| --- |
| license: apache-2.0 |
| tags: |
| - biology |
| - protein-function-prediction |
| - cafa5 |
| - gene-ontology |
| - esm2 |
| --- |
| |
| # CAFA 5 Protein Function Prediction Models |
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| This repository contains trained models, configurations, and inference scripts developed for multi-label Gene Ontology (GO) protein function prediction, aligned with the **CAFA 5** tournament guidelines. |
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| ## 📋 Table of Contents |
| 1. [Overview](#overview) |
| 2. [Model Artifacts & Architectures](#model-artifacts--architectures) |
| * [`esm2_ffn` (ESM-2 + Feed-Forward Network Head)](#esm2_ffn-esm-2--feed-forward-network-head) |
| * [`blast_knn` (BLAST & K-Nearest Neighbors Baseline)](#blast_knn-blast--k-nearest-neighbors-baseline) |
| * [`lr3mer` (Logistic Regression 3-mer Baseline)](#lr3mer-logistic-regression-3-mer-baseline) |
| 3. [Input & Output Schemas](#input--output-schemas) |
| 4. [Quick Usage Example](#quick-usage-example) |
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| ## 1. Overview |
| Predicting protein functions across Gene Ontology (GO) domains (Biological Process, Molecular Function, and Cellular Component) is crucial for functional annotation pipelines. This repository hosts multiple modeling approaches—ranging from deep learning embeddings (`esm2_ffn`) to sequence homology and baseline approaches (`blast_knn`, `lr3mer`)—to generate standardized functional predictions (`EntryID`, `term`, `score`). |
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| ## 2. Model Artifacts & Architectures |
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| ### `esm2_ffn` |
| A hybrid model combining a frozen protein language model encoder with a custom feed-forward classification head. |
| * **Base Model Encoder:** [`facebook/esm2_t33_650M_UR50D`](https://huggingface.co/facebook/esm2_t33_650M_UR50D) (1,280 feature dimensions). |
| * **Training Hardware:** Tesla T4 GPU. |
| * **Training Stats:** Evaluated across 141,995 training proteins and 141,864 test proteins, generating over 200 million prediction rows. |
| * **Head Configuration:** |
| * Hidden Dimension: 512 |
| * Dropout: 0.2 |
| * Learning Rate: 0.001 |
| * Weight Decay: 0.0001 |
| * Positive Weight Cap: 50.0 |
| |
| ### `blast_knn` |
| A sequence-similarity and neighborhood-based baseline utilizing sequence alignment scores and k-nearest neighbors over annotation spaces. |
| * **Configuration:** Configured via `blast_knn/config.json` and tracked with `blast_knn/manifest.json`. |
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| ### `lr3mer` |
| A lightweight sequence-based baseline utilizing 3-mer frequency representations coupled with multi-label logistic regression classifiers. |
| * **Configuration:** Backed by sequence k-mer tracking matrices and `lr3mer/go_terms.json`. |
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| ## 3. Input & Output Schemas |
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| * **Required Input Fields:** |
| * `EntryID`: Unique identifier for the protein sequence. |
| * `sequence`: Amino acid sequence string. |
| * **Prediction Output Schema:** |
| * `EntryID`: Protein identifier. |
| * `term`: Gene Ontology term ID (e.g., `GO:0009987`). |
| * `score`: Confidence score ranging between $0.0$ and $1.0$. |
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| ## 4. Quick Usage Example |
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| You can inspect, download, or execute inference for the `esm2_ffn` model bundle directly using the provided `inference.py` script: |
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| ```python |
| import torch |
| from pathlib import Path |
| from esm2_ffn.inference import load_model, resolve_model_dir |
| |
| # Load model bundle from the Hub |
| model_dir = resolve_model_dir("sasudo2/cafa5_models", subfolder="esm2_ffn") |
| print(f"Model bundle resolved at: {model_dir}") |
| |
| # Example inference script execution |
| # python esm2_ffn/inference.py --model-dir {model_dir} --input proteins.fasta |
