Mosasaurus

Studying influenza and cell intrinsic immunity @BiochemOxford

[Mosasaurus @mosasaurus27]

I have recently interviewed one of Oxford’s most prominent scientists: Professor Raymond Dwek. His contributions to the field of glycobiology have been fundamental (he even coined the term “ glycobiology”). I hope you enjoy reading it:

https://t.co/HqTAHzIUZv

[Mosasaurus @mosasaurus27]

@ARampotas @ScienceTM Awesome, congrats! How well would mutCALR work as a neoantigen in a vaccine, in your opinion?

[Alex Rampotas @ARampotas]

13/ Now that pre-clinical development is complete, we are exploring funding options for a phase I first-in-human clinical trial (CAL-STRIKE). We aim to treat 12 patients to assess safety and detect any preliminary effiacy signals.

[Mosasaurus @mosasaurus27]

@DhuviKarthikey1 Such a cool origin for a fantastic project! Great job pal, also let me know the next time you’re in London, would love to meet up !!

[Mosasaurus @mosasaurus27]

@bots_and_bits Kind of like a Roviant Sciences situation, sounds interesting!

[Olivier Borkowski @O_Borkowski]

A Chemically Defined Synthetic Cell Capable of Growth and Replication

https://t.co/y5dhtHGa8r

[Mosasaurus @mosasaurus27]

@Sayedabdelwhab How did it evade MxA 🤔

[Mosasaurus @mosasaurus27]

@javid_lab Yeah that’s what I thought, and I haven’t seen much on the topic once that letter was released but I saw this the other day:

[Flagship Pioneering @FlagshipPioneer]

D-proteins have an obvious therapeutic rationale — their mirror-image structure confers protease resistance and immune stealth inherently. This has been hypothesized for decades.  

The bottleneck was computational. Every major protein design tool  has been trained on natural L-protein data. With these models, D-protein design simply isn't tractable.

@Abiologics_Inc built a generative model to solve that problem — enabling the design of D-protein therapeutics that penetrate deep into tumor tissue and persist for days.  

Read our latest Substack post: https://t.co/u3eqI6Fkdl

[Mosasaurus @mosasaurus27]

I am suprised that I haven’t seen any biodefense people speak about mirror proteins

[Mosasaurus @mosasaurus27]

@javid_lab 100%

[owl @owl_posting]

insane that modern image gen can, at minimum, three-shot something gorgeous. yes, there's slop along the way, but an hour of work later and now i have four posters that id love to hang in my apartment. only thing stopping me are sufficiently good upscaling models. weird future

[Martin Borch Jensen @MartinBJensen]

The longevity field is over-translated at this point, the basic research needs more support.

There was a ~decade long 'golden era of discoveries' from early 2000s to say 2016 Ocampo paper, where we learned about many new mechanisms.

We naturally started translating those, fueled in part by Calico as validation for investors. This is good, but the field didn't grow enough to replace the researchers now focused on translation. And new sources of funding like @ARPAHealth also focus on translation.

So the well is running low. Time for some rain.

(company data from @KarlPfleger)

[Mosasaurus @mosasaurus27]

@michaelzlin Beautifully written! But would you not say that the complexity of the system lies within how variable/contextual it is?

[Michael Lin, MD PhD 🧬 @michaelzlin]

I think I've figured out one problem.

When biologists say complexity, they often mean variability: different results in similar conditions or with what seem like meaningless differences (different cell density, different culture medium, etc). I believe we/they do this because it sounds better, as if the variability were hinting at something profound rather than being meaningless noise or revealing the model system to be fragile.

When CS people hear complexity, they think great, we can solve complexity better than biologists because we can scale our model to more parameters to capture all the interactions.

Sorry to inform you that biology suffers not primarily from complexity of nodes and edges but from every single living model system being fragile and/or stochastic, from cell culture all the way to humans. The relative robustness of single protein folding or binding simply will not generalize to living cells. Even if you do it in 99 different cell types or condition, the 100th cell type or condition may not give you a predictable result.

[Mosasaurus @mosasaurus27]

@InVitroFuture What do you think the adoption turnout would look like for lab-grown pet food?

[Genyro @Genyroinc]

Genyro is pleased to share a new publication in #NatureBiotechnology from co-founder Brian Hie and his colleagues Stanford University titled “Efficient Generation of Epitope-Targeted Antibodies with Germinal.”

Full paper: https://t.co/VFaUQqxh1O

[Flagship Pioneering @FlagshipPioneer]

D-proteins have an obvious therapeutic rationale — their mirror-image structure confers protease resistance and immune stealth inherently. This has been hypothesized for decades.  

The bottleneck was computational. Every major protein design tool  has been trained on natural L-protein data. With these models, D-protein design simply isn't tractable.

@Abiologics_Inc built a generative model to solve that problem — enabling the design of D-protein therapeutics that penetrate deep into tumor tissue and persist for days.  

Read our latest Substack post: https://t.co/u3eqI6Fkdl

[Bruce Levine, Ph.D. 🇺🇦🥼🔬🧬🧪💉 @BLLPHD]

🚨NEW - Ten-Year Outcomes after CAR T-Cell
Therapy for B-Cell Lymphomas. 38 heavily pretreated patients with relapsed/refractory B-cell lymphomas received CD19 CAR-T cells. At a median follow-up of 10.1 years, no relapses occurred beyond 5.4 years. That is not just response. That is durability. @NEJM @MarcoRuella Luca Paruzzo & @PennMedicine colleagues

[Mosasaurus @mosasaurus27]

@owl_posting @NeuroLuebbert @FoundationOAI Congrats!!

[owl @owl_posting]

Life update: after an incredible year at Noetik, I’ve joined the OpenAI Foundation (@FoundationOAI) to help create its "Public Data for Health" program.

The OpenAI Foundation is a well-capitalized philanthropy, and a meaningful share of its funds will be committed to building and opening up the datasets necessary to massively accelerate biomedical research. Some of our grants will go toward efforts to relieve known data bottlenecks, but others will be more speculative, made on the premise that artificial intelligence is currently reshaping how scientific discovery happens, and that this reshaping will surface fundamentally new data bottlenecks of its own. We have a long to-do list ahead of us, and I’m ecstatic to be joining @JacobTref on this effort!

On writing: I’ve spent the last few years covering the intersection of AI with many, many subfields of the life sciences at https://t.co/QPTHsR3fzm, and it will continue + remain independent. Many exciting essays and podcasts are planned!

Lastly, I remain extremely optimistic on Noetik and am very thankful for my time there. Consider following @Ronalfa and @recursus to stay updated on their efforts!