
Unleashing the Power of Synthetic Proteins - dnetesn
http://alliance.nautil.us/article/150/unleashing-the-power-of-synthetic-proteins
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jfarlow
This is our software to help you build your own novel synthetic proteins if
you want to give it a try yourself right now in your browser (built with Go
and Ember!):

[https://serotiny.bio/](https://serotiny.bio/)

No knowledge of DNA or cloning techniques are needed to use it. Drag and drop
protein domains with the functions you want to produce a single protein with
those functions. And if you wish, we can help place the order for your design
with a DNA synthesizer so that the DNA that encodes your new protein arrive in
the mail. Entire companies are founded on single good ideas for how to
assemble these protein parts. Come up with your own.

It's also a great place to learn about how others are designing their
synthetic proteins. The intent is to build up a plain language app-store for
all of these various capabilities, as well as keep track of how the various
proteins get used. At the end of the day it's not the DNA that does anything
in an organism, it's the proteins that are really the machines of your body.
If the DNA is the source code, the proteins are the apps themselves. DNA is
cool and philosophical, but proteins are useful.

Here is a list and short description of proteins as they've come up on HN in
the past few months. I've been trying to keep a list of 'proteins of interest'
that have some social, economic, or medical interest to Hacker News:

[https://serotiny.bio/notes/proteins/](https://serotiny.bio/notes/proteins/)

~~~
kanzure
How often do fusion proteins fail to work? I see a lot of "just stick this
catalytic domain to dCas9" and I am not really sure why that works.

~~~
jfarlow
More times than not protein fusions work just fine for the desired definition
of 'work'. If you are trying to tune the very last efficiency out of an
enzyme, or push one organism's survival efficiency past another's, then yeah
smashing domains together is not the way you arrive at an evolutionarily
polished machine. However if you need to test or create new functionality it
seems to work just fine. Biology is extraordinarily robust. A common technique
is to combine these to approaches - build out a rationally designed functional
protein by fusion, then run it through a few rounds of directed evolution to
have the organisms themselves iron out the kinks. This strategy has been very
successful.

Just smashing GFP onto every protein in the yeast proteome resulted in ~80%
successfully turning green without noticeably affecting the yeast growth.[1]
Check out eCD4-IG - it's four components fused to become an extraordinarily
effective anti-HIV drug. It is of a different kind and a different order of
effectiveness then the small molecule drugs of the 20th century.[2]

[1]
[http://www.nature.com/nature/journal/v425/n6959/full/nature0...](http://www.nature.com/nature/journal/v425/n6959/full/nature02046.html)

[2]
[https://serotiny.bio/notes/proteins/ecd4ig/](https://serotiny.bio/notes/proteins/ecd4ig/)

------
entee
Some context: the author, David Baker, is a preeminent figure in the world of
protein structure prediction. His lab has consistently had the best protein
structure prediction software out there (Rosetta). They recently released a
paper detailing the structures of a large number of unknown proteins using
their technique as an aid [1].

Some of the ideas in this article are quite cool, but I think it's important
to add a caveat: we don't often know what structure will yield what result. In
other words, if I want to do X, I don't know what protein structure will get
me X, which makes it hard to engineer such a protein.

Nevertheless, when you have some understanding of the problem, you can in fact
engineer proteins. Baker gives several examples in the article. In many of
those cases, the best approach is to start with a variety of engineered
proteins, then use in-vitro simulated evolution to pick the winners and get
pretty substantial improvements over the original design.

Protein engineering is quite a cool area, and hopefully over time we'll get
better at understanding the other parts of biochemistry so that we can more
effectively design useful proteins.

[1]
[http://science.sciencemag.org/content/355/6322/294](http://science.sciencemag.org/content/355/6322/294)

~~~
adrianN
I'm convinced that synthetic proteins are the most viable way to Drexler type
nanotech. Biology demonstrates that it works, we "just" have to understand
how.

~~~
jfarlow
At the sub 10nm scale you need stiff linkages to have predictable and
consistent structures. Covalent 'organic' bonds give you stiff linkages at
that scale while metals are goopy and electronically inconsistent. Proteins
_are_ organic nanotechnology where the 3D feature sizes of the (wet) tools is
measured in fractions of angstroms. The best Intel can do is ~ two orders of
magnitude larger resolution, 2D, dry.

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Lramseyer
Could somebody with more expertise explain how modern day protein folding
simulations work? (Like Rosetta) I'm only familiar with the folding@home
method of brute force molecular dynamics simulation. What kind of
computational power does it take to go from an RNA sequence or polypeptide
chain to stable protein with these algorithms?

This has always been a subject that has fascinated me, ever since high school
when my biology teacher told us about folding@home. I went into computer
hardware, so I always saw this problem as a computational power problem. I
always believed that if we could figure this out, we could do a lot of cool
things like make nanotubes and cure diseases. I saw it as the key to the
nanotechnology revolution.

~~~
zgcarvalho
Rosetta is a software for protein structure prediction, not for protein
folding simulation. There are a lot of hipothesis about protein folding, but
the mechanism is unknown yet. So simulations using molecular dynamics can help
us to understand how proteins fold.

On the other hand, protein structure prediction methods like Rosetta try to
create high quality atomic models of proteins without follow a physical model.
The goal is a good final model, not the fold event.

Rosetta uses a method of fragment assembly where fragments (few amino acids)
of proteins with structure solved experimentally (usually X-ray
crystalography) are assembled to create the new structure. The fragments have
sequences of residues (amino acids) identicals or similar to the protein being
modelled. This is an optimization process and uses methods like Simulated
annealing. So, computational power is very important to explore a lot of
protein conformations.

