Boltzmann Docking Identifies Allosteric Small Molecule Modulators of Protein Activity

Abstract

Computational approaches can facilitate the design of clinically relevant small molecule inhibitors. Classical drug design focuses primarily on targeting a protein's active site based on its x-ray crystal structure. One drawback to this approach is crystal structures do not report on protein dynamics and do not capitalize on the presence of hidden cryptic pockets. Cryptic pockets are transient allosteric sites invisible to conventional structural biology techniques and present new opportunities for drug design. Here we introduce Boltzmann docking as a method to identify small molecules that bind to cryptic pockets and allosterically modulate enzymatic activity. We leverage knowledge of protein dynamics from molecular dynamics simulations and Markov State Models (MSMs) to identify cryptic pockets. MSMs are a network representation of a protein's free energy landscape that provides representative states and their populations. Boltzmann docking screens a library of small molecules against multiple MSM states containing a target cryptic pocket and generates a new Boltzmann score that is a population weighted average of the individual states’ docking scores. We successfully applied this approach to identify multiple allosteric modulators of TEM β-lactamase activity, and are currently looking to identify novel inhibitors of the Ebola viral protein VP35. Boltzmann docking should provide a general framework to identify allosteric modulators of protein activity, especially for targets widely considered “undruggable” owing to the lack of an obvious binding site in their crystal structures.