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

Sergio Decherchi, Anna Berteotti, Giovanni Bottegoni, Walter Rocchia () and Andrea Cavalli ()
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Sergio Decherchi: CONCEPT Lab, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
Anna Berteotti: CompuNet, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
Giovanni Bottegoni: CompuNet, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
Walter Rocchia: CONCEPT Lab, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
Andrea Cavalli: CompuNet, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: 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 kon 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.

Date: 2015
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DOI: 10.1038/ncomms7155

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