Molecular Simulation with OpenMM and Bacalhau

Introduction

In this tutorial example, we will showcase how to containerize an OpenMM workload so that it can be executed on the Bacalhau network and take advantage of the distributed storage & compute resources. OpenMM is a toolkit for molecular simulation. It is a physic-based library that is useful for refining the structure and exploring functional interactions with other molecules. It provides a combination of extreme flexibility (through custom forces and integrators), openness, and high performance (especially on recent GPUs) that make it truly unique among simulation codes.

In this example tutorial, our focus will be on running OpenMM molecular simulation with Bacalhau.

Prerequisite​

To get started, you need to install the Bacalhau client, see more information here

Running Locally​

Downloading Datasets​

We use a processed 2DRI dataset that represents the ribose binding protein in bacterial transport and chemotaxis. The source organism is the Escherichia coli bacteria.

Protein data can be stored in a .pdb file, this is a human-readable format. It provides for the description and annotation of protein and nucleic acid structures including atomic coordinates, secondary structure assignments, as well as atomic connectivity. See more information about PDB format here. For the original, unprocessed 2DRI dataset, you can download it from the RCSB Protein Data Bank here.

The relevant code of the processed 2DRI dataset can be found here. Let's print the first 10 lines of the 2dri-processed.pdb file. The output contains a number of ATOM records. These describe the coordinates of the atoms that are part of the protein.

head ./dataset/2dri-processed.pdb

Expected Output
    REMARK   1 CREATED WITH OPENMM 7.6, 2022-07-12
    CRYST1   81.309   81.309   81.309  90.00  90.00  90.00 P 1           1 
    ATOM      1  N   LYS A   1      64.731   9.461  59.430  1.00  0.00           N  
    ATOM      2  CA  LYS A   1      63.588  10.286  58.927  1.00  0.00           C  
    ATOM      3  HA  LYS A   1      62.707   9.486  59.038  1.00  0.00           H  
    ATOM      4  C   LYS A   1      63.790  10.671  57.468  1.00  0.00           C  
    ATOM      5  O   LYS A   1      64.887  11.089  57.078  1.00  0.00           O  
    ATOM      6  CB  LYS A   1      63.458  11.567  59.749  1.00  0.00           C  
    ATOM      7  HB2 LYS A   1      63.333  12.366  58.879  1.00  0.00           H  
    ATOM      8  HB3 LYS A   1      64.435  11.867  60.372  1.00  0.00           H  

Writing the Script​

To run the script above all we need is a Python environment with the OpenMM library installed. This script makes sure that there are no empty cells and to filter out potential error sources from the file.

# Import the packages
import os
from openmm import *
from openmm.app import *
from openmm.unit import *

# Specify the input files
input_path = 'inputs/2dri-processed.pdb'
if not os.path.exists(input_path):
    raise FileNotFoundError(f"Input file not found: {input_path}")

# Function to check and filter PDB file lines
def filter_valid_pdb_lines(input_path, output_path):
    with open(input_path, 'r') as infile, open(output_path, 'w') as outfile:
        lines = infile.readlines()
        for i, line in enumerate(lines):
            if line.startswith("ATOM") or line.startswith("HETATM"):
                if len(line) >= 54:
                    try:
                        float(line[30:38].strip())
                        float(line[38:46].strip())
                        float(line[46:54].strip())
                        outfile.write(line)
                    except ValueError:
                        print(f"Skipping line {i + 1} because it has invalid coordinates: {line.strip()}")
                else:
                    print(f"Skipping line {i + 1} because it is too short: {line.strip()}")
            else:
                outfile.write(line)

# Filter PDB file
filtered_pdb_path = 'inputs/filtered_2dri-processed.pdb'
filter_valid_pdb_lines(input_path, filtered_pdb_path)

# Load the filtered PDB file
try:
    pdb = PDBFile(filtered_pdb_path)
except ValueError as e:
    print(f"ValueError while reading filtered PDB file: {e}")
    raise

forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')

# Output
output_path = 'outputs/final_state.pdbx'
if not os.path.exists(os.path.dirname(output_path)):
    os.makedirs(os.path.dirname(output_path))

# System Configuration
nonbondedMethod = PME
nonbondedCutoff = 1.0 * nanometers
ewaldErrorTolerance = 0.0005
constraints = HBonds
rigidWater = True
constraintTolerance = 0.000001
hydrogenMass = 1.5 * amu

# Integration Options
dt = 0.002 * picoseconds
temperature = 310 * kelvin
friction = 1.0 / picosecond
pressure = 1.0 * atmospheres
barostatInterval = 25

