Aims

The aim of this tutorial is to train users to learn the syntax of complex collective variables and use them to analyze MD trajectories of realistic biological systems and bias them with metadynamics.

Objectives

Once this tutorial is completed students will be able to:

Write the PLUMED input file to use complex CVs for analysis
Analyze trajectories and calculate the free energy of complex biological systems
- Perform error analysis and evaluate convergence in realistic situations
Setup, run, and analyze metadynamics simulations of a complex system

Resources

The reference trajectories and input files for the exercises proposed in this tutorial can be downloaded from github using the following command:

wget https://github.com/plumed/trieste2017/raw/master/tutorial/data/tutorial_6.tgz

This tutorial has been tested on a pre-release version of version 2.4. However, it should not take advantage of 2.4-only features, thus should also work with version 2.3.

Introduction

In this tutorial we propose exercises on the following biological systems:

the BRCA1-associated RING domain protein 1 (BARD1 complex)
the cmyc peptide in presence of urea at low concentration (cmyc-urea)
the protein G B1 domain
The exercise are of increasing difficulties, inputs are partially provided for the first and second cases while for the last one the user is expected to be autonomous.

Exercise 1: analysis of the BARD1 complex simulation

The BARD1 complex is a heterodimer composed by two domains of 112 and 117 residues each. The system is represented at coarse-grained level using the MARTINI force field.

The BARD1 heterodimer

In the TARBALL of this exercise, we provide a long MD simulation of the BARD1 complex in which the two domains explore multiple different conformations.

Note: We encourage the users to get familiar with the system by visualizing the MD trajectory using VMD.

The users are expected to:

calculate the values of different CVs on the trajectory
estimate the free energies as a function of the CVs tested (mono- and multi-dimensional)
extracting from the trajectories the configurations corresponding to relevant free-energy minima
calculate the error in the associated free energy using the block analysis technique
evaluate the convergence of the original trajectory

The users are free to choose his/her favorite CVs and they are encouraged to use the on-line manual to create their own PLUMED input file. However, we encourage all the users to experiment at least with the following CVs:

1) RMSD and/or DRMSD from the native state

The following line can be added to the plumed.dat file to calculate the RMSD on the beads representing the backbone aminoacids:

rmsd: RMSD REFERENCE=bard1_rmsd.pdb TYPE=OPTIMAL

while this one can be used to calculate the DRMSD between chains:

drmsd: DRMSD REFERENCE=bard1_drmsd.pdb TYPE=INTER-DRMSD LOWER_CUTOFF=0.1 UPPER_CUTOFF=1.5

2) Number of inter-chains contacts (specific, i.e. native, or all).

This can be achieved with the COORDINATION CVs, as follows:

# backbone beads index for chain A
chainA: GROUP ATOMS=1,3,5,7,9,10,12,15,17,19,21,23,25,27,29,31,33,34,36,38,41,43,45,47,49,51,53,55,57,59,61,63,66,68,70,72,74,76,79,81,83,87,89,93,95,98,102,104,106,108,111,113,115,117,119,122,125,126,128,130,132,134,136,138,140,143,145,147,149,151,154,157,159,161,163,165,167,169,172,176,178,180,182,184,186,188,190,192,195,197,199,201,202,206,208,210,212,214,215,217,219,223,224

# backbone beads index for chain B
chainB: GROUP ATOMS=226,228,230,232,234,235,238,239,240,245,246,250,252,255,256,257,259,261,264,266,268,271,273,275,278,280,282,285,287,289,291,293,295,298,300,302,304,306,308,309,310,312,314,318,320,324,326,328,330,332,334,336,338,340,342,343,345,346,348,350,352,354,358,360,362,363,368,370,372,374,376,379,381,383,386,388,390,392,394,396,398,400,402,404,406,409,411,414,416,418,420,424,426,428,430,432,434

coord: COORDINATION GROUPA=chainA GROUPB=chainB NOPBC R_0=1.0

3) A CV describing the relative orientation of the two chains.

This can be achieved, for example, by defining suitable virtual atoms with the CENTER keyword and the TORSION CV:

# virtual atom representing the first half of chain A
chainA_1: CENTER ATOMS=__FILL__
# virtual atom representing the second half of chain A
chainA_2: CENTER ATOMS=__FILL__

# virtual atom representing the first half of chain B
chainB_1: CENTER ATOMS=__FILL__
# virtual atom representing the second half of chain B
chainB_2: CENTER ATOMS=__FILL__

# torsion CV
dih: TORSION ATOMS=__FILL__

Exercise 2: analysis of the cmyc-urea simulation

Cmyc is a small disordered peptide made of 11 aminoacid. In solution, cmyc adopts a variety of different, but equally populated conformations. In the TARBALL of this exercise, we provide a long MD simulation of cmyc in presence of a single molecule of urea.

