This project aims to explore the use of hybrid quantum-classical neural networks for the analysis and classification of protein sequences. Protein folding is a complex process, and understanding the intricacies of protein sequences can provide significant insights into biological functions and disease mechanisms. Traditional computational methods can be enhanced by quantum computing techniques, which may offer new avenues for solving problems in bioinformatics.
Protein folding is the process by which a protein structure assumes its functional shape or conformation. It is a crucial aspect of biological activity since the function of a protein is directly related to its 3D structure. Misfolded proteins can lead to diseases such as Alzheimer's, Parkinson's, and various prion diseases. Understanding protein folding requires a detailed analysis of protein sequences and their interactions, which is computationally challenging due to the vast conformational space.
Proteins are composed of amino acids, and their sequences determine their structure and function. Analyzing protein sequences involves identifying patterns, motifs, and domains that are critical for understanding their biological roles. This project leverages the power of hybrid quantum-classical models to analyze these sequences more effectively.
- It fetches protein sequences from the UniProt database using the REST API. The sequences are saved in FASTA format.
- The Sequences are extracted from the FASTA file, tokenized, and padded to a uniform length. This preprocessing is crucial for preparing the data for neural network training.
- The hybrid model combines classical LSTM layers with the quantum layer. This model is trained to classify protein sequences.
- The hybrid model is trained using protein sequences. Early stopping is employed to prevent overfitting.
- For comparison, a classical LSTM model is also built and benchmarked. Training time and memory usage are measured.
- The hybrid quantum-classical model and classical model are compared based on training time, memory usage, and accuracy.
- Insights into the potential benefits of quantum layers in neural networks for protein sequence analysis are discussed.
This project demonstrates the integration of quantum computing with classical neural networks for protein sequence analysis. While the hybrid model shows promise, further research and optimization are necessary to fully realize the benefits of quantum computing in bioinformatics.