Poly-Proline with AMBER 11

V. Babin and C. Sagui
April 27, 2010

Abstract

This document describes a couple of ways to assess the conformational equilibria of a short polyproline peptide using sander.MPI package from Amber11. In particular, the so called steered molecular dynamics is illustrated along with the famous replica-exchange[1] protocol. The familiriaty with the subject is assumed as the intent of this document is to expose the technicalities rather than describe the basics.

Introduction

When preparing to write this note, I faced the following unavoidable choice: (1) use a simple and hence relatively boring example that is easily runnable within an hour or (2) consider a larger system that takes longer to run, yet is marginally more interesting. Option (2) won this time, and hence this text describes two ways of simulating the Ace(Pro)5 NHe peptide in AMBER 11. All the files and scripts needed to reproduce the runs described in this note should be available alongside with this document in the polyproline-tutorial-files.tar.bz2 archive (Warning: 60 Mb).

Preliminaries

We make the prmtop and inpcrd files using the teLeap program with the input shown in Fig.1. We are going to use GB/SA solvation model from Refs.[234] (igb = 2 and gbsa = 1) and therefore line 5 instructs the teLeap program to use appropriate radii. The rest should be self-explanatory.


 
1 source leaprc.ff99SB 
2 
3 m = sequence {ACE PRO PRO PRO PRO PRO NHE} 
4 
5 set default PBradii mbondi2 
6 saveamberparm m prmtop inpcrd 
7 savepdb m inpcrd.pdb 
8 
9 quit  
10

Figure 1: Input file for the LEaP program used to generate the prmtop and inpcrd files for the Ace(Pro)5 NHe peptide.


It is instructive to try regular molecular dynamics to see the lack of ergodicity at room temperature (assumed to be 300K). To this end, one can use the mdin file shown in Fig.2. Apart from the usual &cntrl namelist the file includes the ncsu_abmd section that defines a collective variable of type of COS_OF_DIHEDRAL (lines 17–25) and instructs sander to save the values of the variable every 500 steps (1 ps) to the monitor.txt file (lines 12, 14, and 15). The collective variable is given by the sum of cosines of the dihedral angles along the peptide bonds connecting successive proline residues (see comments in Fig.2, lines 20–24). For all the bonds in cis conformation, the variable would be assume the value of five, while for the all trans configuration, it would be equal minus five. Once the simulation is complete, examination of the monitor.txt file shows that no cis/trans transitions happened over one nanosecond. It is instructive to run it longer and try higher temperatures as well. Summarizing, the regular molecular dynamics is not ergodic in this case: the trajectory does not visit all equilibrium configurations with Boltzmann probability; it gets stuck in the vicinity of the initial configuration because of the high (free) energy barriers separating different (free) energy minima. The aim of this tutorial is to show two possible routes to study the equilibrium ensemble in spite of presence of these barriers. In the next section we are going to consider so-called steered molecular dynamics that can be used to compute the free energy differences between different conformations. Afterwards we are going to go through a more demanding (yet more powerful as well) replica-exchange[1] technique.


 
1 **** this line is required **** 
2 &cntrl 
3   ntwx = 0, ntpr = 5000, ntwr = 50000, 
4   ntt = 3, temp0 = 300.0, gamma_ln = 1.0, 
5   igb = 2, gbsa = 1, dielc = 1.0, cut = 18.0, 
6   ntb = 0, ntc = 2, ntf = 2, tol = 0.000001, 
7   nstlim = 500000, dt = 0.002, ntp = 0, ibelly = 0, 
8   ntr = 0, imin = 0, irest = 0, ntx = 1, ig = 27606 
9 / 
10 
11 ncsu_abmd 
12   mode = ANALYSIS 
13 
14   monitor_file = monitor.txt 
15   monitor_freq = 500 ! 1 ps 
16 
17   variable 
18     type = COS_OF_DIHEDRAL 
19 
20     i = ( 2,  5,  7, 17,  ! :1@CH3 == :1@C == :2@N == :2@CA 
21          17, 19, 21, 31,  !  :2@CA == :2@C == :3@N == :3@CA 
22          31, 33, 35, 45,  !  :3@CA == :3@C == :4@N == :4@CA 
23          45, 47, 49, 59,  !  :4@CA == :4@C == :5@N == :5@CA 
24          59, 61, 63, 73)  !  :5@CA == :5@C == :6@N == :6@CA 
25   end variable 
26 end ncsu_abmd  
27

Figure 2: Input file for the sander program suitable for doing regular molecular dynamics at room temperature.


polyproline-tutorial-files/2.preliminaries directory.

Steered molecular dynamics

The so-called steered molecular dynamics amounts to a non-stationary harmonic restraint applied to a (set of) collective variable(s). The method is extensively discussed in the literature and we therefore skip any details here directing the reader to the excellent Refs.[567] and references therein. A short summary for the lazy: one can “pull” the system between different states, measure the non-equilibrium work performed, and then infer corresponding equilibrium free energy differences.

Several subsystems in sander implement the “pulling” functionality and only one of them (ncsu_smd) is exposed in this writeup. Curious reader is directed to the user manual to find out more on these and related matters.

