ipSAE @RolandDunbrack can distinguish correct heterocomplex pairs when multiple functionally homologous proteins are present within a BGC.

From Caleb, ipsae_max 👉🏻correlation with kD. biorxiv.org/content/10.64898…

AF3 source code is now open source (aka Apache 2.0)! 😎 Though not the weights... 🙃 github.com/google-deepmind/a…

Antibody LMs learn what looks antibody-like, but not how selection turns naive germline antibodies into strong binders. @aakarshv1 and I are excited to share CoSiNE, a model that learns this germline-to-mature process for variant effect prediction and antibody design. (1/8)

At @Biohub, our goal is to build models that accelerate scientific discovery and progress toward the cure to disease. We’re releasing all of this under MIT license allowing commercial and non-commercial use. Read more here: biohub.ai/esm/protein/

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.

- Protein Data Bank (PDB) - Foldseek - AlphafoldDB - Uniprot - Interpro - Jaspar - protein-sequence-similarity-search - protein-sequence-msa - Ensemble - Pubchem - Chembl - pubmed - unibind-database - clinical-trials-database - opentargets-database Finally Pymol OMG!!

repo now available on deepwiki: deepwiki.com/aqlaboratory/ge…

AI can now design antibodies that bind with atomic precision, but not ones that cells can produce. Our preprint closes this gap, delivering a structural principle, an AI-guided rescue pipeline, and adalimumab variants with 20-100x in vivo potency. biorxiv.org/content/10.64898…

Every time I tell AI utopianists that biology is too complex for AI to "solve", they cite the success of AlphaFold. No, AlphaFold did not "solve" protein folding. It gets broad structures correct ~70-88% of the time (depending on evaluation), enabling useful but flawed statistical guesses. True "solving" would require ~99.9% accuracy, practically zero meaningful edge cases, and high confidence across fine details like side chains and conformations. Even then, this is just one narrow slice of the complexities of proteomics. The persistent gap between the "AlphaFold solved protein folding" claim and reality is a perfect example of AI overhype in biology.