Molecular Dynamics Simulations of Large Macromolecular Complexes … · 2017-10-17 · T C B G 06/15/16 Molecular Dynamics Simulations of Large Macromolecular Complexes PI Klaus Schulten

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06/15/16

Molecular Dynamics Simulations of Large Macromolecular Complexes

PI Klaus Schulten

Theoretical and Computational Biophysics Group

NIH Center for Macromolecular Modeling and Bioinformatics

University of Illinois at Urbana-Champaign

Till Rudack

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The Main Principle

“Everything that living things do can be understood in terms of the jigglings and wigglings of atoms.“

Richard Feynmen

Molecular dynamics (MD) simulations based on the structural data obtained in experiments are well suited to capture high-resolution dynamics of proteins and reveal their functional mechanisms.

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Computers Can Help to Bridge Disciplines

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Num

ber o

f Ato

ms!

1990!

106!

105!

107!

108!

1994! 1998! 2002! 2006! 2010! 2014!

Lysozyme!

(2 nm)3!

ATP Synthase!

STMV! Ribosome!

HIV Capsid!

Photosynthetic!Chromatophore!

(100 nm)3!

Year!

Aquaporin!

All-Atom Molecular Dynamics Today

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HIV capsid (64M atoms)

Chromatophore (100M atoms)

Chemosensory Array (23M atoms)

Rous Sarcoma Virus (750K atoms 128 replicas)

Rabbit Hemorrhagic Disease (6M atoms)

A Sampling of TCBG‘s Petascale Projects

Perilla et al., “Molecular dynamics simulations of large macromolecular complexes.” Current Opinion in Structural Biology 2015 31:64-74.

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1 Gigabit Network

Compressed Video

Handling Big Data

Solution: Interactive Remote Visualization and Analysis

Theoretical investigations of large macromolecular complexes is more than just running MD simulations. It also involves visualization and analysis of Terabytes of data.

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•  Enablevisualiza-onandanalysesnotpossiblewithconven-onalworksta-ons

•  Accessdatalocatedanywhereintheworld–  SameVMDsessionavailabletoanydevice

Remote Visualization Enables Petascale Anywhere

Bringing Blue Waters to your home

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Making MD Simulations Accessible to Everybody

Ribeiro, JV, Bernardi, RC, Rudack, T, et.al., “QwikMD—Integrative Molecular Dynamics

Toolkit for Novices and Experts.” Scientific Reports 6 (2016): 26536

Collaboration with: John Stone

Dr. Rafael Bernardi Dr. João Ribeiro Dr. Jim Phillips

Prof. Peter Freddolino

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www.ks.uiuc.edu/Research/qwikmd

QwikMD: Easy preparation of an MD simulation Employing QwikMD, a user is able to prepare an MD simulation on a laptop or even a supercomputer in just a few minutes, through an intuitive “point and click” graphical interface connecting VMD and NAMD .

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The Receycling System of the Cell

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The ubiquitin proteasome proteolytic pathway

26S Proteasome

Substrate tagging by Ubq4

Ubq4-substrate recognition

Substrate degradation

Bortezomi

b

Kisselev Cancer Cell 2013

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Near-Atomic Model of the 26S Proteasome

Wolfgang Baumeister

Cryo-EM density Subunits from X-ray crystalography,

NMR, and homology modeling

PDB-ID 4CR2

EMDB-ID 2594

Resolution 7.7 Å

Unverdorben et al. PNAS 2014

Molecular Dynamics Flexible Fitting (MDFF): Trabuco et al. Structure 2008

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Functional Subunits of the 26S Proteasome

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Deubiquitylation subunit: Rpn11

Unverdorben et al. PNAS 2014 Chain V of PDB-ID 4CR2

Missing segments

- highly flexible

- ambigous density

Active site of Rpn11: substrate is cleaved from ubiquitin tag

Complete models are a basic prerequisite to perform MD simulations

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Rosetta/MDFF Interactive Model Building

incomplete structural model deposited in the PDB

complete structural model that fits cryo-EM data

de novostructure

prediction

energyranking

modelfiltering

interactiveMDFF of

cryo-EM data



de novostructure

prediction

energyranking

modelfiltering

interactiveMDFF of

cryo-EM data

Rosetta



de novostructure

prediction

energyranking

modelfiltering

interactiveMDFF of

cryo-EM data

Rosetta VMD/NAMDLeaver-Fay et al. Methods Enzymol. 2011 Porter et al. PLoS One 2015

Humphrey et al. J. Mol. Graph. 1996 Philips et al. J. Comput. Chem. 2005

www.ks.uiuc.edu/Research/MDFF

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Model Filtering by Secondary Structure Secondary structure histogram of

predicted ensembles of Rpn11‘s C-terminal tail

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Predicted Model

Represenative model of the predicted averaged secondary structure pattern

for Rpn11‘s C-terminal tail (purple)

Rosetta tends to build compact structures

Secondary structure pattern of amino acids 217-306 (purple)

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Visual Inspection of cryo-EM Density

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Predicted Model to Initiate MDFF

Represenative model of predicted ensemble for Rpn11‘s C-terminal tail

Secondary structure pattern of amino acids 217-306 (purple)

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Interactive Molecular Dynamics Flexible Fitting

MDFF Tutorial on You Tube and at http://www.ks.uiuc.edu/Research/mdff/

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Complete Model of Rpn11 Fitted to Density

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Incomplete vs. Complete Model Incomplete model Complete model

Rosetta/MDFF

Cross correlation 0.61 Cross correlation 0.63

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Low vs. High Resolution Density Model

