Benchmarking generative protein models at scale with GROQ-SEQ 📊 We built a unified benchmarking framework and experimentally tested alignment-based, language, and structure-based models on TEV protease. Structure-based models achieved up to 74% hit rates, a remarkably high level of experimental success, while computational predictions showed weak agreement with experimental activity. This collaboration with the @Deborah Marks Lab enabled large-scale experimental benchmarking of today’s leading generative protein models.🔬 🔗 Dive into the details: hubs.la/Q04gpnfg0 #ProteinEngineering #ProteinDesign #MachineLearning #SyntheticBiology #Biotech #AIinBiology #DataScience #GROQseq

🧬 How Much Diversity Can Proteases Handle? GROQ-seq Has Answers We profiled 11,722 sequence-diverse protease homologs, including AI-designed minimized variants, and uncovered robust activity across surprisingly distant sequences. Even across extreme sequence divergence, protease function against the canonical TEV protease substrate persists, with distinct homologs showing activity at as low as 19% sequence identity. Datasets like this are key to unlocking the next generation of machine learning models for protein engineering 🤖 🔗 Read more: hubs.la/Q04fz4l80 #ProteinEngineering #MachineLearning #SyntheticBiology #Biotech #EnzymeEngineering #HighThroughput #DataScience #AIinBiology #GROQseq