We proceed by pulling on the COS_OF_DIHEDRAL variable described above changing its value between -5 and 5 (that is, between the all-trans and all-cis conformations). To get a robust Δf we perform one hundred of forward pullings (going from -5 to +5) and one more hundred of reverse pullings (going from +5 to -5). Initial coordinates used for each of the ncsu_smd runs were equilibrated in the corresponding state (all-trans for the forward pullings, and all-cis for the reverse pullings). Relevant section of mdin file is shown in Fig.3 and should be self-explanatory. The progress of the pulling is reported to the work.txt file every 10 ps. The total work is reported at the end of this file once the simulation is complete. The files for all two hundred pulls can be found in polyproline-tutorial-files/3.smd directory and we encourage the reader to examine (or re-run) them. There is also a perl script that can be used to compute the free energy difference.

Numerical values of the non-equilibrium work for the forward (WF ) and reverse (WR) pullings can be used to compute the Δf estimate using Crooks[5] relations (the equation for the estimator coincides with the earlier Bennet’s acceptance ratio formula[8]) from the following equation:

 nF                            nR
∑   -----------1------------- ∑   ------------1------------
    1+ nF ∕nR exp (W Fi − Δf ) −    1+  nR∕nF exp(W iR+ Δf ) = 0,
i=1                            i=1
(1)

with nF,R denoting the numbers of forward and reverse simulations, respectively. We got 4.58 kcal/mol for the Δf. It is instructive to compare this estimator with the ones computed from the Jarzynski[9] identity applied to the only forward (10.65 kcal/mol) or only reverse (-1.49 kcal/mol) processes.


 
1 **** this line is required **** 
2 &cntrl 
3    ... 
4 / 
5 
6 ncsu_smd 
7   output_file = output/00’ 
8   output_freq = 5000 ! 10 ps 
9 
10   variable 
11     type = COS_OF_DIHEDRAL 
12 
13     i = ( 2,  5,  7, 17,  ! :1@CH3 == :1@C == :2@N == :2@CA 
14          17, 19, 21, 31,  !  :2@CA == :2@C == :3@N == :3@CA 
15          31, 33, 35, 45,  !  :3@CA == :3@C == :4@N == :4@CA 
16          45, 47, 49, 59,  !  :4@CA == :4@C == :5@N == :5@CA 
17          59, 61, 63, 73)  !  :5@CA == :5@C == :6@N == :6@CA 
18 
19     path = (-5.0, 5.0) harm = (100.0) 
20   end variable 
21 end ncsu_smd  
22

Figure 3: Input file for steered molecular dynamics going from all trans to all cis configuration of the Ace(Pro)5 NHe peptide (the &cntrl namelist is not shown).


(Hamiltonian) replica exchange

The replica-exchange method is a powerful formulation first proposed by Geyer in 1991 in conference proceedings[1] and subsequently rediscovered in a different area of science[10]. The basic idea of the technique is to exchange configurations between several simulations in a way that preserves detailed balance and hence makes every simulation to sample from its target distribution. The barriers that are high in one replica (refered to as “cold” replica with respect to the barriers) may happen to be significantly lower in another one (“hot” replica) so that the barriers crossing is greatly enhanced.

The success of the method depends crucially on the choice of the “hot” replica(s). Here we are going to use the potentials of mean force (PMF) to bias the dynamics in the “hot” replicas. The advantage of this approach over the more conventional parallel tempering is that the temperature does not change across the replicas hence rendering this kind of setup more suitable for explicit solvent simulations. We (shamelessly) refer curious readers to our preprint http://arxiv.org/abs/0911.5132v2 for a more comprehensive presentation.

The idea is very simple (and by no means ours): assuming that a “slow mode” can be described by a collective variable σ(r), and that the potential of mean force (also referred to as Landau free energy) associated with that collective variable is known, one can construct a “hot” replica by biasing the original dynamics by the negative of the PMF thus rendering the states with different values of σ(r) equally probable. In practice the PMF is typically unknown, yet fortunately, a biasing potential that is within a few kBT of the true PMF, will do the trick. The latter can be computed in a variety of ways. In this writeup we are going to use the so-called ABMD[11] (adaptively biased molecular dynamics), which is yet another variation of the earlier local elevation[12] method (LEM), grown up from our efforts to make the so-called metadynamics[1314] method (another LEM variation) more practical.

For the Ace(Pro)5 NHe peptide, one readily identifies the cis/trans transitions of the peptide bonds as the “slow modes”. There are five of them, and hence our aim is to setup a replica-exchange scheme with five “hot” replicas biased by the PMFs associated with the corresponding dihedral angles, five more replicas biased by the scaled down PMFs (to serve as proxies for more efficient random walk between the replicas), and one not biased replica, from which we are going to “read” the equilibrium samples. We compute the (approximate) PMFs using ABMD. For the very first, rough approximation (initial condition for more demanding replica-exchange runs), we assume that the PMF for the :1@CH3 == :1@C == :2@N == :2@CA dihedral of the Ace(Pro)5 NHe is close to the same PMF computed for AceProNHe, and that the PMFs for the dihedrals between Pro residues in the Ace(Pro)5 NHe are close to the one computed for Ace(Pro)2 NHe dimer.

