Comment by flobosg
2 years ago
> If you're setting the ability to crystallize as the single point of failure endpoint, that logic applies to every subfield.
I agree that there are other fields with similar issues. What baffles me is how long protein crystallization has been a problem.
I’ll use your example:
Nowadays, sequencing a gene is unlikely to yield a single publication by itself but is no early point of failure. It’s a solved problem with protocols that have been thoroughly developed and explained to boredom. New early points of failure arise (sample related, maybe?).
Nowadays, determining the structure of a protein is unlikely to yield a single publication by itself but has a clear, early, unsolved point of failure. No understandable protocol other than buying $creening plate$, fetching cat whiskers, drawing a theoretical phase diagram that tells you nothing, and pray that your crystallization tray doesn’t show a scrambled egg tomorrow or in six weeks. This has been an issue for more than fifty years and almost 200k published structures. The jump you mentioned in sequencing hasn’t happened yet in protein crystallography and might never happen because our understanding of macromolecular crystallization is lacking and thus we cannot predict proper crystallization conditions.
Sure, I agree that crystallization in particular has faced this particular bottleneck for a long time. The field of SB, however, has still managed to advance massively too. For example, Cryo-EM can do things we could barely imagine a decade ago.
The point I'm trying to make is that from the perspective of a grad student, no field is devoid of risk, and it's surprisingly easy to be stuck by something that's a solved problem on paper. For example, I know of a grad student that's been trying to develop a mouse line for about a year now, and has now discovered that this strain just won't work for what they have in mind - and must now recreate the mutant combinations in a different strain that's at least a year's work - if it even works. I've heard stories of entire mouse lines die, and you're back to square one - years of work lost.
The other thing that complicates some of these fields is the massive pace of innovation they're undergoing that it is very hard for an individual lab to keep up to date. Grad students are using techniques that were published less than 5 years ago, and there's no locally available expertise to tap into. What remains the same is the level of grunt work grad students and postdocs have to do, even if the techniques get more sophisticated over time.