---
license: other
library_name: pytorch
tags:
  - biology
  - rna
  - protein
  - rbp
  - protein-rna-interaction
  - generative-model
  - classification
datasets:
  - sunlab-ai/ProRiboGen-dataset
---

# ProRiboGen — Inference Package

Inference-only package for a website / API backend. **No training or test CSVs.**

**ProRiboGen** has two modules:

1. **Generation** — protein amino-acid sequence → RNA FASTA + motifs (+ optional logos)
2. **Classification** — protein + RNA → binding probability `binding_prob ∈ (0, 1)`

Protein conditioning uses **VESM-3B** (weights live on your GPU server; not shipped here).

Package size ≈ **2.8 GB** (three checkpoints).

---

## Layout

```
ProRiboGen_code/
├── README.md                 # this file
├── requirements.txt
├── configs/
│   ├── paths.env.example     # copy → paths.env and edit VESM paths
│   ├── sample_fixed80bp.json
│   └── model.json            # classifier backbone / data config
├── checkpoints/
│   ├── generator.pt          # ~1.2G  RNA generation module
│   ├── backbone.pt           # ~1.2G  MLM backbone for classification
│   └── classifier.pt         # ~477M  binding classifier head + fusion
├── generator/                # Generation module (sample.py + src/ + tokenizer/)
├── classifier/               # Classification module (RnaRealismClassifierV3)
├── protein_encoder/          # AA sequence → VESM H5
├── motif/                    # MEME, HOMER conversion, logo PNGs
├── api/                      # CLI + FastAPI
├── examples/                 # demo protein FASTA
├── scripts/                  # smoke tests
└── workspace/                # runtime outputs (created on demand)
```

### Checkpoint roles

| File | Role |
|------|------|
| `generator.pt` | **Generation only** — MaskGIT+ sampling |
| `backbone.pt` | **Classification** — build ESM MLM, then load classifier weights |
| `classifier.pt` | **Classification** — binding / realism score |

Do **not** use `backbone.pt` for generation. Do **not** use `generator.pt` as the classifier backbone (the classifier was trained on the backbone run).

---

## Setup (another server)

### 1. Dependencies

```bash
cd ProRiboGen_code
python3 -m venv .venv && source .venv/bin/activate   # optional
pip install -r requirements.txt
# Motif discovery needs MEME Suite on PATH (`meme` command).
# Logos need: pip install logomaker matplotlib pandas
```

### 2. Configure VESM

```bash
cp configs/paths.env.example configs/paths.env
# edit configs/paths.env
```

Either:

```bash
# A) Standard 17-VESM3 tree
VESM_ROOT=/path/to/17-VESM3
# expects:
#   $VESM_ROOT/models/base/facebook_esm2_t36_3B_UR50D
#   $VESM_ROOT/models/weights/VESM_3B.pth
```

or:

```bash
# B) Explicit paths (overrides A)
VESM_BASE_MODEL_DIR=/path/to/facebook_esm2_t36_3B_UR50D
VESM_WEIGHTS=/path/to/VESM_3B.pth
DEVICE=cuda:0
```

Checkpoint / tokenizer relative paths in `paths.env` usually need no change.

### 3. Smoke tests

```bash
bash scripts/demo_generate.sh
bash scripts/demo_score.sh

# With a real protein FASTA from this package:
python api/generate_rna_and_motif.py \
  --protein @examples/HS90A_HUMAN_P07900.fasta \
  --num-sequences 32 --length-bp 80

python api/score_binding.py \
  --protein @examples/HS90A_HUMAN_P07900.fasta \
  --rna AUGCAUGCAUGCAUGCAUGCAUGCAUGCAUGC
```

---

## Module 1 — Generation (RNA + motifs)

```bash
python api/generate_rna_and_motif.py \
  --protein "MSKSLYVR..." \
  --p-id DemoRBP \
  --num-sequences 64 \
  --length-bp 80
```

Or:

```bash
python api/generate_rna_and_motif.py --protein @/path/to/protein.fasta --num-sequences 64
```

Outputs under `workspace/job_<p_id>/`:

| Path | Meaning |
|------|---------|
| `*_vesm3b.h5` | VESM token embeddings |
| `generated/*.fasta` | Generated RNA |
| `motifs/*/meme.txt` | MEME raw output |
| `homer_motif_matrix/*.motif` | HOMER PWMs |
| `homer_motif_logo/*.png` | Sequence logos (ACGU colors below) |

Skip MEME/logos: `--skip-motif`.

**Logo colors** (same as internal `create_motif_logo.ipynb`):

| Base | Hex |
|------|-----|
| A | `#65a455` |
| C | `#2e45a4` |
| G | `#fda562` |
| U | `#d54f3f` |

Standalone logos:

```bash
python motif/plot_homer_logos.py workspace/job_DemoRBP/homer_motif_matrix workspace/job_DemoRBP/homer_motif_logo
```

Website tip: use `num_sequences=64–256` for latency; paper-style runs use `2048`.

---

## Module 2 — Classification (binding probability)

```bash
python api/score_binding.py \
  --protein "MSKSLYVR..." \
  --rna "UGCAUGCGAU..." \
  --p-id DemoRBP
```

Example JSON:

```json
{
  "p_id": "DemoRBP",
  "rna": "UGCAUGCGAU...",
  "rna_len": 80,
  "logit": 1.23,
  "binding_prob": 0.77
}
```

Default decision threshold: `binding_prob >= 0.5` (same as training eval). Reuse a cached H5 with `--protein-h5` to skip re-encoding.

---

## HTTP API

```bash
uvicorn api.app:app --host 0.0.0.0 --port 8000
```

```bash
curl http://127.0.0.1:8000/health

curl -X POST http://127.0.0.1:8000/v1/generate \
  -H 'Content-Type: application/json' \
  -d '{"protein":"MSK...","p_id":"Demo","num_sequences":32,"run_motif":true}'

curl -X POST http://127.0.0.1:8000/v1/score \
  -H 'Content-Type: application/json' \
  -d '{"protein":"MSK...","rna":"UGCA...","p_id":"Demo"}'
```

Frontend only needs these two endpoints; GPU work stays on this service.

---

## Pipeline

```
AA sequence
    │
    ├─ VESM-3B ─► H5 [L, 2560]
    │
    ├─ generator.pt (generation) ─► FASTA ─► MEME ─► HOMER ─► PNG logos
    │
    └─ backbone.pt + classifier.pt + RNA ─► binding_prob (classification)
```

No pretrained 337-protein H5 is required; each request encodes VESM on the fly.

---

## Copy to another machine

```bash
rsync -avP ProRiboGen_code/ user@host:/path/ProRiboGen_code/
# or
tar -cf ProRiboGen_code.tar ProRiboGen_code
```

On the new host: install deps → edit `configs/paths.env` → run the smoke scripts.

---

## Hardware / notes

1. Prefer GPU ≥ 24 GB (VESM-3B + 12-layer ProRiboGen). Encode then generate/score can be split across steps.
2. Without MEME, use `--skip-motif` (sequences only).
3. `paths.env` is gitignored; ship `paths.env.example` only.
4. Python ≥ 3.10 recommended (`list[str]` typing in scripts).