license: mit
language:
- en
tags:
- biology
- protein
- peptide
- cell-penetrating-peptide
- esm-c
- protein-language-model
pipeline_tag: text-classification
library_name: pytorch
CPPro-600M
Open-weight cell-penetrating-peptide (CPP) classifier. Given a peptide (5-50 aa, canonical
residues) it returns P(CPP). A masked sequence-CNN head on frozen ESM-C 600M embeddings,
hard-negative-mining hardened, as a 5-seed ensemble. Runs locally, no API key.
Performance (v6 benchmark, computed on n=570)
| Metric | value |
|---|---|
| v6 held-out test MCC | 0.814 |
| AUROC | 0.974 |
| recall @0.5 (TPR) | 90.9% |
| false-positive rate @0.5 | 9.5% |
The v6 test is exact-disjoint from the published training data of pLM4CPPs and GraphCPP. On this same disjoint test, prior SOTA pLM4CPPs scores 0.652; the hosted CPPro-6B reaches ~0.87. Hard-negative mining lowers the false-positive rate vs the base head (screening is FP-sensitive).
Why a fresh benchmark? Prior CPP predictors report MCC 0.80-0.96 but train and test on the same CPPsite-2.0 lineage with identity-only dedup (in-distribution). On a strictly disjoint test those numbers don't hold (GraphCPP's own diverse benchmark drops to 0.58).
Usage
pip install "git+https://github.com/VicCar/CPP-Pro"
cppro-score --seq RRRRRRRRR
cppro-score --csv designs.csv --out scored.csv
A high per-seed std flags an out-of-distribution / unstable call. Best on realistic peptide sequences; synthetic homopolymers are out of distribution.
Files
seed0..4.pt (SeqCNN state dicts, in_dim=1152) + metrics.json.
Limitations
Canonical 20 amino acids only; no non-canonical residues or macrocyclisation. Lengths 5-50 aa.
License
MIT (head weights). ESM-C 600M backbone is EvolutionaryScale's, downloaded separately. Code: https://github.com/VicCar/CPP-Pro · Citation: CPPro (Carreira et al., in prep).