QCB Seminar featuring Arvind Ramanathan

Join us for our QCB Seminar featuring Arvind Ramanathan, Argonne National Laboratory. Presentation begins at 2pm in Beckman 3269 and on Zoom, coffee and snacks served in the 3rd Floor TCBG Courtyard (outside room 3151) beginning at 1:30pm. 

Title: Agentic AI for Bio-Molecular Modeling and Simulations: Lessons Learned in Designing Enzymes and Bio-Therapeutics

Abstract: Designing functional enzymes and biotherapeutics remains slow, but the bottleneck is no longer generation — it is verification. This talk traces three successive systems built at Argonne to address that bottleneck, and what each one taught us. The first one addresses, GenSLM, a genome-scale language model pre-trained on roughly 110 million prokaryotic gene sequences and fine-tuned on tryptophan synthase (TrpB) homologs, helped us generate candidates at the codon rather than the amino-acid level. The resulting designs expressed at levels substantially above their natural parents and exhibited altered thermostability and substrate promiscuity, indicating that nucleotide-level evolutionary signal carries information that protein language models discard. The second system, namely, StructBioReasoner extends generation into reasoning: hypotheses are proposed, ranked through tournament selection, and computationally validated by orchestrated tool calls spanning structure prediction, molecular dynamics, and stability and binding estimation, executed at scale on Aurora. Applied to intrinsically disordered cancer targets including NMNAT-2, LIN28B, and WHSC1, this approach revealed dynamic coupling between ordered and disordered pockets that static structure-based pipelines do not capture. Our intelligent design assistant (IDeA) then embeds these capabilities in an autonomous co-scientist for enzyme discovery and biosynthetic pathway optimization, targeting amide synthetases for polyamide production, grounded in federated DOE biological data and closed with robotic experimentation.

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