The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning

Abstract

The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe–immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as k _on and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe–immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug–target recognition and binding.

Abstract

Understanding the dynamics of enzyme-substrate complexation provides an insight into potential drugs, but intermediate states are difficult to observe experimentally. Here, the authors use simulations and machine learning to analyse the binding of transition state inhibitors to purine nucleoside phosphorylase.