1. Advanced Sampling Techniques When molecular dynamics is just not enough.

2. Why do we need enhanced sampling?

3. MD N Trying to fold a peptide from two different initial conditions, gives two different free energy surfaces !

4. Rates depend on barriers and T

5. What can we do to speed things up? It’s basically a problem of rates, or kinetics – Rates are exponentially dependent on temperature – higher T = higher KE = ability to cross higher barriers Can we just simulate at a higher T? Yes! Folding is faster at higher T.

6. Why is convergence so slow? We want the average of observable properties- so we need to include in that weighted average all of the important values that it might have Structural averages can be slow to converge- you need to wait for the structure to change, and do it many, many times. Sampling an event one time doesn’t tell you the correct probability (or weight) for that event – What is the proper weight? Boltzmann’s equation

7. What can we do to speed things up? It’s basically a problem of rates, or kinetics – Rates are exponentially dependent on temperature – higher T = higher KE = ability to cross higher barriers Can we just simulate at a higher T? Yes! Folding is faster at higher T. but…we mess up the thermodynamics. – Equilibrium constants (weights) also depend on T

8. Populations depend on ΔE and T

9. Free energies along a defined reaction coordinate via Umbrella Sampling

10. Umbrella Sampling How to force barrier crossings without compromising thermodynamic properties? Very slow transitions

11. Umbrella Sampling trapped, bad ΔG good for ΔG

12. One could just run dynamics and wait until all space has been sampled. Then, if one extracts P(x k ) from the trajectory, the PMF can be written as: However, it takes forever to properly sample all conformations, and to jump over the barrier. The solution is to bias the system towards whatever value of the coordinate we want. This is called unbiased sampling

13. AMBER WS 2012 CECAM Lausanne Umbrella Sampling True PMF Ideal Biasing Potential No barrier, perfect sampling We could BIAS the simulation, but we do not really know how to do it exactly.

14. AMBER WS 2012 CECAM Lausanne Umbrella Sampling True PMF Windows: 1 2 3 4 5 choose i, k and x i system-dependent Introduce biasing potentials along the reaction coordinate

15. AMBER WS 2012 CECAM Lausanne Adding a quadratic biasing potential

16. Check for sufficient overlap Histograms from neighboring windows should overlap strongly, all points on the RC must be sampled suffciently.

17. AMBER WS 2012 CECAM Lausanne Umbrella Sampling Simulation Window Histogram Part of PMF Final computed PMF from many windows Solved iteratively using e.g. the WHAM program by Alan Grossfield Constructing the PMF

18. AMBER WS 2012 CECAM Lausanne Umbrella Sampling Histograms from neighboring windows should overlap strongly, all points on the RC must be sampled suffciently. Solved iteratively using e.g. the WHAM program by Alan Grossfield (http://membrane.urmc.rochester.edu/ content/wham) Check for sufficient overlap between sampled regions

19. Histograms & free energy profiles Umbrella run needs many simulations Do NOT need to sample full range in 1 simulation G= - RTlnP/P 0

20. Comparing 2 conformations Song, Hornak, de los Santos, Grollman and Simmerling, Biochemistry 2006 It will take much too long to get precise populations for these 2 minima just by running MD.

21. 8OG binding mode in complex: dihedral umbrella sampling syn anti Song, Hornak, de los Santos, Grollman and Simmerling, Biochemistry 2006

22. Simulations reveal how the energy profile changes if a mutation is made syn anti Song, Hornak, de los Santos, Grollman and Simmerling, Biochemistry 2006 Effect of mutations

23.  Conformational space has many local minima  Barriers hinder rapid escape from high-energy minima  How can we search and score all conformations? Replica Exchange Coordinate Energy

24. Early tries: Run MD at T low, once in a while heat up to T high, run some more. Change to T low Repeat… The problem is that the statistical weights of the first and second T low are unknown and unknowable.

25. Metropolis Monte Carlo, one of the most important algorithms of the 20 th century.

26. MANIAC computer. Los Alamos national Lab 1943-1944 80 Kbytes memory 11Khz Clock

27. When Bugs were real (Mark II computer, 1945)

28. Coordinate Energy We want to sample all conformations in a way that when we are done, the probability of finding a particular conformation is

29. Moreover, in equilibrium, we require that the rates from minimum A to B be exactly equal to the rate from minimum B to A. This is called Microscopic Reversibility. Coordinate Energy

30. Temperature Replica Exchange (Parallel tempering) 375K 350K 325K 300K REMD Hansmann, U., CPL 1997 Sugita & Okamoto, CPL 1999

31.  Impose desired weighting (limiting distributionon exchange calculation 375K 350K 325K 300K  Impose reversibility/detailed balance Rewrite using only potential energies, by rescaling kinetic energy for both replicas. Exchange?

32. Temperature Replica Exchange  MD runs over range of T  Periodically swap structures  Faster convergence than regular MD  Populations as a function of T  Drawback: ΔE must be small  small ΔT for large systems Number of replicas ~ N 1/2

33. Does it really converge faster than regular MD? Yes ! But not really as much as one would think (or hope, or dream) 5k B T For a barrier of ~5k B T low, and a T high= 2T low, the barrier crossing rate is x12 Better methods use replicas in Hamiltonian space.  Different biasing for each replica

34. Do you have an MD code?  Build yourself a REMD code in minutes !  Just write a wrapper ! START REMD Spawn replicas and run MD at different temperatures Stop MDs Decide to exchange or not

35. Advanced methods can help MD Replica exchange N Roe, Hornak and Simmerling, J. Mol. Biol. 2006

36. What if you don’t know the pathway for the change?

37. in silico structure interconversions ? “endpoint method”: link many simulations timescale independent Bergonzo, Campbell, Walker & Simmerling, Intl J Quantum Chem 2009

38. NEB : use many simulations to study slow dynamics R P Coordinate 2 F || F┴F┴ Coordinate 1 Final path Standard forces Spring forces

39. Reaction Path Calculations: NEB kcal/mol NEB requires coupling of multiple simulations using “springs” A B How can we get from A to B?

40. Other techniques Accelerated MD Hamiltonian Replica Exchange Multi Dimensional Replica Exchange Constant pH ( + REMD) Steered Molecular Dynamics

41. Accelerated Molecular Dynamics de Oliveira C.A.F., Hamelberg, D., McCammon, J.A., On the Application of Accelerated Molecular Dynamics to Liquid Water Simulations. J. Phys. Chem. B 2006. Hamelberg, D., de Oliveira C.A.F., McCammon J.A., Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. The Journal of chemical physics, 2007. Grant, B.J., Gorfe, A.A., and McCammon, J.A., Ras conformational switching: simulating nucleotide-dependent conformational transitions with accelerated molecular dynamics. PLoS computational biology, 2009. de Oliveira, C.A.F., et al., Large-Scale Conformational Changes of Trypanosoma cruzi Proline Racemase Predicted by Accelerated Molecular Dynamics Simulation. PLoS computational biology, 2011.

42. AMD effect on the Potential

43. Principal Component Analysis Build PC space based on the 500ns aMD simulation Project X-ray, Shaw structures, 500ns cMD, and 1ms cMD Cost: several million dollars Energy consumption ~116.5KW Code mostly hardwired to the hardware Cost: $2000 Energy consumption ~0.9KW AMBER 12 Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics. Levi Pierce, Romelia Salomon-Ferrer, Cesar Augusto de Oliveira, J. Andrew McCammon and Ross C. Walker. J. Chem. Theory Comput., 2012, 8 (9), pp 2997–3002 DOI: 10.1021/ct300284c