Fine-Tuning ProGen2 Protein Language Model

Fine-tuned ProGen2 transformer model on PFAM protein families to generate biologically valid sequences. Achieved 93%+ motif retention rates through targeted hyperparameter optimization and attention mechanism analysis.

Overview

This project demonstrates transfer learning applied to protein sequence generation, fine-tuning a large pre-trained transformer model (ProGen2) on specific protein families to generate novel sequences while preserving critical biological motifs.

Technical Implementation

Model Training:

• Fine-tuned ProGen2 on three PFAM families (PF00257, PF00069, PF00072) using GPU cluster
• Built both single-family and multi-family models achieving 20% and 25% average sequence identity
• Training time: ~3 hours per epoch on distributed GPUs

Optimization:

• Reduced perplexity from 1.72 to 1.30 on PF00257 through hyperparameter tuning
• Systematic exploration of learning rates, batch sizes, and warmup schedules

Validation:

• Applied attention heatmap visualization to understand learned dependencies
• HMMer analysis confirmed 93.9% motif retention for single-family model
• Cross-family validation showed 93% retention for PF00072

Key Technologies

• PyTorch for model implementation
• Hugging Face Transformers for ProGen2 architecture
• GPU cluster computing for distributed training
• HMMer for biological sequence validation

Impact

Successfully demonstrated that careful fine-tuning of large language models can generate biologically plausible protein sequences, with validation metrics confirming preservation of critical functional motifs.