D-Wave goes public with 81-qubit protein modeling
All together now: ‘It’s quantum, innit?’
D-Wave – whose claims to have a working quantum computer have been met with skepticism and major contracts in equal measure – has published a paper in Nature in which it demonstrates the application of quantum annealing to protein folding analysis.
Protein folding is a difficult problem in the classical world, because of the vast number of possible solutions. As D-Wave’s authors put it in their paper (online in full here): “Finding low-energy threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model.”
In nature, proteins should normally fold themselves to a “ground state” – the lowest possible energy configuration for that particular combination of amino acids – because the low-energy state is the most stable. When folding goes wrong in humans, it can result in a range of diseases like Alzheimer’s, Huntington’s and Parkinsons.
However, predicting the “correct” folding for any given protein on a computer is difficult and time-consuming – so much so that scientists have found that crowd-sourcing using the game FoldIt can get results where supercomputers don’t.
D-Wave’s paper claims to demonstrate the first application of quantum principles to solving protein folding. It’s only been performed on a small scale – using 81 qubits – and is intended as a benchmark.
Moreover, the authors state that the scale of the problem they’ve demonstrated would still be solvable using a classical computer. “Even though the cases presented here still can be solved on a classical computer by exact enumeration (the six-amino-acid problem has only 40 possible configurations), it is remarkable that the device anneals to the ground state of a search space of 281 possible computational outcomes. This study provides a proof-of-principle that optimization of biophysical problems such as protein folding can be studied using quantum mechanical devices,” the authors write.
The Register will watch with interest to see how well the D-Wave paper stands up to scrutiny. ®
Re: Well ...
"protein folding calculation"
It's not a 'calculation' really -just a series of relatively poor algorithms. Huge strides in the basic science have taken place in recent years - these have been assisted by the huge increase in processing power but it's all still far from routine - it's often (relatively) easy to fold a new protein if it has homology to a known structure but even in that 'easy' case it's often found that the optimized solution is still a poor fit to the eventual x-ray structure.
Even x-ray structures, produced as they are at very low temperatures in the solid state may not reflect the 'real' situation in vivo, where the protein is in aqueous solution and may well be associated metal ions (esp. calcium) AND other proteins AND be in dynamic equilibrium with various conformations of itself.
So great if the number crunching can be massively speeded-up but in all other respects there is still a long way to go.
Anyone who is interested in doing this themselves I recommend starting out with wafer thin ham as your best bet. Don't get the real cheap stuff either, it'll just fall apart in your hands. After a lot of practice you can move on to other meats like turkey strips and even bacon. One day I hope to fold a duck out of duck.
Re: Well ...
The problem is really basic science. It's possible now to homology model a modest single domain protein in a few hours even on a desktop workstation. Understanding how the current algorithms and model assumptions fail is MUCH harder. In the end a protein is a dynamic entity and that makes it all much harder.
A few years ago I was interested in a kinase enzyme. A xtal structure was available but the reality turned out that the protein was in dynamic equilibrium between ( at least ) two forms. One was equivalent to the xtal structure, the other was a form that could be activated to give the (unstable) working form - what the structure of that was ???.
The whole reason to model proteins is to make use of the information gained - a fast method of getting the wrong, non-physiological answer is useless on it's own. I'm very optimistic really but there are many problems to solve that don't depend on calculation speed.