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

Decherchi S, Berteotti A, Bottegoni G, Rocchia W, Cavalli A - Nat Commun (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 1] CONCEPT Lab, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy [2] BiKi Technologies s.r.l., via XX Settembre 33, 16121 Genova, Italy.

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.

No MeSH data available.


Schematic representation of the association process observed in our simulations.The main different observed states and paths are summarized here, as well as the interconversion times between them. As one can see, the direct binding to the final, long-lived state A was observed only once and took 340 ns. For simplicity, the intermediate state occupied during the ingress into the binding site is indicated only in the case when rebinding was observed.
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f7: Schematic representation of the association process observed in our simulations.The main different observed states and paths are summarized here, as well as the interconversion times between them. As one can see, the direct binding to the final, long-lived state A was observed only once and took 340 ns. For simplicity, the intermediate state occupied during the ingress into the binding site is indicated only in the case when rebinding was observed.

Mentions: Analyzing all the binding trajectories, the complexity of the mechanism of DADME binding to PNP clearly emerged (see Fig. 7). Once in 14 different simulations, the ligand directly reached the state A, which corresponds to the crystallographic structure. More often, DADME got stuck in metastable but still inhibitory states B and C, from which it could proceed towards A. However, the ligand often remained in B and C for the entire simulation or left these states for more solvent-exposed configurations and then re-entered, assuming a different metastable configuration, and eventually proceeding to A. In one simulation, DADME took the following route: OUT (full solvation) ->C->OUT (partial solvation) ->B->A (Fig. 7). Although the ligand did not completely escape out of the enzyme, this could be considered a rebinding event23. The association process prompted us to provide an estimation of the kon, as the time for first binding, as reported in the Methods section and discussed below.


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

Decherchi S, Berteotti A, Bottegoni G, Rocchia W, Cavalli A - Nat Commun (2015)

Schematic representation of the association process observed in our simulations.The main different observed states and paths are summarized here, as well as the interconversion times between them. As one can see, the direct binding to the final, long-lived state A was observed only once and took 340 ns. For simplicity, the intermediate state occupied during the ingress into the binding site is indicated only in the case when rebinding was observed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308819&req=5

f7: Schematic representation of the association process observed in our simulations.The main different observed states and paths are summarized here, as well as the interconversion times between them. As one can see, the direct binding to the final, long-lived state A was observed only once and took 340 ns. For simplicity, the intermediate state occupied during the ingress into the binding site is indicated only in the case when rebinding was observed.
Mentions: Analyzing all the binding trajectories, the complexity of the mechanism of DADME binding to PNP clearly emerged (see Fig. 7). Once in 14 different simulations, the ligand directly reached the state A, which corresponds to the crystallographic structure. More often, DADME got stuck in metastable but still inhibitory states B and C, from which it could proceed towards A. However, the ligand often remained in B and C for the entire simulation or left these states for more solvent-exposed configurations and then re-entered, assuming a different metastable configuration, and eventually proceeding to A. In one simulation, DADME took the following route: OUT (full solvation) ->C->OUT (partial solvation) ->B->A (Fig. 7). Although the ligand did not completely escape out of the enzyme, this could be considered a rebinding event23. The association process prompted us to provide an estimation of the kon, as the time for first binding, as reported in the Methods section and discussed below.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 1] CONCEPT Lab, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy [2] BiKi Technologies s.r.l., via XX Settembre 33, 16121 Genova, Italy.

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.

No MeSH data available.