Protein Sequence Generation - Protein Engineering

Problem Understanding

Introduction:
Proteins, vital for biological functions, are pivotal in drug discovery and biotechnology. Protein engineering manipulates sequences to achieve desired properties, necessitating the generation of novel, meaningful protein sequences.

Objective:
This project aims to develop a model generating accurate, diverse protein sequences. Leveraging machine learning, deep learning, or other computational approaches, it facilitates drug design and protein engineering.

Approaches:

1. Machine Learning & Deep Learning: Decipher sequence patterns.
2. Pre-trained Models: Fine-tune for sequence generation.

Usage

• Data Handling & Analysis: Preprocessing & Visualization.
• Methodology: Custom RNN Architecture & Pretrained Models.
• Mathematical & Logical Reasoning: Model optimization & Training.
• Generating Sequences: Sequence generation process & Results.
• Evaluation Metrics: Visualizing structures & pIDDT confidence.
• Future Scope: Model optimization, Hyperparameter Tuning, Advanced Architectures, Transfer Learning.
• Ongoing Research: Optimized RNN Model.

Demo Video

Demo.mp4

Requirements

• Python 3.x
• PyTorch
• Transformers
• Matplotlib
• NumPy

Acknowledgments

This project contributes to drug discovery and biotechnology advancements. Thanks to contributors in the field for their valuable resources.