Abstract PL05-03: What if all your favorite proteins are druggable

Abstract

Abstract Many proteins that play key roles in cancer are considered difficult drug targets, if not entirely undruggable, due to an apparent lack of pockets where small molecules can bind with the affinity and specificity required for a drug. ‘Cryptic’ pockets that are absent in known structures of proteins but form due to protein dynamics could alleviate this problem. However, it has been hard to assess or exploit the opportunities that cryptic pockets present due to the inherent difficulty in identifying such pockets. Here, I will discuss progress from my lab on combining biophysical experiments, computer simulations, and machine learning to identify and target cryptic pockets. In one case study, we have used computer simulations deployed on the Folding@home distributed computing environment to predict a cryptic pocket that can allosterically control a protein-RNA interaction, and then experimentally confirmed our predictions. Based on data from this study and others in our lab, we have trained a machine learning algorithm to predict where cryptic pockets are likely to form from single protein structures, enabling us to assess how prevalent cryptic pockets are on a large scale. Citation Format: Gregory R. Bowman. What if all your favorite proteins are druggable. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr PL05-03.