TowardsTowards Real Time Real Time-Molecular Dynamics:-Molecular Dynamics:Applications to Neutron ScatteringApplications to Neutron Scattering

Joseph E. Curtis* Joseph E. Curtis*Mounir TarekMounir Tarek

Douglas J. TobiasDouglas J. Tobias

*NIST/University of Maryland *NIST/University of Maryland Universite Henri Poincare, Nancy, FranceUniversite Henri Poincare, Nancy, France

University of California, IrvineUniversity of California, Irvine

Classical MD Simulations and Neutron ScatClassical MD Simulations and Neutron Scatteringtering

MD

F=-grad(U) -> { R(t), V(t) }

Atomic detail responsible for NS

Predict what cannot be measured

Filtering tool to design experiments

NS

Complex environments for FF

Readily calculable observables

Overlapping time scale

MD is becoming a commodity; ECCE/NWChem, VMD/NAMD, etc., but . . .

Several “hurdles” remain for new users:

(1) Yet another software program/language/OS to conquer

(2) Setting up new systems in correct environments relevant for NS

(3) MD parameters appropriate for NS

(4) Analysis: data handling, write analysis codes, NS details

(5) Limits of applicability of MD results (right and wrong & why?)

Goal: Lower the activation barrier Goal: Lower the activation barrier to the generation to the generation trajectories from trajectories from MD simulations MD simulations to analyto analyze neutron experimentsze neutron experiments

Input: Coordinates . . . ‘black-box’ . . .Input: Coordinates . . . ‘black-box’ . . .

. . . Output: NS Observables & Non-observables. . . Output: NS Observables & Non-observables

(atomic and macroscopic)(atomic and macroscopic)

MD should become a transparent tool for the USERMD should become a transparent tool for the USER

ATOM 1 N LYS 1 17.208 26.496 -2.120 1.00 23.56 7RSA 127ATOM 2 CA LYS 1 17.586 25.166 -1.492 1.00 21.72 7RSA 128ATOM 3 C LYS 1 18.376 25.526 -0.224 1.00 17.32 7RSA 129ATOM 4 O LYS 1 18.800 26.649 -0.055 1.00 16.89 7RSA 130ATOM 5 CB LYS 1 18.268 24.389 -2.543 1.00 27.53 7RSA 131ATOM 6 CG LYS 1 19.133 23.202 -2.442 1.00 33.17 7RSA 132ATOM 7 CD LYS 1 19.271 22.450 -3.786 1.00 37.31 7RSA 133ATOM 8 CE LYS 1 19.911 21.079 -3.701 1.00 39.40 7RSA 134ATOM 9 NZ LYS 1 19.031 19.957 -3.304 1.00 40.47 7RSA 135ATOM 10 H1 LYS 1 18.037 27.035 -2.362 1.00 23.61 7RSA 136ATOM 11 H2 LYS 1 16.678 26.324 -3.015 1.00 24.45 7RSA 137ATOM 12 H3 LYS 1 16.566 26.969 -1.475 1.00 23.74 7RSA 138ATOM 13 HA LYS 1 16.632 24.726 -1.163 1.00 22.07 7RSA 139ATOM 14 HB1 LYS 1 17.381 24.106 -3.225 1.00 27.68 7RSA 140ATOM 15 HB2 LYS 1 18.823 25.120 -3.218 1.00 27.60 7RSA 141

. . . ~ 100000 more lines . . .

MD specifically for NS

RT-MDSTUCTURE HANDLER

TOPOLOGY GENERATOR

MD CODE

ANALYSIS

USER

Structure &Connectivity

Desired Observables

Atomic Filters

Convergence Criteria

Open Source MD(NAMD, NWChem,

Gromacs, PINY_MD) (Tcl/Tk)

Wrappers

Error Checking

“Library”

MD / NS Details

Structures, FF

MPI : Distributed

Computing Manager

Sampling Strategy

Convergence Check

Experimental Data

OUTPUT

Spectra

Graphs/Data

Images

Summary

INPUT:

STRUCTURE: { R(0) }

X-ray, NMR, NS, homology

TOPOLOGY: { U(q) }

ConnectivityAtomic details Inter-, Intra- U(q)

ENVIRONMENT: Cluster

Solution

Crystal

Powder

Embedded systems

Example: Immerse protein in a lipid

MD: Observable

Constraints/Restraints

Prompt USER for parameters

Automatic equilibration

Production runs

Distributed computing

ANALYSIS:

Data storage & reduction

Experimental details R(), I(q)

MPI & distributed computing

Convergence

Post-run (re-)analysis

PBC

SHORT-TIME WINDOWS

RMSDMSFI(q,t)

S(q, )(q, )G()

Rho(z)

LONG-TIME WINDOWS

P2S2

I(q) (SANS/SAXS)

PRACTICAL EXPERIENCE

Typical Runs:

Equilibration: 0.1 to 1.0 nsProduction: 0.5 to 20 ns

16 CPU cluster ~ 1 ns (1 day to a week)

Data Sets:

10s of MB to 100s of GB

Analysis Codes:

Most NS calculations ~ minutesSome can take “days” --> MPI

Spare Cycles:Multiple initial conditions, environments

QuickTime™ and aVideo decompressorare needed to see this picture.

