Molecular mechanisms that dictate the viral lifecycle are - - PowerPoint PPT Presentation

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Molecular mechanisms that dictate the viral lifecycle are - - PowerPoint PPT Presentation

V IR A L M O R PH O G EN ESIS T H R O U G H TH E L EN S O F L A R G E -S C A LE C O A R SE -G R A IN ED S IM U LATIO N S Alexander J. Pak and Gregory A. Voth Department of Chemistry, The University of Chicago Blue Waters Symposium, June 5 th , 2018


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VIR A L MO R PH O G EN ESIS TH R O U G H TH E LEN S O F LA R G E-SC A LE CO A R SE-GR A IN ED SIM U LATIO N S

Alexander J. Pak and Gregory A. Voth Department of Chemistry, The University of Chicago Blue Waters Symposium, June 5th, 2018

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Molecular mechanisms that dictate the viral lifecycle are therapeutic targets

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The Goal: Fundamental molecular insights into highly dynamical,

  • ut-of-equilibrium biophysical processes

Electron Microscopy (e.g., cryoEM/cryoET) Fluorescence Microscopy (e.g., spt-PALM/STORM) Computer SimulaIons (e.g., Molecular Dynamics)

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Enhanced Sampling Specialized HPC Conven5onal MD + HPC

The challenge: Overcoming untenable molecular dynamics simulations

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Size (# atoms) Time (# sec.)

103 104 105 106 107 ... 1063 10-9 10-6 10-3 ... 104

EXPERIM EN TS!!

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Our approach: Coarse-grained modeling and simulation

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MAPPING

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1 2 3 4 5 6 3 4 5 6 7 8 9 10 11 12 Unfolded Folded

Pair Distance (Å) Pair Interaction (kcal/mol) PARAMETERIZATION

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A new general framework: ultra-coarse-graining (UCG)

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Dama, … and Voth, JCTC 9:2466 (2013); Davtyan, ... and Voth, JCTC 10:5265 (2014); Dama, … and Voth, JCTC 13:1010 (2017).

States within UCG “beads” — physical — disorder transition ligand binding loop folding/unfolding — chemical — nucleotide hydrolysis redox reaction protonation

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Custom-tailored software on Blue Waters enables UCG-MD simulations

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Grime and Voth, JCTC, 10:214 (2018)

The heterogeneous nature of implicit-solvent UCG models requires MD engine customizaHon:

Load Balancing via Hilbert Space Filling Curves Sparse Data Structures for Efficient Memory Usage

UCG

Dynamic Assignment During RunHme

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SLIDE 7

Our focus: Late-stages of HIV-1

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The immature lattice assembly process is catalyzed by scaffolds

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RNA co-localizes protein and promotes assembly

Pak, Grime, … and Voth. PNAS 114:E10056 (2017)

Membrane deformaLon also serves to co-localize and promote assembly

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SLIDE 9

The mature capsid also requires precise conditions for assembly

9 20 nm 10 nm

Pornillos et al, 2011

No UCG switching UCG switching

Grime, Dama … and Voth. Nat. Comm. 7:11568 (2016)

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Nature seems to call for a balance between strength (E) and specificity (S)

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DISASSEMBLED PROPER ASSEMBLY

E S

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Perturbing this balance may lead to reduced infectivity

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(in collabora,on with Lippinco2-Schwartz (NIH))

Pak, Grime, … and Voth. PNAS 114:E10056 (2017)

Enhancing protein-protein interac,ons

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GS-CA1: A new type of HIV drug

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The HIV capsid (left) protects the viral genome so it can be delivered into host cells. Gilead’s tool compound, GS-CA1 (light green, right), binds between two capsid proteins in the pinwheel-like hexamer. Credit: Gilead Sciences Volume 95 Issue 31 | pp. 23-25 Issue Date: July 31, 2017

Conquering HIV’s capsid

After a dozen years, researchers have struck upon a molecule that can disrupt an elusive HIV target

By Lisa M. Jarvis

For most of his career at Gilead Sciences, medicinal chemist Winston Tse has lived and breathed one thing. While his peers at other companies hopped from project to project, Tse has spent the past decade obsessing over a single target: the HIV capsid. HIV’s capsid is a complex, protein-rich shell that protects the genetic payload the virus is made up of 1,500 capsid proteins that

  • rganize themselves into hexamers and

pentamers to form an eggplant-shaped

  • shell. HIV researchers had no close-ups of

the full capsid; a crystal structure had captured only the monomeric protein. Moreover, scientists weren’t—and still aren’t—sure how the capsid assembles. Many envision something like a molecular knitting project that begins at the stem end of the eggplant and gets wider as rows of hexamers are added. Yet one thing was clear: Those 1,500 proteins need to knit together with just the right geometry and kinetics. “There is a real beauty in how geometrically structured it is,” says Tomas Cihlar, vice president of biology at Gilead. The shell needs to be stable enough to come together during virus maturation but still disassemble to expose its genetic payload once it is inside the host cell. That leads to a “delicate equilibrium in the whole capsid shell, which we thought could really be its Achilles’ heel,” Cihlar, who conceived of the capsid program back in 2006, adds. In addition to having limited structural information about the shell, Gilead researchers knew of no molecules that could convincingly bind to the capsid protein. The only clues in the literature were “some really

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Simulating GS-CA1 effects induces assembly

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(notably, under condi3ons that do not produce self-assembly)

Initial “stabilized” CA Result ≈ 0.5% No effect ≈ 1.0% No effect ≈ 1.5% No effect ≈ 2.5% No effect ≈ 5.0% Single nucleation ≈ 10.0% Multiple nucleation

Self-assembly process appears sensi2ve to even small localized “boosts”

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Enhanced morphological diversity with defective end-points

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Pak, Grime, and Voth, in prepara(on (2018) Ma9ei, Glass, Hagen, Krausslich, and Briggs, Science 354:6318 (2016)

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ABERRANT ASSEMBLY

Increasing strength (E) or decreasing specificity (S) is a viable therapeutic strategy

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DISASSEMBLED PROPER ASSEMBLY

E S E

  • r S

E

  • r S
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Future direction: Coarse-grained directed simulations (CGDS)

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!(#) = −'() ln ∫ - ⃗ /0- ⃗ /12 ⃗ /0 − # 3456( ⃗

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∫ -⃗ 8 3456 ⃗

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Full system with 3N+3M atoms has coordinates ⃗ 8 = (⃗ /0, ⃗ /1), subsystem has coordinates has coordinates ⃗ /0. Integrate out /1leaving PMF ac<ng on subsystem: Then the average value of any observable L of the subsystem coordinates (L ⃗ 8 ≡ L ⃗ /0 ) can be recovered just simula<ng the subsystem: L = ∫ -⃗ 8 L ⃗ 8 3456 ⃗

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∫ -⃗ 8 3456 ⃗

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= N -# L(#)345O P ∫ -#345O P

Hocky, Dannenhoffer-Lafage, and Voth, JCTC 18:4593 (2017)

Prac%cal alterna%ve!

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Toward a high-throughput multi-scale workflow

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~75M atoms ~300K par1cles ~220 capsomers

cryoET (e.g., Briggs Group) CG MD AA MD

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Toward a high-throughput multi-scale workflow

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~300K par+cles ~220 capsomers

cryoET (e.g., Briggs Group) CG MD CGDS (AA MD)

~250-750K atoms

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Thank you for your attention!

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University of Chicago Voth Group University of Virginia Mark Yeager Barbie Ganser- Pornillos NIH Jennifer LippincoE- Schwartz MRC-LMB John Briggs