good-scoring 19S structures
Sampling good-scoring 19S structures
Discretization
discretization of the map into 238 anchor points
Sampling good-scoring 19S structures
LocalizationDiscretization
discretization of the map into 238 anchor points
localization of coarse subunit models, subject to proteomics data
enumeration of all configurations with at most 5 violations
Sampling good-scoring 19S structures
Localization FittingDiscretization
discretization of the map into 238 anchor points
localization of coarse subunit models, subject to proteomics data
enumeration of all configurations with at most 5 violations
local rigid body fitting of alternative atomic subunit models
selection of best subunit models by fitting quality
Sampling good-scoring 19S structures
Localization Fitting RefinementDiscretization
discretization of the map into 238 anchor points
localization of coarse subunit models, subject to proteomics data
enumeration of all configurations with at most 5 violations
local rigid body fitting of alternative atomic subunit models
selection of best subunit models by fitting quality
atomic model refinementsubject to cross-linking and position restraints
Elizabeth Villa
Sampling good-scoring 19S structures
Localization Fitting RefinementDiscretization
discretization of the map into 238 anchor points
localization of coarse subunit models, subject to proteomics data
enumeration of all configurations with at most 5 violations
local rigid body fitting of alternative atomic subunit models
selection of best subunit models by fitting quality
atomic model refinementsubject to cross-linking and position restraints
Elizabeth Villa
Ensemble of ~0.5 million best-scoring models
Ensemble of ~0.5 million best-scoring models
a
Number of violated restraints
Num
ber o
f mod
els
3.0 3.5 4.0 4.5 5.0
5000
10000
15000
20000
25000
1 5 10 15 20 25
200000
400000
600000
800000
1000000
1200000
1400000
1600000
0
Ensemble of ~0.5 million best-scoring models
a
Number of violated restraints
Num
ber o
f mod
els
3.0 3.5 4.0 4.5 5.0
5000
10000
15000
20000
25000
1 5 10 15 20 25
200000
400000
600000
800000
1000000
1200000
1400000
1600000
0
b
ModelCe
ntro
id R
MSD
[Å]
3
4
5
1
0
2
6
c
Correlation acrossall models
Rpn1Rpn2
Rpn3Rpn5Rpn6
Rpn7
Rpn8Rpn9
Rpn10Rpn11
Rpn12
0.96
0.88
0.80
0.72
0.64
0.56
0.48
Ensemble of ~0.5 million best-scoring models
a
Number of violated restraints
Num
ber o
f mod
els
3.0 3.5 4.0 4.5 5.0
5000
10000
15000
20000
25000
1 5 10 15 20 25
200000
400000
600000
800000
1000000
1200000
1400000
1600000
0
b
ModelCe
ntro
id R
MSD
[Å]
3
4
5
1
0
2
6
d
Cluster 3
Rpn7Cluster 1
Rpn2Rpn1 Rpn11Rpn10 Rpn12Rpn3 Rpn5 Rpn6 Rpn8 Rpn9
Cluster 2
7
PCI6Rpt6
Rpn1
Rpn2Rpn13
Rpn10
Molecular architecture of the 26S proteasome
Julio Ortiz
7
PCI6Rpt6
Rpn1
Rpn2Rpn13
Rpn10
Molecular architecture of the 26S proteasome
Julio Ortiz
Interpretation of the 26S structure evolution, function, modulation
Rpn10Rpn13
Rpn3
Rpn5
Rpn12
Rpn6
Rpn1
Rpn2
PCI containing proteinsUbiquitin receptorsAPC-repeat containing proteins MPN containing proteins
C-130°
Rpn1
Rpn2Rpn10
Rpn1
B
30°
Rpn13
-130°
D
Rpn9
Rpn5
Rpn6
Rpn7
Rpn7
Rpn3
Rpn12
Rpn5
Rpn9
Rpn9
1. Determination of assembly structures and mapping of networks benefit greatly from the inclusion of all available information.
2. Developers and users of open source Integrative Modeling Platform (IMP) are most welcome.
3. Molecular architecture and function of the 26S proteasome.
Summary
Acknowledgements
26S Karen Lasker
Wolfgang Baumeister Friedrich Foerster Stephan Bohn Elizabeth Villa Pia Unverdorben Florian Beck Ganesh Pathare Eri Sakata Stefan Nickell Andreas Bracher Julio Ortiz (movie)
Ruedi Aebersold Thomas Walzthoeni Alexander Leitner Martin Beck
Carol Robinson Florian Stengel
Funding NIH, NSF
Integrative modeling Seung Joong Kim Peter Cimermancic Barak Raveh Ben Webb Charles Greenberg Dina Schneidman Sara Calhoun Daniel Saltzberg Shruthi Viswanath Ilan Chemmama Seth Axen Elina Tjioe Daniel Russel Max Bonomi Riccardo Pellarin Frank Alber Bret Peterson Maya Topf
Mike Rout (RU) Brian Chait (RU) David Agard (UCSF) Tom Ferrin (UCSF) Trisha Davis (Univ of Wash) Mark Winey (U Colorado) Ivan Rayment (U Wisconsin) Wah Chiu (Baylor) Nevan Krogan (UCSF) Robert Stroud (UCSF) Roger Kornberg (Stanford) Stanley Prusiner (UCSF) Jeff Ranish (ISB) Haim Wolfson (TAU) Nenad Ban (ETH) ...
wwPDB Hybrid/Integrative Methods Task ForceHelen Berman Torsten Schwede Jill Trewhella Stephen Burley Gerard Kleywegt
Frank Dimao Klaus Schulten Jens Meiler Gerhard Hummer