Isolated lid cryo-EM model

Gabriel Lander / Andreas Martin

PDB-ID 3JCK EMDB-ID 6479

Resolution 3.5 Å

Dambacher et al. eLife 2016

Red: 3.5 Å cryo-EM model of Rpn11 within the isolated proteasomal lid

Purple: completed Rpn11 model within the 7.7 Å proteasomal cryo-EM density

26S proteasome cryo-EM density

Wolfgang Baumeister

EMDB-ID 2594

Resolution 7.7 Å

Unverdorben et al. PNAS 2014

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Low vs. High Resolution Density Model Structure predicted for low resolution matches structure of high resolution

Secondary structure pattern obtained by Rosetta/MDFF employing a 7.7 Å density

Secondary structure pattern of a structure modeled into a 3.5 Å density

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Ubiquitin Recognition by Rpn10

K48-linked tetra-ubiquitin Rpn10 Recognition

Re-folding, electrostatic, and hydrophobic interactions lead to recognition

Zhang, Y, Vukovic, L, Rudack,T et al. “Recognition of poly-ubiquitins by the proteasome through protein re-folding guided by electrostatic and hydrophobic interactions.” Journal Physical Chemistry B, McCammon Festschrift 2016 in press

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Rpn10

PDB-ID 2X5N (S. Pombe)

PDB-ID 2KDE (human)

Ubiquitin interaction motif (UIM)

Ubiquitin recognition and deubiquitylation

Ubiquitin Transport

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Generalized Simulated Annealing – GSAFold

•  Amino acid residues connecting Rpn10’s UIM with the proteasome are likely to be disordered and stochastic searching algorithms such as GSA can be used to explore their conformational space.

•  GSAFold coupled to NAMD searches low-energy conformations to be used as starting points for molecular dynamics studies.

GSAFold NAMD Plugin – Allows ab initio structure prediction

New implementation on Blue Waters also Allows the Conformational Search for Large Flexible Regions

Collaboration with: Marcelo Melo

Dr. Rafael Bernardi Prof. Pedro Pascutti

Melo, MCR, et al. "GSAFold: A new application of GSA to protein structure prediction." Proteins: Structure, Function, and Bioinformatics 80.9 (2012): 2305-2310.

Bernardi, RC., Melo MCR, and Schulten K. "Enhanced sampling techniques in molecular dynamics simulations of biological systems." Biochimica et Biophysica

Acta (BBA)-General Subjects 1850.5 (2015): 872-877.

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Conformation Space of Rpn10 Anchor

Fishing Rod

Fishing Hook

Globular Part

Center of Massof UIM

The conformational space of the Rpn10 linker is highly flexible.

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Ubiquitin Transport to Deubiquitinase Rpn11

Ubiquitin Transport

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The Motor of the Proteasome

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3.9 Å Resolution Density of the Human 26S Proteasome

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High-resolution Real Space Refinment with MDFF

Advantage: Positions of bulky side chains can be observed from density Challenge: no detailed side chain orientation X-ray structure refinement tools failed in the range of 4-5 Å resolution Solution: combining MDFF with monte carlo based backbone and side chain rotamer search algorithms in an iterative manner

Goh BC., Hadden JA, et al., “Computational methodologies for real-space structural refinement of large macromolecular complexes.” Annual Review Biophysics, 2016 in press.

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The ATPase Motor of the 26S Proteasome

Schweitzer A, Aufderheide A, Rudack T, et al. “The structure of the 26S proteasome at a resolution of 3.9 Å.” PNAS 2016 in press.

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The Motor Action of Protein Unfolding

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NAMD QM/MM

Collaboration with:

Marcelo Melo Dr. Rafael Bernardi

Dr. Jim Phillips Prof. Gerd Rocha Prof. Frank Neese

The atomic structure enable detailed investigations of the unfolding process by QM/MM simulations combined with path sampling techniques.

NAMD QM/MM interface with MOPAC and ORCA will be

released in the second semester of 2016

Generic Interface will also

allow the use of any Quantum Chemistry Software

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Why Blue Waters

Running Simulations Investigating the functional processes of large protein machineries which occur on the millisecond timescale, is only possible on petascale computing resources, such as Blue waters. Visualization and Analyses Blue Waters is not only needed for running simulations of large macromolecular complexes but also for visualization and analysis of the produced data. Obtaining Atomic Structures De novo structure prediction and monte carlo based sampling algorithms are only efficient if thousands of models are predicted. Employing these algorithms for the numerous subunits of the proteasome is a well-suited task for the large-scale parallel architecture of Blue Waters.

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Work on Next Generation Computer Systems

Motor Action of Protein Unfolding We would like to investigate how the motor action of ATP hydrolysis is driving the subunit reorganization during the functional cycle of substrate processing, which requires extensive sampling of long trajectories. Drug Design In order to exploit the proteasome’s role as a ubiquitous drug target, high-throughput screening of thousands of drugs that possibly interact with the proteasome might be feasible by 2020 and would require pre-exascale computing for such a complex macromolecular machinery as the 26S proteasome.

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Acknowledgments Klaus Schulten

Wolfgang Baumeister

Ryan McGreevy John Stone

VMD NAMD MDFF

Jim Phillips

The Schulten Group

QwikMD GSA

Joao Ribeiro Marcelo Mello

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Blue Waters Helped to Solve the Atomic Structure of the Human Receycling System

Documents

Molecular Dynamics Simulations of Large Macromolecular Complexes … · 2017-10-17 · T C B G 06/15/16 Molecular Dynamics Simulations of Large Macromolecular Complexes PI Klaus Schulten