HyperProtein

Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.

​Profluent's platform revolutionizes protein design by integrating advanced AI with in-house wet-lab capabilities, enabling the creation of proteins either inspired by nature or reimagined from scratch. This holistic approach allows for precise, adaptable, and scalable solutions to complex biological challenges, delivering results that redefine what's possible with proteins. Profluent's foundation models push the frontier of protein design beyond the limitations of random discovery, facilitating the optimization of multiple attributes simultaneously, accessing greater sequence diversity, and enabling novel functionalities. By extrapolating into new protein spaces, Profluent offers unique possibilities beyond natural or patented proteins, making it cheaper, easier, and feasible for partners to achieve commercial success. Profluent's capabilities are built on a commitment to scientific rigor, leveraging diverse datasets and advanced AI to tackle challenges.