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https://github.com/aws-samples/awesome-protein-analysis-on-aws

A curated list of resources for protein analysis on AWS
https://github.com/aws-samples/awesome-protein-analysis-on-aws
List: awesome-protein-analysis-on-aws
Last synced: 3 months ago
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A curated list of resources for protein analysis on AWS
Host: GitHub
URL: https://github.com/aws-samples/awesome-protein-analysis-on-aws
Owner: aws-samples
License: mit-0
Created: 2022-10-21T21:40:16.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2023-08-09T14:42:48.000Z (about 1 year ago)
Last Synced: 2024-04-11T22:02:23.174Z (7 months ago)
Homepage:
Size: 237 KB
Stars: 19
Watchers: 7
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: readme.md
- Changelog: CHANGELOG.md
- Contributing: contributing.md
- License: LICENSE
- Code of conduct: code-of-conduct.md
Awesome Lists containing this project

ultimate-awesome - awesome-protein-analysis-on-aws - A curated list of resources for protein analysis on AWS. (Other Lists / PowerShell Lists)
README

        # Awesome Protein Analysis on AWS [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

> A curated list of awesome resources for protein analysis on AWS.

## Contents

- [1. Overview](#1-overview)

- [2. Resources by Workflow Type](#2-resources-by-workflow-type)

- [2.1. Resources for Sequence Homology Search](#21-resources-for-sequence-homology-search)

- [2.2. Resources for Sequence Property Prediction](#22-resources-for-sequence-property-prediction)

- [2.3. Resources for Structure Prediction](#23-resources-for-structure-prediction)

- [2.4. Resources for Experimental Analysis](#24-resources-for-experimental-analysis)

- [2.5. Resources for Molecular Dynamics Simulations](#25-resources-for-molecular-dynamics-simulations)

- [2.6. Resources for Protein Design](#26-resources-for-protein-design)

- [2.7. Other Resources](#27-other-resources)

- [3. Content Creators](#3-content-creators)

- [4. Contribute](#4-contribute)

## 1. Overview

Knowing the physical structure of proteins is an important part of both small molecule and biologics drug R&D. Machine learning algorithms like DeepMind’s AlphaFold can significantly reduce the cost and time required to generate usable protein structures. These high-profile projects have inspired increased development of AI-driven workflows for protein structure prediction, de novo protein design, and protein-ligand interaction analysis. AWS pharmaceutical and life science customers believe that these in silico tools can help reduce the cost and time required to bring new therapeutics to market.

AWS technical teams have developed solutions to common protein analysis problems. These use a variety of services to best fit the needs of our customers.

---

## 2. Resources by Workflow Type

:newspaper: Blog Post  

:hammer: Architecture  

:books: Customer Story  

### 2.1. Resources for Sequence Homology Search

- 2022 :hammer: [AWS Batch Architecture for JackHMMER/HHBlits](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

---

### 2.2. Resources for Sequence Property Prediction

- 2024-03-18 :newspaper: [Protein language model training with NVIDIA BioNeMo framework on AWS ParallelCluster](https://aws.amazon.com/blogs/hpc/protein-language-model-training-with-nvidia-bionemo-framework-on-aws-parallelcluster/)

- 2024-03-11 :newspaper: [Find the Next Blockbuster with NVIDIA BioNeMo Framework on Amazon SageMaker](https://aws.amazon.com/blogs/industries/find-the-next-blockbuster-with-nvidia-bionemo-framework-on-amazon-sagemaker/)

- 2024-03-6 :newspaper: [Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/efficiently-fine-tune-the-esm-2-protein-language-model-with-amazon-sagemaker/)

- 2021-06-10 :newspaper: [Exploring the UniProt protein knowledgebase with AWS Open Data and Amazon Neptune](https://aws.amazon.com/blogs/industries/exploring-the-uniprot-protein-knowledgebase-with-aws-open-data-and-amazon-neptune/)

- 2021-05-27 :newspaper: [Fine-tune and deploy the ProtBERT model for protein classification using Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/fine-tune-and-deploy-the-protbert-model-for-protein-classification-using-amazon-sagemaker/)

