Automated milestoning pipeline for kinetic and thermodynamic predictions

This repository provides an end-to-end automated pipeline for running milestoning simulations with machine-learned force fields to accelerate drug-target kinetic and thermodynamic predictions. The pipeline utilizes SEEKR2, SEEKRTools, and espaloma=0.3.2 to automate simulations.

Getting Started

To install and set up the necessary environment, run the following commands in sequence.

1. Create a new conda environment named 'one_step_kinetics' with Python 3.10

The --yes flag ensures it installs without prompting for confirmation.

conda create -n one_step_kinetics python=3.10 --yes

2. Activate the conda environment

Once the environment is created, activate the conda environment, switching the current shell session to use the new environment.

conda activate one_step_kinetics

3. Install mamba for faster dependency resolution

Mamba is a faster drop-in replacement for conda that speeds up package installations.

conda install conda-forge::mamba --yes

4. Install SEEKR2 plugin

This step installs OpenMM plugin for SEEKR2 package.

mamba install seekr2_openmm_plugin openmm=8.1 --yes

5. Verify SEEKR2 OpenMM plugin installation

Run the following command to check if the SEEKR2 OpenMM plugin is correctly installed. If no error message appears, the installation was successful.

python -c "import seekr2plugin"

6. Install Git

Git is required to clone repositories. Ensure it is installed using:

conda install conda-forge::git --yes

7. Install the SEEKR2 package

SEEKR2 is the core package required for performing milestoning simulations. We will download it from GitHub, install it, and verify that it works correctly.

Before proceeding, go to the home directory

cd ~

Clone the SEEKR2 repository from GitHub into the current directory

git clone https://github.com/seekrcentral/seekr2.git

Navigate into the seekr2 directory, where the cloned repository is located

cd seekr2

Install SEEKR2 using pip, making it accessible as a Python package in the conda environment

python -m pip install .

Run tests to verify that SEEKR2 has been installed correctly and is functioning as expected

pytest

Return to the home directory (~), ensuring a clean workspace before proceeding to the next steps

cd ~

8. Install the SEEKRTools package

SEEKRTools is a companion package to SEEKR2 that provides utilities for preparing and facilitating multiscale milestoning simulations.

Before proceeding, go to the home directory

cd ~

Clone the SEEKRTools repository from GitHub into the current directory

git clone https://github.com/seekrcentral/seekrtools.git

Navigate into the seekrtools directory where the cloned repository is located

cd seekrtools

Install SEEKRTools using pip, making it accessible as a Python package in the conda environment

python -m pip install .

Run tests to verify that SEEKRTools has been installed correctly and is functioning as expected

pytest

Return to the home directory (~), ensuring a clean workspace before proceeding to the next steps

cd ~

9. Install the OpenFF toolkit

The Open Force Field (OpenFF) Toolkit is required to assign and manipulate molecular mechanics parameters.

conda install conda-forge::openff-toolkit --yes

10. Install OpenEye toolkits

The OpenEye Toolkits are used for quantum chemistry-based force field parameterization. The OpenEye toolkits require a valid OpenEye academic license, free for academic users but must be obtained directly from https://www.eyesopen.com/academic-licensing.

Install OpenEye toolkits

Run the following command to install OpenEye toolkits via conda:

conda install openeye::openeye-toolkits --yes

Obtain and place the license file

After obtaining an OpenEye academic license, save the provided oe_license.txt file in a secure location on your system. For example, you may place it in:

/home/USERNAME/licenses/oe_license.txt

Add the license to your environment

To ensure that OpenEye toolkits can find the license file at runtime, export the license path by adding the following line to your ~/.bashrc.

export OE_LICENSE="/home/USERNAME/licenses/oe_license.txt"

Source ~/.bashrc

To apply this change immediately in the current terminal session, run:

source ~/.bashrc

11. Install OpenMM forcefields

OpenMM Force Fields provide additional parameter sets for molecular simulations using OpenMM.

conda install conda-forge::openmmforcefields --yes

12. Install espaloma machine-learned force field

Install espaloma version 0.3.2, which includes the latest parameterization models.

conda install conda-forge::"espaloma=0.3.2" --yes

13. Install BrownDye2

BrownDye2 is a package used for Brownian dynamics (BD) simulations, which are needed to compute association rate constants. If you plan to run BD simulations, follow these installation steps. Some of these steps require sudo privileges (administrator access). If you do not have sudo access, contact your system administrator.

