# Presentation: 330437

## Towards Real Time-Molecular Dynamics:Applications to Neutron Scattering Joseph E. Curtis*Mounir TarekDouglas J. Tobias*NIST/University of Maryland Universite Henri Poincare, Nancy, France  University of California, Irvine

## Classical MD Simulations and Neutron Scattering

- _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 to the generation trajectories from MD simulations to analyze neutron experiments

- Input: Coordinates . . . ‘black-box’ . . .
-            . . . Output: NS Observables & Non-observables
- 				(atomic and macroscopic)
- 	**MD should become a transparent tool for the USER**

- ATOM      1  N   LYS     1      17.208  26.496  -2.120  1.00 23.56      7RSA 127
- ATOM      2  CA  LYS     1      17.586  25.166  -1.492  1.00 21.72      7RSA 128
- ATOM      3  C   LYS     1      18.376  25.526  -0.224  1.00 17.32      7RSA 129
- ATOM      4  O   LYS     1      18.800  26.649  -0.055  1.00 16.89      7RSA 130
- ATOM      5  CB  LYS     1      18.268  24.389  -2.543  1.00 27.53      7RSA 131
- ATOM      6  CG  LYS     1      19.133  23.202  -2.442  1.00 33.17      7RSA 132
- ATOM      7  CD  LYS     1      19.271  22.450  -3.786  1.00 37.31      7RSA 133
- ATOM      8  CE  LYS     1      19.911  21.079  -3.701  1.00 39.40      7RSA 134
- ATOM      9  NZ  LYS     1      19.031  19.957  -3.304  1.00 40.47      7RSA 135
- ATOM     10  H1  LYS     1      18.037  27.035  -2.362  1.00 23.61      7RSA 136
- ATOM     11  H2  LYS     1      16.678  26.324  -3.015  1.00 24.45      7RSA 137
- ATOM     12  H3  LYS     1      16.566  26.969  -1.475  1.00 23.74      7RSA 138
- ATOM     13  HA  LYS     1      16.632  24.726  -1.163  1.00 22.07      7RSA 139
- ATOM     14  HB1 LYS     1      17.381  24.106  -3.225  1.00 27.68      7RSA 140
- ATOM     15  HB2 LYS     1      18.823  25.120  -3.218  1.00 27.60      7RSA 141
- . . . ~ 100000 more lines . . .

- MD specifically for NS

## RT-MD

- STUCTURE 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) }
- 	Connectivity
- 	Atomic 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

- RMSD
- MSF
- I(q,t)
- S(q, )
- (q, )
- G()
- Rho(z)
- _LONG-TIME WINDOWS_
- P2
- S2
- I(q) (SANS/SAXS)

- _PRACTICAL EXPERIENCE_
- Typical Runs:
- Equilibration:  0.1 to 1.0 ns
- Production: 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 ~ minutes
- Some can take “days” --> MPI
- Spare Cycles:
- Multiple initial conditions, environments

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

- Courtesy of Ryan Benz (UCI)
- CNBT computational team:
- L. Saiz (NIST)
- R. Benz, F. Castro-Roman, D. Tobias, S. Whilte (UCI)

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

## Membrane 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,) = kz(Z - Z*)2 + k( - *)2

- U(Z,) = kz(Z - Z*)2 + k( - *)2

*Diagram by Stephen White*

- 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.

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

- Useful?
- { R(0) } a model
- Hydration effects
- Dynamical averaging effects
- MPI

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

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

## Dynamics:  NS and MD

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

- _Simulation_: calculate resolution-broadened
- S(Q,E) as FT of I(Q,t)R(t), where R(t) is the
- FT of the instrument resolution function

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

- MD gives excellent representation of dynamics of native -lactalbumin
- MD qualitatively reproduces enhanced broadening (i.e. additional motion) in MG
- QENS shows more broadening in MG vs. N state because MG sample contains substantial population of more highly unfolded states
- MD 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 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

## Solution

- Order parameter
- extrapolation

- Neutron data
- (Doster et al.)

- Dehydrated Powder

## Summary

- Tools exist for “black box” MD
- Flexible framework; new MD and analysis code
- Mature MD techniques & analysis code for NS
- Structure and dynamics (day(s) & GBs)
- Next Steps?
- Pick a builder
- Carefully evaluate MD codes for NS
- Carefully evaluate MD codes for computing infrastructure
- Link computer scientists and MD/NS experts