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Profluent-Bio/protein2pam-cas9
protein2pam-cas9 is a machine learning model from Profluent-Bio. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
This repo contains Protein2PAM models and training data.
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From the Hugging Face model README
This repo contains Protein2PAM models and training data.
For Python API, see:
PAM prediction models for CRISPR-Cas nucleases.
| Model Name | Input Protein/Domain | CRISPR Type | Samples |
|---|---|---|---|
cas8 | Cas8 or Cas10d | Type I | 28,410 |
cas9 | Cas9 PI-domain | Type II | 15,843 |
cas12 | Cas12 protein | Type V | 1,720 |
| Model Name | Input Protein/Domain | CRISPR Type | Samples |
|---|---|---|---|
cas9_full | Cas9 protein | Type II | 15,843 |
cas9_full_nolit | Cas9 protein | Type II | 15,731 |
cas9_pid_nolit | Cas9 PI-domain | Type II | 15,731 |
cas9_pid_nme | Cas9 PI-domain | Type II | 15,843 |
cas12_no_lit | Cas12 protein | Type V | 1,675 |
Training data is stored in: Profluent-Bio/protein2pam-training-data
| Field name | Description | Data type | Example |
|---|---|---|---|
| cas_family | CRISPR-Cas family classification | string | Cas8 |
| source | Data source ('CRISPR-Cas Atlas' or Literature') | string | CRISPR-Cas Atlas |
| protein_id | Identifier for literature sequences | string or null | null |
| citation | Literature citation(s) | string or null | null |
| doi | DOI for the associated publication | string or null | null |
| protein_sequence | Full amino acid sequence | string | MTFMILQALYRY...NQN |
| pid_sequence | PAM-interacting domain sequence (Cas9 only) | string or null | null |
| pam_consensus | Consensus PAM sequence | string | TTC |
| pam_logo_acgt | ACGT-ordered numerical matrix for PAM logo | array of float arrays | [[0.005, 0.008, 0.007, 0.01], ...] |
| type | CRISPR-Cas type classification | string | Type I |
| subtype | CRISPR-Cas subtype or effector family | string | Cas8 |
Models and data are licensed under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0). You may share and adapt the models for non-commercial use with appropriate attribution.
If you use Protein2PAM in your research, please cite the following preprint:
Nayfach, S., Bhatnagar, A., Novichkov, A., et al. (2025). Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data. bioRxiv.