Science Updates

Key Takeaways The scientific community is currently debating how to balance open research with biosecurity protections when it comes to biological AI tools. Biological AI tools are primarily designed for drug discovery, treating illnesses, and other broader societal benefits. Advancements in AI, such as chatbots, could provide opportunities for even inexperienced bad actors to misuse…

Key Takeaways Biohub has released open source ESM protein language models that use evolutionary data to streamline drug discovery and map functional protein spaces. ESMFold2 successfully generated lab-validated, high-affinity protein binders against five disease targets in cancer and immunology. The models were used to generate the ESM Atlas, which maps 6.8 billion sequences and 1.1…

Key Takeaways OpenBind is generating dense, high-quality protein-ligand datasets that link structural data with binding measurements at a large scale.  This release provides a realistic, structurally novel testbed linking high-resolution crystallographic data directly with biophysical binding affinity. The initial release targets the Enterovirus A71 (EV-A71) 2A protease (modeled via a closely related Coxsackievirus A16 surrogate…

Key Takeaways: dEVA (design by EVolutionary Algorithm) is a framework that achieves the zero-shot design of a highly efficient metalloenzyme without any reliance on natural templates. Design objectives were tailored to the specific chemistry of metalloenzymes, in particular catalytic zinc sites, and from only three experimentally-tested designs found desB, the most efficient de novo designed…

Key Takeaways: With advancements in AI, the field of protein engineering has shifted from template-based approaches to de novo designs with approaches including seed-based design, deep generative models, and binder hallucination. Design towards specific protein functions is the current frontier factoring in parameters including neosurfaces, conditional binding with biological stimuli, and dynamic modeling, linking deep…

Key Takeaways: NVIDIA’s new Proteina-Complexa model combines generative AI with inference-time search algorithms to operate 30-60x faster than RFdiffusion when designing custom proteins that target specific diseases. In a test to find binders for 127 different targets from over 1 million protein designs, the model successfully found binders for 68% of the targets. The model…

Why this work matters Cyclic peptides are attractive scaffolds because their closed backbone can promote structural rigidity and proteolytic stability. Yet assigning sequences that reliably fold into a given cyclic backbone remains a key challenge. In this study, the authors introduce CyclicMPNN, a fine-tuned version of ProteinMPNN designed specifically to improve sequence design for short…