A Qiskit implementation of a quantum algorithm for protein structure prediciton in 2D hydrophobic-hydrophilic model. This notebook contains the accompanying code for the paper in [1].
As of June 29, 2023, this notebook has been released by AWS on their GitHub repository platform as well as on the AWS Solutions Library: Quantum Computing Exploration for Drug Discovery on AWS.
Work is currently in progress on the accompanying code for [2].
For simplicity, this notebook requires the user to change manually the variable j specifying the expected energy (= number of hydrophobic-hydrophobic contacts in the lattice). The present value of j is 1 as this is the expected energy level for the amino acid sequence PHPPH (encoded as 01001).
This notebook is based on the theory described in [1].
The code was written by Renata Wong (https://renatawong.github.io/).
This work benefited greatly from discussions with Prof. Weng-Long Chang (National Kaohsiung University of Science and Technology) and Dr. Aoyu Zhang (AWS). All remaining deficiencies are my own.
[1] R. Wong and W-L. Chang. Fast quantum algorithm for protein structure prediction in hydrophobic-hydrophilic model, Journal of Parallel and Distributed Computing 164:178-190, 2022, DOI: 10.1016/j.jpdc.2022.03.011, https://www.sciencedirect.com/science/article/abs/pii/S0743731522000673.
[2] R. Wong and W-L. Chang. Quantum speedup for protein structure prediction, IEEE Transactions on Nanobioscience 20(3): 323-330, 2021. DOI: 10.1109/TNB.2021.3065051, https://ieeexplore.ieee.org/document/9374469.