Biohub, the non-profit research organization co-founded by Priscilla Chan, MD, and Mark Zuckerberg, has now unveiled the latest update to the ESM protein language model family, with expanded capabilities in binder design and protein function mapping for therapeutic discovery. The release comes just seven months after Biohub recruited the team behind EvolutionaryScale. The system includes ESMC (Evolutionary Scale Modeling Cambrian), a language model trained on approximately 2.8 billion sequences drawn from a breadth of life, including organisms adapted to extreme environments, and more than 20,000 types of proteins found in the human body. Evolutionary information encoded in ESMC is translated into atomic-resolution protein structures and interactions using the design engine and prediction model, ESMFold2. Alex Rives, PhD, head of science at Biohub and former chief scientist at EvolutionaryScale, presented the work at this week’s “AI in Biology” symposium at Cold Spring Harbor Laboratory. These models aim to transform the earliest stages of drug discovery by making biology more programmable. While traditional discovery workflows rely on slow and resource intensive experimental screens to identify promising drug candidates, rational protein design guided by in silico predictions has the potential to dramatically accelerate development timelines. “We’re at an exciting point in protein biology where accurate digital representations allow asking experimental questions at a scale that wouldn’t be possible in the laboratory,” Rives told GEN Edge. ESMC provides a foundation for modeling the sequence, structure, and function of proteins. ESMFold2 predicts the structure of proteins and biomolecular complexes. Features derived from the representations of the model capture fundamental principles of structure and function that form a compositional grammar for protein biology. [Biohub]ESMFold2 designed high-affinity protein binders against five…
