---
license: apache-2.0
library_name: pytorch
tags:
  - protein
  - protein-protein-interaction
  - esm2
  - bernett
  - biology
---

# LoGoPPI Bernett

LoGoPPI is a protein–protein interaction prediction model built on ESM-2. It combines a Global sequence representation with a residue-level Maxsim score. The two calibrated scores are combined with fixed weights of 0.5 and 0.5.

## Model overview

This checkpoint was trained independently on the Bernett human PPI benchmark. It does not load or continue training from the LoGoPPI Cross-species checkpoint.

## Training and evaluation data

The data originate from the [Bernett human PPI benchmark, Figshare version 3](https://doi.org/10.6084/m9.figshare.21591618.v3), which is based on high-confidence human interactions from HIPPIE v2.3 and uses leakage-controlled dataset construction.

The `data/bernett/` directory contains `proteins.fasta` and `query,text,label` CSV files for training, checkpoint selection, calibration, and testing.

| Split | Pairs |
|---|---:|
| Training | 163,192 |
| Validation selection | 29,630 |
| Validation calibration | 29,630 |
| Test | 52,048 |

## Results

| Metric | Value |
|---|---:|
| AUPR | 0.684319 |
| AUROC | 0.701470 |
| NLL | 0.632073 |
| F1 | 0.673916 |

## Usage

Install [LoGoPPI v2.0.0](https://github.com/netbiolab/LoGoPPI/tree/v2.0.0) and download the model files:

```bash
git clone https://github.com/netbiolab/LoGoPPI.git
cd LoGoPPI
conda env create -f environment.yml
conda activate logoppi

python - <<'PY'
from huggingface_hub import snapshot_download

snapshot_download(
    "netbiolab/LoGoPPI-Bernett",
    revision="v2.0.0",
    local_dir="models/bernett",
    ignore_patterns=["data/*"],
)
PY
```

Run inference with a FASTA file and a `query,text` pair CSV:

```bash
python inference.py \
  --model_dir models/bernett \
  --fasta_path proteins.fasta \
  --pair_csv pairs.csv \
  --output_path predictions.csv \
  --gpus 0
```

Training, testing, sequence-only input, and embedding reuse are documented in the [GitHub repository](https://github.com/netbiolab/LoGoPPI).

## License and citation

LoGoPPI is released under the Apache License 2.0. Please cite the accompanying LoGoPPI study and the [Bernett benchmark](https://doi.org/10.1093/bioinformatics/btaf192) when using this model or the distributed data.