The cmyc peptide in presence of one urea molecule

Note: We encourage the users to get familiar with the system by visualizing the MD trajectory using VMD.

The users are expected to:

characterize the conformational ensemble of cmyc by calculating free-energies as a function of different CVs.
calculate the fraction of bound and unbound molecules of urea by defining suitable CVs to measure the position of urea relative to cmyc.
find the cmyc aminoacids that bind urea the most and the least.
calculate the ensemble averages of different experimental CVs.

Warning: Be careful that the original trajectory might be broken by PBC!

The users are free to choose his/her favorite CVs and they are encouraged to use the on-line manual to create their own PLUMED input file. However, we encourage all the users to experiment at least with the following CVs to characterize the conformational landscape of cmyc:

1) the radius of gyration of cmyc (GYRATION)

2) the content of alpha (ALPHARMSD) and beta (ANTIBETARMSD) secondary structure

The fraction of bound and unbound molecules of urea can be computed after evaluating the minimum distance among all the distances between heavy atoms of cmyc and urea, as follows:

# cmyc heavy atoms 
cmyc: GROUP ATOMS=5,6,7,9,11,14,15,17,19,22,24,26,27,28,30,32,34,38,41,45,46,47,49,51,54,56,60,64,65,66,68,70,73,75,76,77,79,81,83,87,91,92,93,95,97,100,103,104,105,108,109,110,112,114,118,119,120,122,124,127,130,131,132,133,134,135,137,139,142,145,146,147,148,149,150,152,154,157,160,161,162,165,166,167,169,171,174,177,180,183,187,188

# urea heavy atoms
urea: GROUP ATOMS=192,193,194,197

# minimum distance cmyc-urea 
mindist: DISTANCES GROUPA=cmyc GROUPB=urea MIN={BETA=50.}

For estimating the cmyc aminoacid that bind the most and the least urea, we leave the users the choice of the best strategy.

For the calculation of ensemble averages of experimental CVs, we suggest to use:

1) 3J scalar couplings (JCOUPLING)

2) the FRET intensity between termini (FRET)
and we encourage the users to look at the examples provided in the manual for the exact syntax.

Exercise 3: Protein G folding simulations

GB1 is a small protein domain with a simple beta-alpha-beta fold. It is a well studied protein that folds on the millisecond time scale. Here we use a structure based potential and well-tempered metadynamics to study the free energy of folding and unfolding. In the TARBALL of this exercise we provide the files needed to run the simulation, the user should write the plumed input file needed to bias the sampling.

The crystal structure of the protein G B1 domain

The users are expected to:

setup and perform a well-tempered metadynamics simulation
evaluate convergence and error calculation of the metadynamics simulation
calculate the free energy difference between the folded and unfolded state of this protein
evaluate the robustness of the former by reweighting the resulting free energy as function of different CVs

The users are free to choose his/her favorite CVs and they are encouraged to use the on-line manual to create their own PLUMED input file. However, we encourage all the users to experiment at least with the following CVs to characterize the free-energy landscape of GB1:

RMSD with respect to the folded state
GYRATION
ALPHABETA
DIHCOR

The users should select two of them for the METAD simulation. Once you are satisfied by the convergence of your simulation, you can use one of the reweighting algorithms proposed to evaluate the free-energy difference between folded and unfolded state as a function of multiple collective variables.

#this allows you to use short-cut for dihedral angles
MOLINFO STRUCTURE=GB1_native.pdb

#add here the collective variables you want to bias

#add here the METAD bias, remember that you need to set: one SIGMA per CV, one single HEIGHT, one BIASFACTOR and one PACE. 
#Using GRIDS can increase the performances, so set a as many GRID_MIN and GRID_MAX as the number of CVs with reasonable ranges 
#(i.e an RMSD will range between 0 and 3, while ALPHABETA and DIHCOR will range between 0 and N of dihedrals). 

#add here the printing

Conclusions

In summary, in this tutorial you should have learned how to use PLUMED to:

Analyze trajectories of realistic biological systems using complex CVs
Extract conformations that correspond to local free-energy minima
Apply block analysis to estimate error in the reconstructed free-energy profiles
Calculate ensemble averages of experimental CVs
Reweight well-tempered metadynamics simulations