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I went through biochem, but didn’t fully understand just how gigantic & complicated proteins are until I started learning about computational protein folding. There’s several levels of abstraction just between rna/ribosomes and functional proteins… that’s one of the most shocking complexities to me, most pieces of life are rather elegant when you come to understand them but it’s hard to imagine how complex proteins evolved spontaneously. There’s just endless complexity there.

There’s 574 amino acids making four separate interlocking chains in a single globin, plus the heme, all just to bind 4 oxygen molecules. It’s simultaneously elegant but hugely complex, far above any discussion of the rna sequencing.

It’s a big part of the “gap” between chemistry and biology IMO.



I worked for a professor (James Milner-White) who was interested in early protein evolution and I remember a conversation we had about the possibility that proteins could have evolved from large to small.

Not sure if it was from a published paper, but the idea was that early proteins might have been large - say several hundred residues - but mostly disordered.

The smaller, more ordered 'domains' would then have evolved within these larger chains. Recombination and deletion would then have pruned down the disordered parts to leave more efficient smaller proteins.

No idea if that idea makes sense or has any research behind it, but it's quite a neat theory.


There was a paper a few years ago about a similar effect in artificial neural networks [0]. The gist was that a large network can contain many subnetworks, and the number of subnetworks grows much faster than the size of the network they are contained in. They were able to find a subnetwork in a randomly weighted network with equivalent performance to a trained network of a much smaller size.

[0] https://arxiv.org/abs/1911.13299


Nice. Sounds like these self-assembling subnets could be the basis for a viable model explaining the mechanisms behind early evolution.


Skynet?


Knowledge Distillation is a related concept in deep NNs, as are the concepts behind the compression of data in signal processing.


wow ... it makes sense ... more of a top down approach.


Its actually top down and bottom up at the same time. All of biochemistry operates on the basic rules of physics which determine how the chemistry happens with feedback from the surroundings/system as the top down part


The hemoglobin molecule is different for every species, and if you chart changes in the molecule, it forms the same tree as evolutionary biologists had already figured out.

Humans have the most complicated hemoglobin molecule.


> I went through biochem, but didn’t fully understand just how gigantic & complicated proteins are until I started learning about computational protein folding.

Some years back, there seemed an opportunity to create an educational web interactive, a full-scale 3D folding sim, with hands-on direct manipulation, by aiming for plausible-not-correct folding. The simulation literature having built up lots of shortcuts for slashing computation costs, which sacrificed correctness but not plausibility. So one might variously knead a protein, alter it and its environment, and watch it flail. I wonder if anyone ever got around to it?


This sounds like FoldIt?




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