We begin with the monomer and proceed by doing two-stage “flooding”: starting with more aggressive (less equilibrium) and following it up with a smoother (closer to the equilibrium). Please find the corresponding files in the 4.hremd/1.preliminaries/0.monomer subdirectory of the supporting files archive. The “flooding” here means na´ve, linear grows of the biasing potential aimed to compensate for the true PMF. We refer the reader to our preprint for the details. Finally, we check the “quality” of the PMF by doing short equilibrium biased run (the files are in 0.monomer/3.umbrella). It turns out that the angle indeed explored all possible values in this short run. This implies that the biasing potential is within few kBT of the true PMF, as intended. If it turned out to be not the case, we would go with slower and slower flooding as long as needed.

In the next step we compute PMF for the :2@CA == :2@C == :3@N == :3@CA dihedral of Ace(Pro)2 NHe dimer following the same routine (the files in 4.hremd/1.preliminaries/0.dimer).

In order to equilibrate initial replica-exchange setup and to check whether those approximate PMFs computed using smaller molecules do approximate the true ones with the accuracy not worse than a few kBT, we ran a short replica-exchange simulation comprising ten replicas (five fully biased and five “proxies”). These files are located in 4.hremd/2.hremd.eq and we encourage the reader to examine them. We utilize the familiar multisander philosophy, yet use another (not mainstream) code path which is activated by the ncsu_bbmd section in the mdin files. Everything there should be self-explanatory (hit me by an e-mail, if it is not). This short run has revealed that the initial PMF do not approximate the true ones very well, as the values of the dihedrals are distributed very non-uniformly. To correct for that, we did a “flooding” run (4.hremd/3.hremd.flooding) with stationary “proxy” biasing potentials. We then checked the (presumably) more accurate PMFs by doing a short equilibrium run (4.hremd/4.hremd.umbrella) and proceeded to the production phase (in case we where unhappy with the PMFs quality, we would do more of slow “flooding”).

The “production” run is in 4.hremd/5.hremd.production. It comprises eleven replicas (five “hot”, five “proxies” and one “cold”) each ran for 50 ns. We try to exchange five random pairs of replicas every 91 step. The exchange statistics is reported in the exchange_log_file and turned out to be quite well.

Upon completion of the “production” run we got: (1) the monitor files from all eleven replicas: ten holding biased samples of the dihedral angles, and eleventh with the equilibrium samples of the COS_OF_DIHEDRAL collective variable; (2) unbiased configurations saved as the trajectory from the eleventh replica. We are going to analyze the trajectory, noting in passing that the biased samples can be used to improve the precision of the PMFs associated with the dihedral angles. We used ptraj to extract the values of the slow angles from the trajectory. It can be seen that the angles are indeed very sharply concentrated near the cis and trans values and hence they can be unambiguously classified as being either cis or trans. It is thus natural to use five-letter string consisting of T (for trans) and C (for cis) to describe each trajectory frame. A simple perl script (omegas.pl file in the scripts subdirectory of the supplementary archive) can be used to compare the frequencies of different conformations. We got that the TTTTT (all trans) configuration is indeed the most probable one (global minimum) with the probability of 33%. It is also possible to compute the free energy difference between the TTTTT and CCCCC states (that would correspond to the COS_OF_DIHEDRAL values of -5 and +5 respectively): we got 4.42 kcal/mol, which compares very favorably with the steered molecular dynamics described above.

References

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[4]   J. Weiser, P. S. Shenkin, and W. C. Still. Approximate Atomic Surfaces from Linear Combinations of Pairwise Overlaps (LCPO). J. Comp. Chem., 20:217–230, 1999.

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[7]   Ioan Kosztin, Bogdan Barz, and Lorant Janosi. Calculating potentials of mean force and diffusion coefficients from nonequilibrium processes without jarzynski’s equality. J. Chem. Phys., 124(6):064106, 2006.

[8]   C. H. Bennett. Efficient estimation of free energy differences from monte carlo data. J. Comp. Phys., 22:245–268, 1976.

[9]   C. Jarzynski. Nonequilibrium equality for free energy differences. Phys. Rev. Lett., 78(14):2690–2693, 1997.

[10]   K Hukushima and K Nemoto. Exchange monte carlo method and application to spin glass simulations. Journal of the Physical Society of Japan, 65:1604–1608, 1996.

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[12]   Thomas Huber, Andrew E. Torda, and Wilfred F. van Gunsteren. Local elevation: a method for improving the searching properties of molecular dynamics simulation. J. Comput. Aided. Mol. Des., 8:695–708, 1994.

[13]   A. Laio and M. Parrinello. Escaping free-energy minima. Proc. Natl. Acad. Sci., 99:12562–12566, 2002.

[14]   M. Iannuzzi, A. Laio, and M. Parrinello. Efficient exploration of reactive potential energy surfaces using car-parrinello molecular dynamics. Phys. Rev. Lett., 90:238302–1, 2003.