Development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years we and others have<br/>developed several techniques based on molecular dynamics simulations: MD-Markov state models<br/>to identify and capture network theory approach utilizing the interaction energy within the protein to understand how allosteric control is conferred. We focus on the key case study of p53 Y220C mutation restoration by the compound PK11000, a compound experimentally shown to reactivate<br/>p53 native function in suppressing tumors. We gain insights into the mutation and allosteric reactivation of the protein, which we anticipate will be applicable to do novo design to engineer new compounds not only for this mutation, but others, and in other macromolecular systems as well.

Application of molecular dynamics and machine learning toward rational drug designof allosteric reactivators

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.