jfarlow · HackerTrans

el mes pasadodiscuss

Network effects + energetic fficiencies. On an energy landscape that includes integration over very short and very long lifetimes, the thalweg of utility/energy rests right about where the current codon optimizations are. And any schemes that deviate don't get to share in the others' bounty. Reusing your foods' effort saves a lot, metabolically.

jfarlow

hace 7 mesesdiscuss

This is a challenge, even for someone who has professionally used the breadth of proteins. I really like the test. I'm actually kind of surprised at my own pulling on knowledge to make a guess - it's an orthogonal way to think about the question than is usually posed.

I wonder if there's a way to ease the difficulty by filling in 'correct' features of the guesses: if your guess is a 'transmembrane' then it reveals that as a property. On the other hand, I don't think the annotations are clean enough - and are often designed for 'at all' rather than 'primary' features. For one of the examples, once I noticed it was an adhesion protein, it would have been interesting to sift through classes or cell types as opposed to just continuing to shoot in the dark based on the structure alone.

I presume you're showing even the 'low confidence' portions of the predicted structure? Please do.

You could also show the primary amino acid sequence too - there's a weird familiarity with those given how often the structures themselves have historically not been so accessible. BLASTING each of the guesses would be another interesting thing to see.

jfarlow

hace 7 mesesdiscuss

And the atoms in the proteins and DNA that are exactly replicated to the atom each have a feature sizes resolved at fractions of a nanometer in 3 dimensions (and likely in time/dynamics too).

jfarlow

hace 4 añosdiscuss

Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/

Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.

We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities & custom protein design software. Please reach out of you're interested in this role or software development in biotech: [email protected].

https://serotiny.com/careers/bioinformatics-scientist/

jfarlow

hace 4 añosdiscuss

Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/ Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.

We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities & custom protein design software. Please reach out of you're interested in this role or software development in biotech: [email protected].

https://serotiny.com/careers/bioinformatics-scientist/

jfarlow

hace 4 añosdiscuss

Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/

Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.

We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities. Please reach out of you're interested in this role or software development in biotech: [email protected].

https://serotiny.com/careers/bioinformatics-scientist/

jfarlow

hace 7 añosdiscuss

Penrose's 'Road to Reality' [1] is a kind primer on where the math comes from, as it applies to physics. Kind of a philosophical walkthrough of how math applies to physics. It is nowhere near as concise as Feynman's lectures, but it does complement them pretty well, while getting more into the math, and why the math is needed to describe various aspects of physical reality.

[1] https://www.math.columbia.edu/~woit/wordpress/?p=154

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