Courtesy of Ryan Benz (UCI)

CNBT computational team:CNBT computational team:

L. Saiz (NIST)L. Saiz (NIST)

R. Benz, F. Castro-Roman, D. Tobias, S. Whilte (UCI)R. Benz, F. Castro-Roman, D. Tobias, S. Whilte (UCI)

Membrane Structure: CNBT at NCNR S. White (UCI)Membrane Structure: CNBT at NCNR S. White (UCI)

Membrane Structure by Direct InversionMembrane Structure by Direct Inversion

The Problem:

Experimental determination of atomic details of density profiles is too time consuming AND existing MD simulations are in error.

U(Z,U(Z,) = k) = kzz(Z - Z*)(Z - Z*)22 + k + k(( - - *)*)22

Diagram by Stephen White

QuickTime™ and aVideo decompressorare needed to see this picture.

Once validated, the idea is . . .

On new/unknown membrane, measure one or two profiles (say,

RC=CR’), use Z* and *).

Then, calculate membrane properties using restrained MD.

€

I(q) = Alml

0 (q,t)− Blml

0 (q,t)2

m l

∑l

∑N, t

where

Alml

0 (q,t) = 4πi l fk(q) jk(qrk(t))Ylml

* (ωk(t))k

N

∑

Blml

0 (q,t) = 4πi lρ 0 Vk fk(q) jk(qrk(t))Ylml

* (ωk(t))k

N

∑

Useful?

{ R(0) } a model

Hydration effects

Dynamical averaging effects

MPI

Biomolecular Structure by MD-SAXS / MD-SANSBiomolecular Structure by MD-SAXS / MD-SANS

Merzel and Smith PNAS 99 (8): 5378, 2002

Dynamics: NS and MDDynamics: NS and MD

Experiment: estimate mean-squared displacement from elastic intensity viaDebye-Waller factor: I(0) = exp(–Q2<u2>)

Simulation: calculate resolution-broadenedS(Q,E) as FT of I(Q,t)R(t), where R(t) is theFT of the instrument resolution function

€

{r r j}MD →

1

Ne

i (r Q •

r r j (t ))

j

∑ e− i (

r Q •

r r j (0))

≡ I(r

Q ,t)

1

2πI(

r Q ,t)× R(t)e−iωtdt∫ = S(

r Q ,ω ) ⊗ R(ω )

Dynamics of N and MG states in solution: neutron Dynamics of N and MG states in solution: neutron scattering vs. MDscattering vs. MD

MD gives excellent representation of dynamics of native MD gives excellent representation of dynamics of native -lactalbumin-lactalbumin

MD qualitatively reproduces enhanced broadening (i.e. additional motion) in MGMD qualitatively reproduces enhanced broadening (i.e. additional motion) in MG

QENS shows more broadening in MG vs. N state because MG sample contains QENS shows more broadening in MG vs. N state because MG sample contains substantial population of more highly unfolded statessubstantial population of more highly unfolded states

MD provides atomic details necessary to generate more robust analytical modelsMD provides atomic details necessary to generate more robust analytical models

MD vs. QENS on disk chopper TOF instrument at NIST ( ~ 100 ps) Tarek et al. Chemical Physics 292, 435-443, 2003

Native Molten globule

Model Free Approach and NMR Relaxation DataModel Free Approach and NMR Relaxation Data

2H NMR on a calmodulin-peptide complex with partially deuterated methyl groups (48 of 79). Lee & Wand, Nature 411, 501-503, 2001.

Methyl group dynamics quantified by generalized order parameters obtained by fitting relaxation data using Lipari & Szabo “model free” approach

Order parameterextrapolation

Neutron data(Doster et al.)

Solution Dehydrated Powder

SummarySummary

Tools exist for “black box” MDTools exist for “black box” MD Flexible framework; new MD and analysis codeFlexible framework; new MD and analysis code

Mature MD techniques & analysis code for NSMature MD techniques & analysis code for NS Structure and dynamics (day(s) & GBs)Structure and dynamics (day(s) & GBs)

Next Steps?Next Steps? Pick a builderPick a builder Carefully evaluate MD codes for NSCarefully evaluate MD codes for NS Carefully evaluate MD codes for computing infrastructureCarefully evaluate MD codes for computing infrastructure Link computer scientists and MD/NS experts Link computer scientists and MD/NS experts