Main Conference Day 1 - PT (Pacific Time, GMT-08:00)
- Brandon DeKosky, Ph.D. - Associate Professor of Chemical Engineering, MIT and The Ragon Institute
- Dima Kozakov, PhD - Professor, Stony Brook University
G protein-coupled receptors (GPCRs) are the largest class of membrane proteins in humans and serve as key drug targets for a wide variety of indications. Using a combination of computational and experimental screening methods we have developed agonistic and antagonistic VHH antibody fragments targeting GPCRs for potential therapeutic uses. These tools can also serve as the basis for bispecifics that can induce receptor degradation or control subcellular localization, enabling new and distinctive pharmacological activities compared to small-molecule ligands.
- Andrew Kruse - Professor of Biological Chemistry and Molecular Pharmacology, Harvard University
- Bowen Jing, PhD - Head of Machine Learning, Divergence Labs
While a vast number of antibody sequences have been reported in the literature, the majority remain uncharacterized. For instance, among over 5,000 influenza antibodies discovered to date, only 20% have defined epitope information. To bridge this gap, we have developed machine learning models and high-throughput experimental screening to analyze the epitopes and binding specificity of these uncharacterized antibodies. Our findings provide novel molecular insights into the landscape of influenza antibodies.
- Nicholas Wu - Assistant Professor, Department of Biochemistry, University of Illinois at Urbana-Champaign
Agonistic antibodies targeting co-stimulatory receptors represent a promising strategy for cancer immunotherapy. Using structure-guided disulfide engineering, we generated conformationally-restricted antibodies with enhanced agonism, by introducing non-native disulfides between F(ab) arms. An integrative approach combining structural, computational and cellular methods was used to validate and characterise our designs. These findings establish conformational tuning of immunostimulatory antibodies as a readily translatable strategy for the rational development of more effective anti-cancer therapeutics.
- Isabel Elliott, PhD - Postdoctoral Researcher, University of Southampton
- Dzmitry Padhorny, PhD - Research Assistant, The University of Texas at Austin
AI-generated antibodies raise new questions for patent drafting, inventorship, enablement, written description, and obviousness. This presentation explores how to build antibody patent portfolios around sequence differences, CDR modifications, epitope characterization, functional advantages, and experimental data that support protection in a changing legal landscape.
- Christopher Betti, PhD - Patent Lawyer, Morgan, Lewis & Bockius LLP
