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GlycoProteomics might come after Glycanomics and biotechnology applications

Richard K Collins All Global Open Devices, All knowledge, All Languages September 23, 2024
reviewing Glycoproteomics at https://www.nature.com/articles/s43586-022-00128-4
from https://x.com/slavov_n/status/1837829021133910203

My Comments: GlycoProteomics might come after Glycanomics and biotechnology applications

Nickolai, Glycosylation ought to be one of the easier calculations to do. It is not a large problem by today’s computer standards. Easier to calculate precisely and simulate than to study in a lab. There are so many competing priorities; lots of “most importants”.
 
Why is it in your list of “importants”? What is it that you want to do but cannot? What is it you want to know or be more certain about, but you are not?
 
I read the paper and it is fairly simple summary of today’s lab methods. But not any sense of urgency. Their words are “popular”,”favored”, “advantageous”, “may be useful”. “useful”. The only clear change seems to be (we found a lot of them and we can run them through our systems fairly quickly), and some biomarker interests. “as the field develops”, “new opportunities to explore”, “still a maturing field”.
 
Perhaps “important” is something like “more accessible and in the future we will find lucrative applications”?
 
It seems from “Data deposition and sharing” this is a set of emerging methods, still trying to find a market”
 
I think the problem is they still mostly rely on “wet” methods and could do with more 3D chemical imaging, modeling and simulation. – at all spatial and molecular weight scales.
 
It sounds like “we see these things all over the place, they must be important. We found lots of ways to approach them but still have no real clue what to do. Maybe the data will be useful to someone in the future. We put some on the web, maybe someone will figure it out and then demand for our skills and tools and data will increase.”
 
The same old demand drivers – cancers, viruses, genetic disorders, bacterial pathogen food.
 
I would bet on bacteria and look at the bacterial uses of oligoproteins and glycans. Ignore humans, check viruses, then wait for human researchers to jump on board and then provide core mechanism controls and analyses. Get more 24/7 AIs to gather and analyze trends in interests of groups. I think part of the problem is legacy LC companies too complacent or bored, not looking far enough. Probably not looking at all countries. Which is why I suggest bacteria.

Also bioengineering for plants and food production might be “very important” because those can turn into real markets where quality, efficiency, traceability, identification, broad coverage, and adaptability are life critical. You have a few die from cancer, but billions have to eat or die.

Glycanomics sounds good.  Not requiring genetic modifications, simply more efficient tracking, filtering and separation into products and intermediate streams for higher quality and value. And it is “natural” and smart.

If you do plants, animals, bacterial and yeast colonies, molds, viruses, all industrial bio-separations, that experience of problems in vats and processes is likely applicable to humans and domesticated animals and plants as well. You can share it or hoard it. I would prefer you share it, because I am not greedy for billions or trillions or more.

Richard Collins, The Internet Foundation
Richard K Collins

About: Richard K Collins

The Internet Foundation Internet policies, global issues, global open lossless data, global open collaboration

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