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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.


RMSD of DADMe–immucillin-H with changing simulation time.Non-hydrogen atom displacement was monitored over the simulation time relative to the crystal structure. Colour encodes the simulation runs. All the simulations reported in the plot led to binding.
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f2: RMSD of DADMe–immucillin-H with changing simulation time.Non-hydrogen atom displacement was monitored over the simulation time relative to the crystal structure. Colour encodes the simulation runs. All the simulations reported in the plot led to binding.

Mentions: In our simulation set-up, a PNP trimer, the biological functional unit, and 9 ligands freely evolve in a cubic box full of explicit water molecules, summing up to about 100,000 atoms. Out of 14 runs, we identified 11 events that can be ascribed to binding. To monitor the binding process, we used the root mean square deviation (RMSD) of the heavy atoms of the ligand after superimposing the backbone binding site residues onto the reference structure, represented by the PNP monomer in complex with DADME (PDB entry 1RSZ) (Fig. 2).


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)

RMSD of DADMe–immucillin-H with changing simulation time.Non-hydrogen atom displacement was monitored over the simulation time relative to the crystal structure. Colour encodes the simulation runs. All the simulations reported in the plot led to binding.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: RMSD of DADMe–immucillin-H with changing simulation time.Non-hydrogen atom displacement was monitored over the simulation time relative to the crystal structure. Colour encodes the simulation runs. All the simulations reported in the plot led to binding.
Mentions: In our simulation set-up, a PNP trimer, the biological functional unit, and 9 ligands freely evolve in a cubic box full of explicit water molecules, summing up to about 100,000 atoms. Out of 14 runs, we identified 11 events that can be ascribed to binding. To monitor the binding process, we used the root mean square deviation (RMSD) of the heavy atoms of the ligand after superimposing the backbone binding site residues onto the reference structure, represented by the PNP monomer in complex with DADME (PDB entry 1RSZ) (Fig. 2).

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.