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Studies in autonomic, data-driven and industrial computing

A Hybrid Quantum Machine Learning for the Prediction of Protein Folding

Paridhi Chawhan, Ishita Singh

Published Oct 15, 2024• 0 citations
Publisher
DOI
10.1007/978-981-97-5862-3_2

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Protein folding is a computationally intensive problem in bioinformatics that involves predicting the three-dimensional structure of a protein from its amino acid sequence. Classical approaches face challenges due to the exponential complexity of the search space. In this paper, we propose Q-ProteinFold, a quantum-classical hybrid system that leverages the Variational Quantum Eigensolver (VQE) for protein folding approximation. The system encodes protein sequences into parameterized quantum circuits using Qiskit and explores multiple conformations through quantum superposition and entanglement. A classical optimizer iteratively minimizes the energy function to identify stable protein structures. The implementation is developed using Python, Streamlit, and Qiskit Aer simulator. Experimental results demonstrate energy convergence, probabilistic state distribution, and effective fold prediction. This work highlights the potential of quantum computing in solving complex biological optimization problems.

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This paper focuses on developing an innovative solution to the protein folding problem, a long-standing challenge in Bioinformatics, through the lens of quantum reinforcement learning (QRL). It proposes a hybrid method that incorporates a Quantum Neural Network created using a Parameterized Quantum Circuit. The study is conducted on the Hydropolar Model, a simplified representation of proteins in terms of their polarity, and the experiments are carried out in both 2D and 3D lattice representations. The datasets used for analysis include benchmark proteins from existing research and PDB or FASTA files from the Protein Data Bank. The core of the paper presents the detailed process of modeling and implementing the protein folding problem using QRL, along with detailed comparisons with classical deep reinforcement models inspired by the literature. The findings from this study demonstrate that QRL performs effectively in predicting energies close to optimal levels. However, it poses a greater challenge in terms of convergence when compared to classical methods. Besides the novel method proposed, we also present a supporting custom tool - the protein-folding-gym-utils python library - designed specifically to assist researchers in applying reinforcement learning techniques to the Protein Folding Problem.
Quantum Machine Learning (QML) holds immense potential in revolutionizing the prediction of protein structures, a critical challenge in computational biology. This research explores the application of quantum states, including superposition and entanglement, to capture the intricate and uncertain nature of protein conformations. Quantum gates and Fourier transforms are investigated as tools to manipulate and enhance quantum states, showcasing their ability to discern features essential for accurate predictions. The integration of hybrid quantum-classical models addresses the current limitations of quantum hardware, combining classical and quantum computing strengths. Quantum error correction is identified as a pivotal aspect for ensuring the reliability of predictions in the quantum domain. A numerical example is presented to illustrate the probabilistic nature of quantum states and the potential for obtaining optimized outcomes through quantum machine learning. The findings highlight the need for continued interdisciplinary collaboration between quantum physicists, computer scientists, and computational biologists to advance the field. While the exploration of QML for Protein Structure Prediction is in its early stages, the research emphasizes the transformative potential of quantum computing in unraveling the complexities of molecular structures.
Protein folding is a computationally intensive problem in bioinformatics that involves predicting the three-dimensional structure of a protein from its amino acid sequence. Classical approaches face challenges due to the exponential complexity of the search space. In this paper, we propose Q-ProteinFold, a quantum-classical hybrid system that leverages the Variational Quantum Eigensolver (VQE) for protein folding approximation. The system encodes protein sequences into parameterized quantum circuits using Qiskit and explores multiple conformations through quantum superposition and entanglement. A classical optimizer iteratively minimizes the energy function to identify stable protein structures. The implementation is developed using Python, Streamlit, and Qiskit Aer simulator. Experimental results demonstrate energy convergence, probabilistic state distribution, and effective fold prediction. This work highlights the potential of quantum computing in solving complex biological optimization problems.