Classical MD Simulations and Neutron Scattering

1. Towards Real Time-Molecular Dynamics:Applications to Neutron Scattering Joseph E. Curtis*Mounir TarekYDouglas J. TobiasJ*NIST/University of Maryland YUniversite Henri Poincare, Nancy, FranceJUniversity of California, Irvine

2. 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?)

3. 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 MD specifically for NS 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 . . .

4. USER Structure & Connectivity Desired Observables Atomic Filters Convergence Criteria RT-MD STUCTURE HANDLER TOPOLOGY GENERATOR MD CODE ANALYSIS 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 Open Source MD (NAMD, NWChem, Gromacs, PINY_MD) (Tcl/Tk)

5. 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 PBC MD: Observable Constraints/Restraints Prompt USER for parameters Automatic equilibration Production runs Distributed computing ANALYSIS: Data storage & reduction Experimental details R(w), I(q) MPI & distributed computing Convergence Post-run (re-)analysis

6. 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 SHORT-TIME WINDOWS RMSD MSF I(q,t) S(q, w) C(q, w) G(w) Rho(z) LONG-TIME WINDOWS P2 S2 I(q) (SANS/SAXS)

7. 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)

8. 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.

9. U(Z,s) = kz(Z - Z*)2 + ks(s - s*)2 Once validated, the idea is . . . On new/unknown membrane, measure one or two profiles (say, RC=CR’), use Z* and s*). Then, calculate membrane properties using restrained MD. Diagram by Stephen White

10. Biomolecular Structure by MD-SAXS / MD-SANS Useful? { R(0) } a model Hydration effects Dynamical averaging effects MPI Merzel and Smith PNAS 99 (8): 5378, 2002

11. 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

12. MD vs. QENS on disk chopper TOF instrument at NIST (t ~ 100 ps) Tarek et al. Chemical Physics 292, 435-443, 2003 Native Molten globule Dynamics of N and MG states in solution: neutron scattering vs. MD MD gives excellent representation of dynamics of native a-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

13. 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

14. Order parameter extrapolation Neutron data (Doster et al.) Solution Dehydrated Powder

15. 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