griffithlab / griffithlab/pVACtools
Investigate adding pMHCchat as an additional binding prediction tool
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- Dominant language
- Python
- Stars
- 188
- Forks
- 81
- Avg merge
- 9d 17h
- Merged PRs (30d)
- 6
Description
pMHCchat uses a deep learning framework to allow MHC class II - peptide interaction predictions.
I think it outputs binding affinity score and probability of binding but not percentile, so we may need to take that into account to determine how we want to interpret their score.
In their own benchmarking studies, the tool outperformed DeepMHCII and STMHCpan.
GitHub: https://github.com/jianiM/pMHChat
Publication: Ma J, Wang Z, Tong C, Yang Q, Zhang L, Liu H. pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning. Brief Bioinform. 2025;26(4):bbaf321. doi:10.1093/bib/bbaf321
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the pMHChat GitHub repository and the cited publication to understand its inputs, outputs, and benchmarking claims. The investigation is done when pVACtools has a clear feasibility and integration plan, including how its affinity and binding-probability scores should be interpreted and whether percentile handling is required.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100