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Doesn't your disclaimer kind of also explain a bias against SB, as NMR is mostly used for protein dynamics elucidation (vs. the more rigid structures of Cryo-EM
by hobofan 6y ago
Doesn't your disclaimer kind of also explain a bias against SB, as NMR is mostly used for protein dynamics elucidation (vs. the more rigid structures of Cryo-EM)? Or do you actually mean all fields of SB?
- dekhn 6y agoI mean all fields of SB. NMR is one of the worst- my PhD is in NMR structural biology. NMR gives you some dynamics information, but it's not great, hard to deconvolve and it's limited to very small molecules. of all the techniques, cryo is probably the best right now in terms of ease of sample prep and utility of data, but my point is much more that we don't even really need to know the structure of many things to make useful progress- fluorescence microscopy is far easier than any structural biology technique and provides far more useful signal.
- hobofan 6y agoInteresting! > the most utility are fluorescent imaging with light microscopy, often at low magnification. These are more amenable to scale-up and machine learning Any particular problems there, that you think can be tackled well by machine learning? I just recently started my first ML project in a fluorescence microscopy lab (biochem undergrad with previous software engineering experience), and while I see a lot of things that could benefit from ML for incremental improvements (which often need to be really tailored for each project), as is the case with most disciplines, I haven't really seen anything where I thought that there was potential for something groundbreaking.
- dekhn 6y agothere's good stuff here https://www.nature.com/collections/cfcdjceech https://www.nature.com/collections/cfcdjceech if you have access to Nature.