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

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The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. / Decherchi, Sergio; Berteotti, Anna; Bottegoni, Giovanni; Rocchia, Walter; Cavalli, Andrea.

In: Nature Communications, Vol. 6, 6155, 27.01.2015.

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@article{929c32c599a849c0bb53e906f4d37f68,
title = "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.",
author = "Sergio Decherchi and Anna Berteotti and Giovanni Bottegoni and Walter Rocchia and Andrea Cavalli",
year = "2015",
month = jan
day = "27",
doi = "10.1038/ncomms7155",
language = "English",
volume = "6",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning

AU - Decherchi, Sergio

AU - Berteotti, Anna

AU - Bottegoni, Giovanni

AU - Rocchia, Walter

AU - Cavalli, Andrea

PY - 2015/1/27

Y1 - 2015/1/27

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84923096526&partnerID=8YFLogxK

U2 - 10.1038/ncomms7155

DO - 10.1038/ncomms7155

M3 - Article

C2 - 25625196

AN - SCOPUS:84923096526

VL - 6

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 6155

ER -