miniWebinar | Drugging the undruggable: A highly accurate method for detecting and ranking cryptic pockets

This session, on August 14th, will be presented by David LeBard, Head of Science at OpenEye. 

David has a background in both theoretical and computational modeling of complex biological systems and works to foster industrial and academic collaborations that find solutions to problems in drug discovery, including modeling of passive permeability of drug-like molecules, rare-event sampling of biomolecules, and cryptic pocket detection in proteins.

About this session

Some proteins involved in life-threatening diseases have proved difficult to treat simply because a binding pocket for a molecular inhibitor could not be found. One classic example is the KRAS protein, which is a GTPase involved in more than 25% of all human cancers. KRAS was thought to be undruggable for over 30 years despite immense efforts by academic and industrial researchers to find a viable pocket. To help expand the druggable proteome to include difficult-to-drug targets like KRAS, we present an automated workflow that allows non-experts to investigate a protein for its ligandability. With only a protein structure (X-Ray, Cryo-EM, Al-generated, etc.), our workflow uses Weighted Ensemble path sampling to sample rare protein conformational states, which are analyzed with a set of Markov state models to identify residues that cooperatively form pockets. Furthermore, the workflow also ranks predicted pockets by their ligandability using a neural network model estimator of the potential to bind a ligand in the pocket. This workflow can uncover pockets that form either by conformational selection of rare protein states or through an induced-fit mechanism by a probe molecule. Here, we present a proof-of-concept study of KRASG12D to illustrate that our methodology can predict known cryptic pockets, including the Switch-II pocket that remained hidden for decades. Finally, a validation of our automated protein sampling and cryptic pocket detection workflow on a set of 19 proteins that represent 24 unique pocket types will also be presented. 

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