AlphaFold and friends on the HPC

AlphaFold
Introduction to AlphaFold
2 Topics
DeepMind
Critical Assessment of protein Structure Prediction (CASP)
The AlphaFold pipeline
2 Topics
Overview of the architecture
AlphaFold training details
Online accessibility
3 Topics
Publications, GitHub code and database
Official AlphaFold colab
ColabFold
AlphaFold on the HPC
10 Topics
Requirements to use the HPC
Basic UNIX commands
Prepare directories and files
Setting up the job script
AlphaFold outputs
Computational limits
Basic workflow example (ssh)
Basic workflow example (web interface)
Troubleshooting
Extra: AlphaFold-Multimer
VIBFold on the HPC
3 Topics
VIBFold: an adapted AlphaFold script
Instructions for running VIBFold
Overview of VIBFold outputs
AlphaPulldown on the HPC
2 Topics
Introduction
Instructions for running AlphaPulldown
RoseTTAFold
The RoseTTAFold pipeline
Online Accessiblity
Exercises
VIB Training Session (AlphaFold)
10 Topics
HPC: A first AlphaFold run
HPC: AlphaFold-Multimer
HPC: Generate PAE/PLDDT/MSA plots
HPC: Analyze the predicted SarsCoV2-VHH.E complex
HPC: Run AlphaPulldown on the HPC
ChimeraX: Display AlphaFold predictions and error estimates using ChimeraX
AFDB: Analyze the prediction for USP7
Colab: ColabFold on Google Colab
ESM: Run ESMFold via web interface
(old) HPC: Analyze the predicted SarsCoV1-VHH.72 complex
Solutions
VIB Training Session (AlphaFold)
3 Topics
Analyze the prediction for USP7 on AlphaFoldDB (solutions)
Analyze the predicted SARS-CoV-2 + VHH-E complex (solutions)
(old) Analyze the predicted SARS-CoV-1 + VHH-72 complex (solutions)
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Basic workflow example (ssh)

AlphaFold and friends on the HPC AlphaFold on the HPC Basic workflow example (ssh)

A full instruction set for running a new protein through AlphaFold is described in this section.

1. Log in using your credentials (details)

2a. If running for the first time: set up directories

After this, copy the content of the job script to the nano program on the command line (details on how to work with nano), or copy it from your personal computer using WinSCP. Note that pasting in PuTTY is done with a right-click by default.

2b. If directories already set up: navigate to the alphafold/ directory

3. Create a new FASTA file

You can paste the contents inside the command line using right-click. More details on working with nano can be found here.

4. Modify the job script to use your FASTA file

5. Swap to the GPU cluster

6. Submit your job, check progress, wait
The state (Q) will change to R when running, F when failed, and C when completed (or crashed).
The standard output and error files are created in alphafold/.

7. Gather outputs

The outputs (more details) can be found in the newly created directory in runs/. Copy the results you need (mainly the ranked_0 to 5 .pdb files to your personal computer, using for instance WinSCP).

8. Generate visualizations

As described in an earlier topic, you can generate visualizations for this run using an extra python script. Load in the correct modules (AlphaFold and matplotlib) and run visualize_alphafold_results.py as shown below.

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