Quantum Computer Solves Protein Folding Problem

 Quantum Computing

In a new study, D-Wave CEO Geordie Rose and other D-Wave researchers have teamed up with Harvard quantum physicist Alán Aspuru-Guzik and post-doc Alejandro Perdomo-Ortiz to demonstrate that the D-Wave One system can solve the challenging task of finding the lowest-energy configuration of a folded protein.

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128-qubit D-Wave One quantum computer has solved the puzzle of how certain proteins fold, Nature News Blog reports.

The finding from a Harvard’s Alan Aspuru-Guzik and his colleagues shows that the D-Wave One could predict the lowest-energy configurations of a folded protein. 

A cornerstone of computational biophysics, lattice protein folding models provide useful insight into the energy landscapes of real proteins. Understanding these landscapes, and how real proteins fold into the shapes that help give them their function, is an extremely difficult problem for today's computers to solve.

The model consisted of mathematical representations of amino acids in a lattice, connected by different interaction strengths. The D-wave computer found the lowest configurations of amino acids and interactions, which corresponds to the most economical folding of the proteins. 

The study, “Finding low-energy conformations of lattice protein models by quantum annealing,” is published in a recent issue of Nature’s Scientific Reports. 

The D-Wave One quantum computer (which bears more than a passing resemblance to the monolith) consists of 128 superconducting quantum bits or ‘qubits’. The computer works on the principle of quantum annealing. Essentially it involves preparing some sub-group of the qubits into their lowest-possible energy state, or “ground state,” and then performing a series of operations to put it into a more complex ground state that can’t be easily solved using classical methods.
If it sounds complicated, it is; so much so that some scientists have questioned D-Wave’s claims in the past. More recently, however, the company has been able to prove that its computer is working as claimed.

The computer used quantum annealing to find the lowest-energy protein configuration by solving for the configuration as an optimization problem, where the optimal state was the lowest-energy state. Proteins can be folded in a large number of ways because they’re made up of many chains of amino acids. Yet somehow, proteins almost always manage to fold themselves in the correct configuration (when they don’t fold correctly, they can cause misfolded-protein diseases such as Alzheimer's, Huntington's, and Parkinson's). 

Scientists think that proteins fold themselves correctly because the correct configuration is also the state of lowest energy, the state at which the protein becomes stable. In quantum annealing, the system starts by randomly picking a starting state, and then selecting random neighbor states to see if they have lower energies than the starting state. If they do, the computer replaces the original state with the lower-energy state. 

The process is considered quantum because it involves quantum tunneling to explore the different states by traveling directly through certain barriers rather than climbing over them. In this way, quantum annealing differs from the classical version, called “simulated annealing,” which explores different states based on temperature. 

Previous research has shown that quantum annealing has advantages over simulated annealing in some situations. In this study, the researchers showed that D-Wave One - which has the distinction of being the first commercial quantum annealer - can solve some simple protein folding problems by annealing all the way to the ground state. The problems here only contain a small number of amino acids, so they have only a relatively small number of possible configurations, and can still be solved on a classical computer. 

Also, the quantum technique has low odds of measuring the ground state, with only 13 out of 10,000 measurements yielding the desired solution. The researchers attribute this low percentage in part to the limitations of the machine itself, and in part to thermal noise that disrupted the computation. Nevertheless, the study provides the first quantum-mechanical implementation of protein models using a quantum computer.

“Harnessing quantum-mechanical effects to speed up the solving of classical optimization problems is at the heart of quantum annealing algorithms (QA),” the researchers wrote in their study. “There is theoretical and experimental evidence of the advantage of solving classical optimization problems using QA, over its classical analogue (simulated annealing). 

In QA, quantum mechanical tunneling allows for more efficient exploration of difficult potential energy landscapes such as that of classical spin-glass problems. In our implementation of lattice folding, quantum fluctuations (tunneling) occurs between states representing different model protein conformations or folds.” The study also lends more support to the quantum nature of the D-Wave One, since the system behaved exactly as expected if quantum aspects were contributing. The researchers see even more to look forward to in the future. “The approach employed here can be extended to treat other problems in biophysics and statistical mechanics, such as molecular recognition, protein design, and sequence alignment,” they wrote.
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Dr. Alejandro Perdomo-Ortiz, the lead author of the paper, stated that: "Knowing that we can use real quantum computers to solve hard problems in biology is an exciting and important result. The techniques developed in this report can also be used to tackle other biophysical problems such as molecular recognition, protein design, and sequence alignment."

SOURCE  PhysOrg

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