previous episode

Running Molecular Dynamics on Alliance clusters with AMBER

next episode

Hands-on 5: Generating topologies and parameters for small molecules.

Overview

Teaching: 30 min
Exercises: 5 min
Questions

  • How to parameterize small molecules?

Objectives

  • Parameterize hexanediol

Generating topologies and parameters for small molecules.

ANTECHAMBER

Download hexanediol 3D structure

cd ~/scratch/workshop_amber/example_05
module purge
module load StdEnv/2023 openbabel
obabel Conformer3D_COMPOUND_CID_147023.sdf -O hexanediol.pdb
sed  "s/UNL/HEZ/g" hexanediol.pdb > HEZ.pdb

Make mol2 file with antechamber

module purge
module load StdEnv/2023 ambertools/23
antechamber -i HEZ.pdb -fi pdb -o HEZ.mol2 -fo mol2 -c bcc -s 2

The file HEZ.mol2 contains the definition of our HEZ residue including connectivity, all of the charges and atom types.

Run parmchk2 to find out if there are any missing force field parameters

parmchk2 -i HEZ.mol2 -f mol2 -o HEZ.frcmod

If it can antechamber will fill in these missing parameters by analogy to a similar parameter.

Create the library file for HEZ using tleap:

source leaprc.gaff2
HEZ=loadmol2 HEZ.mol2
check HEZ
saveoff HEZ hez.lib

Of course point charges are not very accurate because they are derived using semi-empirical method, but antechamber can also use results of gaussian QM calculations.

Deriving accurate point charges

User can derive charges using RESP and supply them in mol2 file.

Electrostatic Parameterization with py_resp.py

  1. QM Geometry Optimization (gaussian)
  2. Electrostatic Potential Calculation (gaussian)
  3. Convert the Gaussian ESP data format for PyRESP (ambertools:espgen)
  4. Generate input for py_resp.py (ambertools:pyresp_gen.py)
  5. RESP Parameterization (ambertools:py_resp.py)

Charge derivation methods

Activity 1: Derive RESP, CM5 and AM1-BCC2 (sqm) charges and compare them
Activity 2: Compare binding free energy calculated using different charge sets.

Comparison of Charge Derivation Methods Applied to Amino Acid Parameterization. - Derivation does not matter much for aminoacids?

Molecular Insights into the Covalent Binding of Zoxamide to the β-Tubulin of Botrytis cinerea - Some ligands for exercise on parameterization: carbendazim (CBZ), diethofencarb (DEF), zoxamide (ZOX)

AnteChamber PYthon Parser interfacE (ACPYPE)

ANTECHAMBER, a module of the AmberTools package, is the main tool for creating topological parameters in AMBER force fields. It can be used to generate topologies for most organic molecules.

ACPYPE - AnteChamber PYthon Parser interfacE.

Install ACPYPE:

module load StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 ambertools/23
virtualenv env-acpype
source env-acpype/bin/activate
pip install acpype

Create force field files:

acpype -i HDX.pdb -n 0

Free energy calculations

MMPBSA

Prepare tolopogies:

ante-MMPBSA.py -p ../../start.prmtop -s '!(:214-456,669-1029)' -c complex.prmtop
ante-MMPBSA.py -p complex.prmtop -n ':1-243' -l ligand.prmtop -r receptor.prmtop 

Input file for running PB and GB in serial
&general
   startframe=0, endframe=10, interval=1,
   keep_files=2, verbose=1, use_sander=1,
   strip_mask=!(:214-456,669-1029),
   ligand_mask=:1-243, receptor_mask=:244-604,
/
&gb
  igb=2, saltcon=0.150,
/

#!/bin/bash
#SBATCH --ntasks=10
#SBATCH --mem-per-cpu=4000M
#SBATCH --time=3:00:00
module purge
module load StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 ambertools/23
MMPBSA.py.MPI -O -i mmpbsa.in -o FINAL_RESULTS_MMPBSA.dat -sp ../../start.prmtop -cp complex.prmtop -rp receptor.prmtop -lp ligand.prmtop -y ../../../min/min.dcd

Memory requirements

Generalized Born: 3N (atom positions) + 2N (atom parameters) + data structures for evaluating the full energy. GB memory requirements should scale more or less linearly with the number of atoms in the system

Normal modes: slightly more than (3N * 3N)/2 to store Hessian matrix + data structures for evaluating the full energy. The major expense here is the N^2 scaling of the Hessian storage.

Poisson Boltzmann: the memory is dominated primarily by the grid, it depends strongly on the grid spacing.

3D-RISM: also requires a grid. The 3D-RISM grid needs to be denser than the corresponding grid for PB, so RAM requirements for 3D-RISM are typically a bit higher than PB.

Key Points

previous episode

next episode