Scientists are actively researching ways to make phage therapies more effective. This includes efforts to engineer phages to expand their host range and aid in bacterial defense system evasion. More at Molecule of the Month: pdb101.rcsb.org/motm/318

I want to SCREAM every time I read an LM paper that says how human language is similar to protein or DNA

Why does tryptophan have only one codon, UGG?

For the past 70 years, modern biology has been built on a static objects worldview: DNA = information, proteins = structures, cells = nanomachines, disease = broken parts, drugs = part repair/update. I'm honestly excited for a “new worldview” and “Dynamic Biology” to emerge.

In January, I reviewed a Review for a crappy MDPI journal. It was 100% AI, missing citations, etc. I sent a massive report, and the authors withdrew it. Today, I see it published without a single change in another even crappier MDPI journal.

A striking graph of MIT's admin bloat in a @PNASNews paper by @VickyCYang (@MITSloan).

We've done a million of these deep dives into interpreting and understanding ESMC features, it's just that we don't quite know how to write about them other than to say "here are a bunch of cool observations".

Can someone give me the tldr on ESMFold vs ESMFold2? They also did an atlas release with ESMFold years ago. And isn't ESM2 like 5 years old? What happened to ESM3, which was open weights and 2 years old?

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.