---

### 2.3. Resources for Structure Prediction

- 2023-11-29 :books: [AWS and Accenture Help Merck Use Cloud Technology to Reduce Drug Discovery Time and Accelerate Clinical Trial Development](https://press.aboutamazon.com/2023/11/aws-and-accenture-help-merck-use-cloud-technology-to-reduce-drug-discovery-time-and-accelerate-clinical-trial-development)

- 2023-07-31 :newspaper: [Build protein folding workflows to accelerate drug discovery on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/build-protein-folding-workflows-to-accelerate-drug-discovery-on-amazon-sagemaker/)

- 2023-07-05 :newspaper: [Running protein structure prediction at scale using a web interface for researchers](https://aws.amazon.com/blogs/hpc/running-protein-structure-prediction-at-scale-using-a-web-interface-for-researchers/)

- 2023-06-20 :newspaper: [Protein Structure Prediction at Scale using AWS Batch](https://aws.amazon.com/blogs/hpc/protein-structure-prediction-at-scale-using-aws-batch/)

- 2023-05-09 :newspaper: [Accelerate protein structure prediction with the ESMFold language model on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/accelerate-protein-structure-prediction-with-the-esmfold-language-model-on-amazon-sagemaker/)

- 2023-01-30 :hammer: [AlphaFold2 Webapp on AWS](https://github.com/aws-samples/alphafold-protein-structure-prediction-with-frontend-app)

- 2022-10-25 :newspaper: [Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS](https://aws.amazon.com/blogs/machine-learning/run-inference-at-scale-for-openfold-a-pytorch-based-protein-folding-ml-model-using-amazon-eks/)

- 2022-10-04 :newspaper: [Optimize Protein Folding Costs with OpenFold on AWS Batch](https://aws.amazon.com/blogs/hpc/optimize-protein-folding-costs-with-openfold-on-aws-batch/)

- 2022-04-18 :newspaper: [Predicting protein structures at scale using AWS Batch](https://aws.amazon.com/blogs/industries/predicting-protein-structures-at-scale-using-aws-batch/)

- 2022 :hammer: [AWS Batch Architecture for AlphaFold 2.0](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2022 :hammer: [AWS Batch Architecture for AlphaFold-Multimer](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2022 :hammer: [AWS Batch Architecture for OpenFold](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2022 :hammer: [AWS EKS Architecture For OpenFold Inference](https://github.com/aws-samples/aws-do-openfold-inference)

- 2022 :hammer: [AWS Batch Architecture for ESMFold](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2022 :hammer: [AWS Batch Architecture for OmegaFold](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2021-11-05 :newspaper: [Run AlphaFold v2.0 on Amazon EC2](https://aws.amazon.com/blogs/machine-learning/run-alphafold-v2-0-on-amazon-ec2/)

- 2021 :hammer: [AWS Batch Architecture for RoseTTAFold](https://github.com/aws-samples/aws-rosettafold)

---

### 2.4. Resources for Protein Language Model Training

- 2024-05-01 :newspaper: [Accelerate drug discovery with NVIDIA BioNeMo Framework on Amazon EKS](https://aws.amazon.com/blogs/hpc/accelerate-drug-discovery-with-nvidia-bionemo-framework-on-amazon-eks/)

- 2024-03-18 :newspaper: [Protein language model training with NVIDIA BioNeMo framework on AWS ParallelCluster](https://aws.amazon.com/blogs/hpc/protein-language-model-training-with-nvidia-bionemo-framework-on-aws-parallelcluster/)

- 2024-03-11 :newspaper: [Find the Next Blockbuster with NVIDIA BioNeMo Framework on Amazon SageMaker](https://aws.amazon.com/blogs/industries/find-the-next-blockbuster-with-nvidia-bionemo-framework-on-amazon-sagemaker/)

- 2024-03-6 :newspaper: [Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/efficiently-fine-tune-the-esm-2-protein-language-model-with-amazon-sagemaker/)

---

### 2.5. Resources for Experimental Analysis

- 2022-11-21 :newspaper: [Building Digitally Connected Labs with AWS](https://aws.amazon.com/blogs/industries/building-digitally-connected-labs-with-aws/)

