mRNA/cDNA display allows random libraries of up to ~ 10^13 proteins to be subject to selection for binding to arbitrary binders. In the listed studies, proteins selected for ATP binding also had ATP hydrolysis activity. This likely means that selection for transition state analogue binding can create enzymes as well, or at least functional uniform binders. More stringent selection schemes with lower diversity can then be used to improve this initial activity.

Functional proteins from a random-sequence library

ATP selection in a random peptide library consisting of prebiotic amino acids

The question is, why isn't this process used more in biotechnology to create proteins with the desired enzymatic activity? Is scouring database for appropriate natural proteins that much more better, or are there technical barriers that I'm missing here?

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    $\begingroup$ Used more often in biotech to do what? $\endgroup$
    – Bryan Krause
    Jan 23, 2023 at 16:45
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    $\begingroup$ To create proteins with desired activity for what purpose? "Biotech" is extremely broad, covering everything from basic research to pharmaceuticals to food to engineering problems. $\endgroup$
    – Bryan Krause
    Jan 23, 2023 at 16:55
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    $\begingroup$ I have narrowed it down to enzymatic activity. $\endgroup$ Jan 23, 2023 at 16:57
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    $\begingroup$ Think about it - if you can find an existing protein with [some of] the characteristics you desire, why would you go through the bother and expense of creating something from scratch? $\endgroup$
    – MattDMo
    Jan 23, 2023 at 17:45
  • $\begingroup$ I'm not sure if this is the kind of tools you are asking for. "pubmed.ncbi.nlm.nih.gov/16919347". They uses an evolutionary algorithm to search proteins with high enzymatic activity. $\endgroup$
    – heracho
    Jan 25, 2023 at 17:30

1 Answer 1


Think about it from a numbers perspective. Briefly, $10^{13}$ is a very small number in the combinatorics of protein sequence space.

There are $20^{100} = 1.267651*10^{130}$ possible peptides of 100 amino acids, a rather small protein. That's more proteins than there are atoms in the universe by 50 orders of magnitude. If you like, here's a paper I found via google that happened to pick this same example, they also have some discussion of the sequence space of naturally occurring proteins.

Do you want to do $10^{117}$ such $10^{13}$-plex random peptide experiments to on average 1X sample that 100 amino acid sequence space, or do you want to do one single $10^{13}$-plex mutagenesis experiment on a known protein that kind of does what you want already, which will let you readily sample the local sequence space around that protein?

You can undoubtedly shave a few orders of magnitude off there by encoding your random peptide generative model with non-uniform aa sampling, structural motifs, sequence Markov models, or whatever. But that isn't going to help you very much. You could also argue that you don't have to exhaustively sample the space as long as you get in the locality of something useful. But a useful protein is almost certainly longer than 100 aas, so you still need to sample astronomical numbers of random proteins.

Even if you get lucky with your random polypeptide approach, you are almost certainly only sampling an unoptimized protein, so you will have to turn around and optimize whatever random polypeptide you find.

The existing protein can admittedly probably only get you to a local optimum. But the search for the global optimum is through an incredibly massive domain of possible proteins.

  • $\begingroup$ I was expecting more of a technical than theoretical answer. My intuition is that every approach will have pros and cons, and random protein generation has to have pros too. It would depend on the details of the process. $\endgroup$ Jan 24, 2023 at 12:14
  • $\begingroup$ @symmetrickittens certainly, I've addressed the pro already: it's possible to search for a globally (or "less locally") optimal protein. That could readily be worth the search, we have no reason to expect that existing enzymes are globally optimal! As for theory vs. technical, search through sequence space is theory, on their own the numbers are pretty daunting. Empirically, consider the ratio of de novo gene birth vs. gene duplication as ways of generating useful genic sequence in genomes (a ratio of "very few" to "almost all"). If I have a little time later, I can try to add to question. $\endgroup$ Jan 24, 2023 at 18:16
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    $\begingroup$ @symmetrickittens put differently: when the numbers are this far against you, the theoretical becomes the technical. My knowledge of the breadth of the Pacific Ocean is theoretical, but it gets technically useful when considering whether I can row to Australia from the N. American west coast. $\endgroup$ Jan 24, 2023 at 18:52

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