Quantum Annealers Yield Promising Results for Protein Research

Dr Sandipan Mohanty and his team found out that the quantum computers outperformed traditional ones in identifying the lowest energy structures.
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Indian-origin physicist Dr Sandipan Mohanty, presently working at the Jülich Supercomputing Centre in Germany, recently collaborated with scientists from Lund University in Sweden to use a quantum computer to examine protein folding. He has worked on molecular biology simulations for supercomputers for over 20 years.

The IIT-Kanpur graduate went on to get a PhD and Post Doctoral in theoretical physics from Lund University.

The aim of Mohanty’s research was to show the viability of quantum computers for non-trivial research questions in their field. The team specialised in Monte Carlo simulations which is a process based on statistical physics and stochastic sampling. 

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Read more: New Algorithms That Harnessed Protein-folding Power in 2022


The researchers used a simplified “HP” model on the 5000

qubit D-Wave Advantage quantum annealer, JUPSI, at the Jülich Supercomputing Centre of Research Centre Jülich, Germany. 

By maintaining only the bare minimum physical details necessary for the folding process and categorising amino acids into just two groups, hydrophobic (H) or polar (P), the so-called HP model greatly simplifies the problem.


The scientists discovered that the quantum annealer outperformed classical computers in identifying the lowest energy structures, with a success rate of 100% for all examined protein sequences, the longest consisting of 64 amino acids. 

For comparison, the researchers used the simulated annealing Monte Carlo (MC) method with the same HP model on classical computers. Although for infinitely long runs, classical MC simulations are guaranteed to find the ground state, in practice, simulations have to run for a finite amount of time. 

The researchers found a decreasing hit rate for successful annealing cycles with increasing system size. For 30 amino acids, the success rate for the classical runs was at 80%, and for the two longer sequences with 48 and 64 amino acids, it was considerably lower, with large uncertainties. The D-Wave hybrid annealer on the other hand, maintained a 100% success rate throughout.

More Research Avenues

The majority of quantum computers only have a few qubits, which makes it challenging to run simulations like those used in drug research. According to Mohanty, it will take two to three more generations of devices before we can run more intricate simulations.

However, this study represents an important first step in the use of quantum computers in biological research and it has the potential to improve our comprehension of diseases by ‘protein misfolding’.

Proteins form the crux of the human body. The capacity of a protein to attach to other molecules, conduct chemical processes, transport molecules across cell membranes, and carry out numerous other vital functions for life depends on its precise shape. If a protein folds incorrectly, it can lead to the formation of harmful structures called ‘misfolded proteins’, which can cause fatal diseases such as Alzheimer’s, Huntington’s, and cystic fibrosis.

Read more: Protein Wars Part 2: It’s OmegaFold vs AlphaFold

Shritama Saha
Shritama Saha is a technology journalist who is keen to learn about AI and analytics play. A graduate in mass communication, she is passionate to explore the influence of data science on fashion, drug development, films, and art.

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