- 2022 :books: [Vertex Pharmaceuticals Reduces Costs of Cryo-EM Data Storage and Processing by 50% Using AWS](https://aws.amazon.com/solutions/case-studies/vertex-case-study/)

- 2022-09-21 :newspaper: [Japan’s High Energy Accelerator Research Organization, KEK, accelerates search for new vaccines with AWS](https://aws.amazon.com/blogs/publicsector/japans-high-energy-accelerator-research-organization-kek-accelerates-search-new-vaccines-aws/)

- 2022-07-12 :newspaper: [How Thermo Fisher Scientific Accelerated Cryo-EM using AWS ParallelCluster](https://aws.amazon.com/blogs/hpc/how-thermo-fisher-scientific-accelerated-cryo-em-using-aws-parallelcluster/)

- 2022-01-17 :newspaper: [CelerisTx: Drug Discovery for Incurable Diseases with ML on AWS](https://aws.amazon.com/blogs/startups/celeristx-drug-discovery-for-incurable-diseases-with-ml-on-aws/)

- 2022 :books: [ENPICOM Supports Faster Insights for Therapeutic Antibody Discovery Using AWS](https://aws.amazon.com/solutions/case-studies/ENPICOM-case-study/?did=cr_card&trk=cr_card)

- 2022 :hammer: [Cryo-EM on AWS ParallelCluster](https://github.com/aws-samples/cryoem-on-aws-parallel-cluster)

- 2021-08-17 :newspaper: [Stion – a Software as a Service for Cryo-EM data processing on AWS](https://aws.amazon.com/blogs/hpc/stion-a-saas-for-cryo-em-data-processing-on-aws/)

- 2019-10-30 :newspaper: [Structural biologists learning cryo-electron microscopy can access educational resources powered by AWS](https://aws.amazon.com/blogs/publicsector/structural-biologists-learning-cryo-electron-microscopy-have-new-educational-resources-powered-by-aws/)

- 2019-10-29 :newspaper: [Building a GPU-enabled CryoEM workflow on AWS](https://aws.amazon.com/blogs/industries/building-a-gpu-enabled-cryoem-workflow-on-aws/)

---

### 2.6. Resources for Molecular Dynamics Simulations

- 2022-06-09 :newspaper: [Running cost-effective GROMACS simulations using Amazon EC2 Spot Instances with AWS ParallelCluster](https://aws.amazon.com/blogs/hpc/running-gromacs-on-spot-with-checkpointing/)

- 2022-02-16 :newspaper: [GROMACS performance on Amazon EC2 with Intel Ice Lake processors](https://aws.amazon.com/blogs/hpc/gromacs-performance-on-amazon-ec2-with-intel-ice-lake-processors/)

- 2021-11-02 :newspaper: [Running 20k simulations in 3 days to accelerate early stage drug discovery with AWS Batch](https://aws.amazon.com/blogs/hpc/running-20k-simulations-in-3-days-with-aws-batch/)

- 2021-10-14 :newspaper: [Running GROMACS on GPU instances: multi-node price-performance](https://aws.amazon.com/blogs/hpc/running-gromacs-on-gpu-instances-multi-node-price-performance/)

- 2021-10-13 :newspaper: [Running GROMACS on GPU instances: single-node price-performance](https://aws.amazon.com/blogs/hpc/running-gromacs-on-gpu-instances-single-node-price-performance/)

- 2021-10-12 :newspaper: [Running GROMACS on GPU instances](https://aws.amazon.com/blogs/hpc/running-gromacs-on-gpu-instances/)

- 2021-03-31 :newspaper: [GROMACS price-performance optimizations on AWS](https://aws.amazon.com/blogs/hpc/gromacs-price-performance-optimizations-on-aws/)

- 2020-11-25 :newspaper: [A new key to unlocking drug discovery](https://aws.amazon.com/blogs/publicsector/new-key-unlocking-drug-discovery/)

- 2020-11-02 :newspaper: [Crowdsourcing a cure for COVID-19: How the cloud and Folding@home are accelerating research and drug discovery](https://aws.amazon.com/blogs/publicsector/crowdsourcing-cure-covid-19-cloud-accelerating-research-drug-discovery/)

