Scientist Stories: Stuart Schreiber, Molecular Glues & Bifunctional Molecules

9 Jun 2023 · 74 min
1:13:46

In a recorded Andrews University seminar, Professor Stuart Schreiber (Harvard/Broad Institute) delivers a multifaceted talk that intertwines personal narrative, mentorship philosophy, and cutting‑edge chemical biology. He begins with a striking genetic‑genealogy map of his paternal Y‑chromosome lineage, tracing ancestors from 30,000‑year‑old Nigerians through Central Asia to modern‑day Hungary, and recounts discovering his maternal and paternal families via genetic genealogy, revealing new siblings and a late‑discovered biological father. Schreiber credits his mother, a Cajun “uplifter,” for his resilience and co‑founds the Tamarind Foundation to support similar mentors. He shares career advice emphasizing passion, long‑term commitment, and embracing failure, citing influences from Yoshido Kishi and Theodore Roosevelt. The scientific core introduces molecular glues—small molecules that cooperatively bind two proteins (e.g., FK506, rapamycin, lenalidomide)—and bifunctional compounds that link two binders but can suffer a “hook effect.” He explains how missense mutations and post‑translational modifications mimic glue behavior, and illustrates therapeutic applications such as Vertex’s Trikafta for cystic fibrosis and PROTAC‑style degraders. Schreiber describes his laboratory’s diversity‑oriented synthesis (DOS) and DNA‑encoded library (DEL) platforms that generate three‑dimensional, stereochemically rich libraries, enabling rapid discovery of binders, bifunctionals, and molecular glues via barcoded screening and cooperativity metrics (α factor). Recent examples include a VHL‑BRD4 bifunctional degrader and a highly cooperative glue identified through ternary enrichment. He outlines future directions toward tissue‑specific glues, especially brain‑restricted FKBP12.6‑targeting, and mentions a new venture, Magnet Biomedicine, to develop CNS medicines. Finally, he discusses the emerging role of AI and large language models as assistants for hypothesis generation, while cautioning against over‑hype, and clarifies the collaborative structure of his Harvard appointment and Broad Institute labs.

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