Before proceeding, go to the home directory

cd ~

Install required dependencies

BrownDye2 requires several system libraries for compilation. Install them using:

sudo apt-get install libexpat1 make apbs liblapack-dev

sudo apt-get install ocaml ocaml-native-compilers

sudo apt-get install libexpat1-dev

Download the latest BrownDye2 source code

wget https://browndye.ucsd.edu/downloads/browndye2.tar.gz

Extract the downloaded archive

tar xvfz browndye2.tar.gz

Navigate into the browndye2 directory

cd browndye2

Compile the software

make -j 4 all

Return to the home directory

cd ~

Clean up unnecessary files

rm -rf browndye2.tar.gz

Instructions for setting up and running the automated milestoning pipeline

Once the conda environment is sett up with necessary package installations, please follow the step-by-step instructions on how to clone, navigate, and set up the milestoning simulation pipeline for kinetic and thermodynamic predictions.

1. First, clone the repository from GitHub

git clone https://github.com/anandojha/automated_milestoning_pipeline.git

2. Once the repository is cloned, change to the working directory to the project folder

cd automated_milestoning_pipeline

3. Navigate to the trypsin-benzamidine folder

cd trypsin_benzamidine

4. Activate the conda environment

conda activate one_step_kinetics

5. Export the OpenEye license

Replace "USERNAME" with the actual home directory name and make sure the oe_license.txt file is stored in the specified location.

export OE_LICENSE="/home/USERNAME/licenses/oe_license.txt"

Once the above steps are successful, read the README.md file in the trypsin_benzamidine project folder for further instructions on running the series of scripts.

There is a separate folder named trypsin_benzamidine_simulation, where these scripts are executed for comparison purposes. Note that these simulations are run for a very short time, meaning that the computed kinetic, thermodynamic, and rate constants may not accurately represent absolute experimental values. The purpose of this execution is to demonstrate the workflow and methodology, but actual simulations should be run for extended durations to obtain scientifically meaningful results. The users can compare results across different simulation times by increasing sampling duration and milestone transitions for better accuracy.

Relevant GitHub repositories

1. SEEKR2: https://github.com/seekrcentral/seekr2
2. SEEKR2 OpenMM Plugin: https://github.com/seekrcentral/seekr2_openmm_plugin
3. SEEKRTools: https://github.com/seekrcentral/seekrtools
4. QMrebind: https://github.com/seekrcentral/qmrebind

Relevant milestoning papers

1. Ojha, Anupam Anand, Lane William Votapka, Gary Alexander Huber, Shang Gao, and Rommie Elizabeth Amaro. "An introductory tutorial to the SEEKR2 (Simulation enabled estimation of kinetic rates v. 2) multiscale milestoning software [Article v1. 0]." Living Journal of Computational Molecular Science 5, no. 1 (2023): 2359-2359.
2. Votapka, Lane W., Andrew M. Stokely, Anupam A. Ojha, and Rommie E. Amaro. "SEEKR2: Versatile multiscale milestoning utilizing the OpenMM molecular dynamics engine." Journal of chemical information and modeling 62, no. 13 (2022): 3253-3262.
3. Ojha, Anupam Anand, Lane William Votapka, and Rommie Elizabeth Amaro. "QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations." Chemical Science 14, no. 45 (2023): 13159-13175.
4. Ojha, Anupam Anand, Ambuj Srivastava, Lane William Votapka, and Rommie E. Amaro. "Selectivity and ranking of tight-binding JAK-STAT inhibitors using Markovian milestoning with Voronoi tessellations." Journal of chemical information and modeling 63, no. 8 (2023): 2469-2482.
5. Votapka, Lane W., Benjamin R. Jagger, Alexandra L. Heyneman, and Rommie E. Amaro. "SEEKR: simulation enabled estimation of kinetic rates, a computational tool to estimate molecular kinetics and its application to trypsin–benzamidine binding." The Journal of Physical Chemistry B 121, no. 15 (2017): 3597-3606.
6. Jagger, Benjamin R., Anupam A. Ojha, and Rommie E. Amaro. "Predicting ligand binding kinetics using a Markovian milestoning with voronoi tessellations multiscale approach." Journal of Chemical Theory and Computation 16, no. 8 (2020): 5348-5357.
7. Jagger, Benjamin R., Christopher T. Lee, and Rommie E. Amaro. "Quantitative ranking of ligand binding kinetics with a multiscale milestoning simulation approach." The journal of physical chemistry letters 9, no. 17 (2018): 4941-4948.
8. Votapka, Lane W., and Rommie E. Amaro. "Multiscale estimation of binding kinetics using Brownian dynamics, molecular dynamics and milestoning." PLoS computational biology 11, no. 10 (2015): e1004381.

Authors and contributors

The following people have contributed directly to the coding and validation efforts of automated milestoning pipeline for kinetic and thermodynamic predictions (listed in alphabetical order of first name). The authors would like to thank everyone who has helped or will help improve this project by providing feedback, bug reports, or other comments.

1. Anupam A. Ojha, Flatiron Institute
2. Lane W. Votapka, UC San Diego
3. Rommie E. Amaro, UC San Diego
4. Sonya M. Hanson, Flatiron Institute