Protein Engineering Server

Use cases:

1. Input a protein, and get a probability distribution of amino acids at each position (masked prediction). 1a. Following from this, predict most likely variant
2. Input a protein, and get the latent representation of it (representation).
3. Visualize protein, and labeled variant.

Deployment:

graph TB
  F["Frontend (Deployment)"] -- "Job Request" --> KM["Kubernetes Manager"]
  KM -- "Create Job" --> K["Kubernetes Cluster"]
  K -- "Run Job (Pod with ML script)" --> P["Pod with T4 GPU"]
  F -- "Input File" --> P
  P -- "Run ML Inference" --> R["Results"]
  R -- "Return Results" --> F

Loading

1. Frontend: Kubernetes Deployment
2. Backend (Kubernetes Manager): Google Cloud Functions
3. ML Inference: Kubernetes Job
4. File communication: Google Cloud Buckets

Progress

Structure

• Architecture Diagram

Frontend

• frontend input
• [] Add File icon to input box once FASTA file has been inputted
• [] mock output for frontend
• [] probability distribution viz, protein 3d viz for frontend
• [] create new bucket with input files
• [] call Kubernetes Job Manager, include link to new bucket for application I/O
• [] upload preference inference data to viz

Inference

• test running ESMFold on GCP instance

• test running ESMFold with GPUs on GCP instance

• ESM optimization using sharding not worth it, running into issues loading the sharded model. I think the model is already sharded from HF since it's 15GB

• packaging ESM into a dockerfile

• [] loading ESM dockerfile on GKE with reserved instances

• [] saving files from ESM GKE

• [] communicating files to frontend from GKE backend

• [] ? emailing users of results

• [] ESM masked probability task on jupyter notebook

• [] add ESM masked probability to Docker image

Backend (Kubernetes Job Manager)

• [] read up on python kubernetes
• [] spin up test Kubernetes job manager