1,124 karma · joined February 3, 2017
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.
KSSEPASVSAAERRAETEQHKLEQENPGIVWLDQHGRVTAENDVALQILGPAGEQSLGVAQDSLEGIDVVQLHPEKSRDKLRFLLQSKDVGGSPVKSPPPVAMMINIPDRILMIKVSSMIAAGGASGTSMIFYDVTDLTTEPSGLPAGGSAPSHHHHHH
It is a protein encoding the PxRcoM-1 heme binding domain with C94S mutation and a C-terminal 6xHis tag (RcoM-HBD-C94S)
[1] https://www.pnas.org/doi/10.1073/pnas.2501389122#supplementa...
That was one of the first cases of _germline_ gene editing using CRISPR - NOT "the first instance of gene editing." There have been quite a few other genetic editing tools that predate CRISPR, and there have been other edits using CRISPR that were not of the entire human's genome.
What's intriguing is not the 'custom' part, but the speed part (which permits it to be custom). Part of what makes CRISPR so powerful is that it can easily be 'adjusted' to work on different sequences based on a quick (DNA) string change - a day or two. Prior custom protein engineering would take minimum of months at full speed to 'adjust'.
That ease of manipulating DNA strings to enable rapid turnaround is similar to the difference between old-school protein based vaccines and the mRNA based vaccines. When you're manipulating 'source code' nucleic acid sequences you can move very quickly compared to manipulating the 'compiled' protein.
In microscopy, this is called 'super-resolution'. You can take many images over and over, and while the light itself is 100s of nanometers large, you actually can calculate the centroid of whatever is producing that light with greater resolution than the size of the light itself.
Welcome to Biology!
Llama 8G loads and runs pretty well on the new M-series Macs with a reasonable amount of RAM.
1) using the patient's own cells [personalization of T Cells]
2) customized therapeutic genetic payload, per patient [personalization of the CAR]
There are current competing factions for #1 - where cells are from just the patient ["Autologous"] (safer, slower, more expensive), and where the cells are from a universal donor ["Allogeneic"] (possible immune response, but can be manufactured at scale).
The therapeutic payload is a DNA sequence encoding a synthetic chimeric receptor ["CAR"]. This sequence is customized based on the details of the patient's particular cancer, but are common across many people. If the cancer has an excess of "Protein X" on it, then the CAR sequence is designed to target Protein X. All patients with a similar cancer profile receive the same CAR sequence as a payload to the T cells. This too could be personalized, to not just profile the _class_ of cancer, but particular to that _specific patent's cancer's profile_ - but this is not yet feasible given the turnaround time to build, test and evaluate a new genetic payload in the context of a person's specific tumor cells.
This particular therapy has the cells be from the patient (personalized), but the CAR sequence provided to the cells is common for all people that have the same cancer profile (semi-personalized). In this case, the cancer profile includes those that have an abundance of the protein called Claudin-6.
I kind of find the distinction of 'robots' vs cells funny, as once you get down to the (sub)nanometer level one's intuition should flip: organic material acts stiffer and more lego-like than metals - which act more like unreliable putties. A "device" that becomes small enough is much more likely to be made of organic molecules than metallic molecules - cells ARE those futuristic robots...
The kinesin motor proteins are pretty cool too [1], but those are naturally occurring machines that I suspect we'll be imitating for a long time.
Once you get DNA oligos in there you can do computation, as X binds X', and Y to Y'. So you can have all sorts of complex synthetic & designed interactions using chemistry that is both seamless and doesn't interfere with normal molecular biolgy.
Once you have proteins, you can localize particular chemistries.
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 & scale our design software. Please reach out of you're interested in this role or software development in biotech: jobs_platform@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: jobs_platform@serotiny.com.
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: jobs_platform@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: jobs_platform@serotiny.com.
The original gene therapies (early 2000s) were essentially RNA therapies (adenovirus). And their unethical rush and subsequent failures caused a bit of a 'gene therapy winter' [1]. We've since made enormous progress on both the ability to safely deliver genes, but also our ability to generate/design new useful genes.
[1] https://www.labiotech.eu/in-depth/gene-therapy-history/
> In 1972, a paper titled ‘Gene therapy for human genetic disease?’ was published in Science by US scientists Theodore Friedmann and Richard Roblin, who outlined the immense potential of incorporating DNA sequences into patients’ cells for treating people with genetic disorders. However, they urged caution in the development of the technology, pointing out several key bottlenecks in scientific understanding that still needed to be addressed.
I don't think that this is accurate.
We design proteins for immunotherapies - this kind of thing would help us more rapidly design our proteins (and more efficiently use our wet-lab resources to speed existing projects). For others, some drugs are hard build without knowing how they will interact - this could both provide new 'targets' to go after, but also might help prevent projects that would otherwise accidentally target an important protein.
I personally think having even a modest intuition for how to map physical knowledge to its appropriate 'scale' (time/space, from plank to universe) is one of the most straightforward ways to be "smart". And a great way to get to know the limits of our knowledge.
I still haven't seen a video that matches my intuition. Which is unfortunate. I want to build a version of your scaler there, but for VR that you can slide up and down along at least time/scale axes - and maybe additional ones too.
An event like a "protein folding" can take milliseconds, in a tube [1]. While atomic/biophysical/biochemical simulations have time-steps of femto-seconds.
[1] https://www.youtube.com/watch?v=gFcp2Xpd29I
From https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890418/
"Traditional MD [Molecular Dynamics (protein folding)] simulations are limited in length by timestep limits. Studies by our group and others have shown that traditional MD is limited to timesteps of about 2 fs due to high-frequency resonance frequencies.1–3 Many biologically relevant motions occur on the microsecond to millisecond range, which is 9 to 12 orders of magnitude greater than the timesteps possible with traditional MD. Further, each step requires a costly force calculation (O(N) to O(N2)). As such, simulating medium-size proteins often requires months of computer time on a large distributed cluster such as Folding@home4,5 to simulate milliseconds of dynamics, while simulating a large protein (e.g. the β-2 Adrenergic Receptor) on biologically-relevant time scales (milliseconds through hours) using a standard desktop computer would take years. Thus, it is not feasible to simulate timescales of biological interest without substantial advances in MD methods."
12 orders of magnitude of difference in time is akin to the difference between causal events happening once per millisecond and those happening once per century. How do you show a movie capturing the nuance of someone's blink reflex, along with their birth life and death...
Sometimes these are chunks of toxins that your body recognizes a toxic, but without the actual part of the toxin that causes the toxicity. A kind of flag but without the army behind it.