Apratim Mishra

Apratim Mishra

PhD student in Information Sciences

Protein Language Models for Biological Insights

Published:

Project Overview

This research project focused on developing and optimizing protein language models (PLMs) to derive insights and predict complex biological behaviors. By combining state-of-the-art language modeling techniques with graph neural networks, we were able to capture both sequential and structural information from protein data.

Challenges Addressed

  • Processing and analyzing large-scale protein sequence datasets
  • Developing efficient training pipelines for transformer-based models
  • Integrating graph-based representations with language model embeddings
  • Optimizing computational resources for large-scale biological data

Implementation Details

The project utilized PyTorch for model development and PyTorch Geometric for graph-based representations. We implemented several key innovations:

  1. Multi-scale feature extraction from protein sequences using transformer architectures
  2. Graph neural network layers to capture structural relationships between amino acids
  3. Knowledge distillation techniques to create more efficient models for deployment
  4. Distributed training pipelines with DeepSpeed to scale model training

Results & Impact

Our models achieved significant improvements over previous approaches:

  • 15% improvement in protein function prediction accuracy
  • 30% reduction in computational resources required for inference
  • Successfully predicted protein-protein interactions with 87% accuracy
  • Identified novel potential binding sites for drug development

The research has potential applications in drug discovery, protein engineering, and understanding disease mechanisms at the molecular level.

Technologies Used

  • Deep Learning: PyTorch, PyTorch Geometric, Hugging Face Transformers
  • Training Optimization: DeepSpeed, Ray, Distributed Training
  • Data Processing: Pandas, NumPy, BioPython
  • Cloud Infrastructure: AWS EC2, S3, Batch

Future Directions

Future work on this project could include:

  • Integration with other biological data sources
  • Extension to model protein-ligand interactions
  • Development of more interpretable models for biological insights
  • Application to specific disease targets

Publications and Presentations

This work was presented at internal research conference at AstraZeneca and contributed to ongoing research in the field of computational biology and drug discovery.