# Simulation Options
steps = 10
equilibrationSteps = 0
# platform = Platform.getPlatformByName('CUDA')
platform = Platform.getPlatformByName('CPU')
# platformProperties = {'Precision': 'single'}
platformProperties = {}
dcdReporter = DCDReporter('trajectory.dcd', 1000)
dataReporter = StateDataReporter('log.txt', 1000, totalSteps=steps,
                                 step=True, time=True, speed=True, progress=True, elapsedTime=True, remainingTime=True,
                                 potentialEnergy=True, kineticEnergy=True, totalEnergy=True, temperature=True,
                                 volume=True, density=True, separator='\t')
checkpointReporter = CheckpointReporter('checkpoint.chk', 1000)

# Prepare the Simulation
print('Building system...')
topology = pdb.topology
positions = pdb.positions
system = forcefield.createSystem(topology, nonbondedMethod=nonbondedMethod, nonbondedCutoff=nonbondedCutoff,
                                 constraints=constraints, rigidWater=rigidWater, ewaldErrorTolerance=ewaldErrorTolerance,
                                 hydrogenMass=hydrogenMass)
system.addForce(MonteCarloBarostat(pressure, temperature, barostatInterval))
integrator = LangevinMiddleIntegrator(temperature, friction, dt)
integrator.setConstraintTolerance(constraintTolerance)
simulation = Simulation(topology, system, integrator, platform, platformProperties)
simulation.context.setPositions(positions)

# Minimize and Equilibrate
print('Performing energy minimization...')
simulation.minimizeEnergy()
print('Equilibrating...')
simulation.context.setVelocitiesToTemperature(temperature)
simulation.step(equilibrationSteps)

# Simulate
print('Simulating...')
simulation.reporters.append(dcdReporter)
simulation.reporters.append(dataReporter)
simulation.reporters.append(checkpointReporter)
simulation.currentStep = 0
simulation.step(steps)

# Write a file with the final simulation state
state = simulation.context.getState(getPositions=True, enforcePeriodicBox=system.usesPeriodicBoundaryConditions())
with open(output_path, mode="w+") as file:
    PDBxFile.writeFile(simulation.topology, state.getPositions(), file)
print('Simulation complete, file written to disk at: {}'.format(output_path))

Running the Script​

python run_openmm_simulation.py

This is only done to check whether your Python script is running. If there are no errors occurring, proceed further.

Uploading the Data to IPFS​

The simplest way to upload the data to IPFS is to use a third-party service to "pin" data to the IPFS network, to ensure that the data exists and is available. To do this, you need an account with a pinning service like Pinata or nft.storage. Once registered, you can use their UI or API or SDKs to upload files.

When you pin your data, you'll get a CID. Copy the CID as it will be used to access your data

Containerize Script using Docker​

To build your own docker container, create a Dockerfile, which contains instructions to build your image.

See more information on how to containerize your script/app here

FROM conda/miniconda3

RUN conda install -y -c conda-forge openmm

WORKDIR /project

COPY ./run_openmm_simulation.py /project

LABEL org.opencontainers.image.source https://github.com/bacalhau-project/examples

CMD ["python","run_openmm_simulation.py"]

Build the container​

We will run docker build command to build the container:

docker build -t <hub-user>/<repo-name>:<tag> .

Before running the command, replace:

hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created

repo-name with the name of the container, you can name it anything you want

tag this is not required but you can use the latest tag

In our case, this will be:

docker buildx build --platform linux/amd64 --push -t ghcr.io/bacalhau-project/examples/openmm:0.3 .

Push the container​

Next, upload the image to the registry. This can be done by using the Docker hub username, repo name, or tag.

docker push <hub-user>/<repo-name>:<tag>

Run a Bacalhau Job​

Now that we have the data in IPFS and the docker image pushed, we can run a job on the Bacalhau network.

export JOB_ID=$(bacalhau docker run \
    --input ipfs://bafybeig63whfqyuvwqqrp5456fl4anceju24ttyycexef3k5eurg5uvrq4 \
    --wait \
    --id-only \
    ghcr.io/bacalhau-project/examples/openmm:0.3 \
    -- python run_openmm_simulation.py)

Structure of the command​

Lets look closely at the command above:

1. bacalhau docker run: call to Bacalhau

2. bafybeig63whfqyuvwqqrp5456fl4anceju24ttyycexef3k5eurg5uvrq4: here we mount the CID of the dataset we uploaded to IPFS to use on the job

3. ghcr.io/bacalhau-project/examples/openmm:0.3: the name and the tag of the image we are using

4. python run_openmm_simulation.py: the script that will be executed inside the container

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Checking the State of your Jobs​

Job status: You can check the status of the job using bacalhau job list.

bacalhau job list --id-filter=${JOB_ID} --no-style

When it says Published or Completed, that means the job is done, and we can get the results.

Job information: You can find out more information about your job by using bacalhau job describe.

bacalhau job describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

rm -rf results && mkdir -p results
bacalhau job get ${JOB_ID} --output-dir results # Download the results

Viewing your Job Output​

To view the file, run the following command:

cat results/outputs/final_state.pdbx

Support​

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).