Add Python bindings for peptide property prediction models

[Copilot]

Exposes RT, CCS, and MS2 prediction models to Python via PyO3 bindings, enabling Python users to leverage ReDeeM's deep learning models without reimplementation.

Implementation

New crate: redeem-properties-py

• RTModel: Retention time prediction (rt_cnn_lstm, rt_cnn_tf)
• CCSModel: Collision cross-section prediction (ccs_cnn_lstm, ccs_cnn_tf)
• MS2Model: MS2 fragment intensity prediction (ms2_bert)

Key features:

• Native NumPy array I/O via numpy crate
• Optional CUDA support through feature flag
• Maturin-based packaging for pip distribution

Usage

from redeem_properties_py import RTModel

model = RTModel("model.safetensors", "rt_cnn_lstm")
rt_predictions = model.predict(
    sequences=["PEPTIDE", "SEQUENCE"],
    mods=["", ""],
    mod_sites=["", ""]
)

CCS and MS2 models follow the same pattern with additional charge state, NCE, and instrument parameters as needed.

Structure

crates/redeem-properties-py/
├── Cargo.toml          # PyO3 dependencies
├── pyproject.toml      # Maturin build config
├── README.md           # Python-specific docs
└── src/lib.rs          # Python bindings (410 LOC)

Dependencies: PyO3 0.22, numpy 0.22, Python 3.8+

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Original prompt

I want to create a python crate that exposes some of the peptide property prediction models so that they could be used in python.

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