How do huge computer simulations help in medical research?
I found the there is a project Folding@home to fight against COVID-19. As far as I understand, it uses huge amount of computing power to find a cure. Why do we need such a huge number of potential candidates for compounds? I have understood that the limiting thing when developing drugs is the clinical tests, and one research team can't do really many tests in a day. So why don't researchers just use a normal laptop and run some simulation program which says that next one should test the following candidates? Or have I understood wrongly how the distributed computing or super computers helps on medicine? It sound really weird if researchers has so accurate heuristics that simulations can say that this candidate looks more promising as that one.
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For the COVID-19 coronavirus, the first step of infection occurs in the lungs, when a protein on the surface of the virus binds to a receptor protein on a lung cell. This viral protein is called the spike protein (Wrapp, et al. 2020), and the receptor is known as ACE2 (Angiotensin-converting enzyme 2).
Before proteins can do their work, they need to assemble themselves. This self-assembly is what is called "folding" and there are many, many different ways proteins can fold.
An antibody is a type of folded protein that can block the spike protein from binding to its receptor, therefore preventing the virus from infecting the lung cell.
Folding refers to the way human protein folds in the cells that make up your body. We rely on the proteins to keep us healthy and they assemble themselves by folding. But when they misfold, there can be serious consequences to a person’s health. (Source: Folding@home Homepage)
You asked
So why don't researchers just use a normal laptop and run some simulation program which says that next one should test the following candidates?
Testing protein folding permutations takes a huge amount of computer power. Using the network of computers using the Folding@home app, they are combining the power of all the connected computers at once to work out the protein sequence as quickly as possible.
We can build computational models that accomplish this goal, but it takes a lot of computing power.
This is where you come in! With many computers working towards the same goal, we aim to help develop a therapeutic remedy as quickly as possible (Source: Folding@home COVID-19 page).
One protein from 2019-nCoV, a protease encoded by the viral RNA, has already been crystallized (See: Worldwide Protein Data Bank).
Although the 2019-nCoV spike protein of interest has not yet been resolved bound to ACE2, our objective is to use the homologous structure of the SARS-CoV spike protein to identify therapeutic antibody targets (Source: Folding@home COVID-19 page).
References
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C. L., Abiona, O., ... & McLellan, J. S. (2020). Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483), 1260-1263. doi: 10.1126/science.abb2507
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