- 2020-08-14 :newspaper: [Folding@home infectious disease research with Spot Instances](https://aws.amazon.com/blogs/compute/foldinghome-infectious-disease-research-with-spot-instances/)

---

### 2.7. Resources for Protein Design

- 2024-06-25 :newspaper: [Revolutionizing Generative Biology with AWS and EvolutionaryScale](https://aws.amazon.com/blogs/industries/revolutionizing-generative-biology-with-aws-and-evolutionaryscale/)

- 2023-05-10 :hammer: [Perform Antibody Structure Prediction and Design](https://catalog.workshops.aws/hcls-aiml/en-US/protein-analysis)

- 2023-04-23 :newspaper: [How Evolvere Biosciences performs macromolecule design on AWS](https://aws.amazon.com/blogs/hpc/how-evolvere-biosciences-performs-macromolecule-design-on-the-aws-cloud/)

- 2022 :hammer: [AWS Batch Architecture for ProteinMPNN](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

- 2022 :hammer: [AWS Batch Architecture for RFDesign](https://github.com/aws-samples/aws-batch-arch-for-protein-folding)

---

### 2.8. Other Resources

- 2022-10-12 :newspaper: [BioContainers are now available in Amazon ECR Public Gallery](https://aws.amazon.com/blogs/hpc/biocontainers-are-now-available-in-amazon-ecr-public-gallery/)

- 2021-08-16 :newspaper: [Menten AI Leverages AWS, AI, and Quantum Computing to Create New and Improved Drugs](https://aws.amazon.com/blogs/startups/menten-ai-leverages-aws-ai-quantum-computing-to-create-drugs/)

- 2021 :books: [Powering Moderna’s digital biotechnology platform to develop new vaccines](https://aws.amazon.com/solutions/case-studies/moderna-machine-learning/?did=cr_card&trk=cr_card)

- 2020 :books: [Relay Therapeutics Uses AWS to Accelerate Drug Discovery](https://aws.amazon.com/solutions/case-studies/relay-therapeutics/)

## 3. Content Creators

- [Ariyawansa, Shamika](https://github.com/shamika)

- Atnoor, Deven

- Bhatkar, Swapnil

- [Bollig, Evan](https://github.com/bollig)

- [Broyde, Joshua](https://github.com/JoshB29)

- Carter, Maggie

- Cherian, Austin

- [Chiyoda, Shingo](https://github.com/shingochiyoda)

- Cianfrocco, Michael

- Deshpande, Aniket

- Eng, Edward

- Enigbokan, Olajide

- Greene, Eric

- Grewal, Jasleen 

- [Hsieh, Michael](https://github.com/michaelhsieh42)

- [Ishio, Chiaki](https://github.com/chiaki-i)

- Joshi, Vaijayanti

- Kadyan, Sachin

- [Khanuja, Mani](https://github.com/mani-aiml)

- Kitzmiller, Grace

- Kniep, Christian

- Kumbadakone, Shubha

- [Kuriyama, Daiki](https://github.com/kuridaik)

- [Loyal, Brian](https://github.com/brianloyal)

- Malhotra, Sudhanshu

- [Miyamoto, Daisuke](https://github.com/DaisukeMiyamoto)

- [Ozturk, Doruk](https://github.com/dorukozturk)

- [Patel, Shivam](https://github.com/shimpat)

- Pamulapati, Santhan

- Peven, Ben

- [Pizarro, Angel](https://github.com/delagoya)

- Poonawala, Hasan

- Rapp, Micah

- [Schreckengaust, Scott](https://github.com/scottschreckengaust)

- Shi, Yuan

- [Skjerven, Brian](https://github.com/skjerven)

- [Smith, Sean](https://github.com/sean-smith)

- [Srivastava, Ankur](https://github.com/awsankur)

- Tessler, Lee

- Trummer Christopher

- Wang, Qi

- Weber, Noah

- [White, Natalie](https://github.com/natalie-white-aws)

- Wu, Angela

- Wood, Matt

- [Xu, Rafa](https://github.com/aaaaatoz)

---

## 4. Contribute

Contributions welcome! Read the [contribution guidelines](contributing.md) first.