Drug Discovery Workflows for AWS HealthOmics

Description

A collection of AWS HealthOmics workflow examples to accelerate drug discovery.

Workflow Catalog

Modules

• Alphafold2-Monomer: From Google DeepMind. Predict the 3D structure of one or more single-chain proteins
• Alphafold2-Multimer: From Google DeepMind. Predict the 3D structure of multi-chain protein complexes.
• AMPLIFY Pseudo Perplexity: From Amgen and Mila. Calculate the pseudoperplexity of an amino acid sequence using a protein language model.
• Boltz-2: From MIT. Predict biomolecular structures containing combinations of proteins, RNA, DNA, and other molecules. Now supports MSA inference, including with paired alignments for protein complexes!
• BoltzGen: From MIT. All-atom generative model for designing proteins and peptides across all modalities to bind a wide range of biomolecular targets.
• Chai-1: From Chai Discovery. Predict the structure of biomolecule complexes including proteins, amino acids, and/or ligands.
• Colabfold-Search: Iterative MSA search algorithm using MMseqs2.
• ESMfold: From Meta. Rapidly predict protein structures using embeddings geneted by the ESM2 protein language model.
• Generate Protein Sequence Embeddings: From Meta. Generate ESM-2 vector embeddings for one or more protein amino acid sequences.
• TemStaPro: From Institute of Biotechnology, Life Sciences Center, Vilnius University. Predict protein thermostability using sequence representations from a protein language model.

Archived Workflows

The following workflows are provided in assets/workflows/archive for reference but are no longer maintained.

• ABodyBuilder3: From Exscientia. Predict the 3D structure of antibody heavy and light chains.

• Aggrescan3D: From University of Warsaw and Biologia Molecular Universitat Autònoma de Barcelona. Predict protein stability.

• AlphaBind: From A-Alpha Bio. Predict and optimize antibodu-antigen binding affinity.

• AntiFold: From Oxford Protein Informatics Group. Antibody inverse folding.

• BioNeMo NiM Protein Design Use BioNeMo NiM containers to design proteins using RFDifusion, ProteinMPNN, and AlphaFold-Multimer.

• BioPhi: From Merck. Automated humanization and humanness evaluation.

• DeepSTABp: From RPTU. Predict protein stability.

• Design Nanobodies: Generate de novo nanobody candidates against a given target protein structure and epitope using RFDiffusion, ProteinMPNN, ESMFold, AMPLIFY, and NanobodyBuilder2.

• DiffAb: From Helixon. Antigen-specific protein design.

• Efficient Evolution: From Stanford University. Rapid protein evolution

• EvoProtGrad: From NREL. Directed evolution on a protein sequence with gradient-based discrete Markov chain monte carlo (MCMC).

• EquiFold: From Prescient Design, a Genentech accelerator. Predict protein structures with an novel coarse-grained structure representation.

• Humatch: From Oxford Protein Informatics Group. Humanize antibodies.

• MMseqs2: From Max Planck Institute. Ultra fast and sensitive sequence search and clustering suite.

• NanobodyBuilder2: From Oxford Protein Informatics Group. Predict the 3D structure of single-chain nanobodies.

• OpenFold2: From Columbia University. Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2.

• PEP-Patch: From University of Innsbruck. Predict protein electrostatics.

• ProteinMPNN-ddG: From Pepton. Inverse folding model of protein stability.

• RFDiffusion-ProteinMPNN: From the Institute for Protein Design at the University of Washington. Generate protein backbone structures and sequences given a binding target or other structural context.

• ThermoMPNN: From the University of North Carolina School of Medicine. Predict changes in thermodynamic stability for protein point mutants.

Deployment

This repository contains Amazon CloudFormation templates and supporting resources to automatically deploy AWS HealthOmics private workflows into your AWS account. You are responsible for all costs associated with the deployed resources.

Quick Start

1. Clone this repository to your local environment.
2. Authenticate into your AWS account of interest and cd into the project dir.
3. Run the following command, replacing the placeholders with the name of a S3 bucket, desired stack name, and region:

bash scripts/deploy.sh \
  -b "my-deployment-bucket" \
  -n "my-aho-ddw-stack" \
  -r "us-east-1"

The CloudFormation deployment and asset build steps should finish in about 15 minutes. Once the deployment has finished, you can create a private workflow run using the Amazon HealthOmics console, CLI, or SDK.

Once the deployment has finished, you can create a private workflow run using the Amazon HealthOmics console, CLI, or SDK. You may re-run the ./deploy.sh script with the same arguments to update the CloudFormation stacks after code modifications to NextFlow scripts, Dockerfiles, or container build context directories are saved. This will trigger a rebuild and push of containers to ECR with the latest tag, and create new versions of the HealthOmics workflows.

To add a new module add the necessary files to the assets folder. There are three main components:

Many of the workflows in this repository require additional model weights or reference data. Please refer to the README files for each workflow in the workflows/ folder.

Third Party Credentials

Follow these steps to download data from third-party repositories:

1. Obtain an API key or other credential with the necessary access to the data.
2. Save the credentials in AWS Secrets Manager, for example:

aws secretsmanager create-secret \
    --name MyDataCredentials \
    --description "My data credentials." \
    --secret-string "{\"API_KEY\":\"MyFakeKey\",\"ORG\":\"myfakeorg\"}"

1. Add your data uri to a new file in the assets/data folder.
2. Run the deploy.sh script with the -s option and pass in your secret name (not the key or value) from step 1. CodeBuild will save these secret values as environment variables in the data download job.

Infrastructure Diagram

Development

To add a new module, fork the repository. There are three main components:

• Containers: contains the required information/data to build Docker images for specific tasks
• Data: contains links to parameters and other reference data used by workflow models
• Workflows: Specifc workflows, such as AlphaFold-Multimer that contain the main.nf script.

assets/
└──containers/
    ├── alphafold
    ├── biolambda
    └── ...
    data/
    ├── esm2.txt
    ├── esmfold.txt
    ├── rfdiffusion.txt
    └── ...
    workflows/
    ├── alphafold2/
    ├── alphafold-multimer/
    └── ...

The containers folder contains Dockerfiles and supporting files to build docker containers. The deployment process will attempt to use every subfolder here as a Docker build context without any further configuration. Right now, there are two types of containers provided by default.

The data folder contains .txt files that specify uris to download during stack creation. The deployment workflow will save the contents of each file in the following S3 locations:

Linting

You can lint this repositories NextFlow code using the AWS provided tool awslabs/linter-rules-for-nextflow, which has been been integrated with make:

make lint