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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.


Free energy estimation for the three different binding paths towards different binding poses.(a) Free energy path associated to the gating mechanism leading to ensemble A. (b) Free energy path associated to the frontal mechanism leading to ensemble A. (c) Free energy path associated to the upper mechanism leading to ensemble C. When leading to the crystallographic pose (ensemble A) the overall free energy difference between bound and unbound is about 13–14 kcal mol−1, whereas ensemble C is a kind of loose pre-binding state. Gating and frontal are well characterized paths, while the upper path exhibits a less featured profile.
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f6: Free energy estimation for the three different binding paths towards different binding poses.(a) Free energy path associated to the gating mechanism leading to ensemble A. (b) Free energy path associated to the frontal mechanism leading to ensemble A. (c) Free energy path associated to the upper mechanism leading to ensemble C. When leading to the crystallographic pose (ensemble A) the overall free energy difference between bound and unbound is about 13–14 kcal mol−1, whereas ensemble C is a kind of loose pre-binding state. Gating and frontal are well characterized paths, while the upper path exhibits a less featured profile.

Mentions: In Fig. 6, the free energy profiles along the three paths (gating, frontal and upper) are reported. The gating mechanism (Fig. 6a) showed two minima before reaching the final binding configuration, which corresponds to ensemble A. These two minima were representative of DADME in the gate pointing towards the solvent and towards the binding site, respectively (see also Fig. 5 for a structural representation of these intermediate states). The final state corresponded to the crystallographic pose, and the free energy difference between the initial (DADME in the solvent) and the final (the crystal pose) states was estimated to be about 13 kcal mol−1, in good agreement with the experimental Ki of 9 pM. Notably, the barrier for breaking the pivotal H-bond network established between DADME and the residues of PNP catalytic site turned out to be about 10 kcal mol−1, in remarkably good agreement with the experimental value reported by Kicska et al.18 on removal of the bi-dentate interaction.


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)

Free energy estimation for the three different binding paths towards different binding poses.(a) Free energy path associated to the gating mechanism leading to ensemble A. (b) Free energy path associated to the frontal mechanism leading to ensemble A. (c) Free energy path associated to the upper mechanism leading to ensemble C. When leading to the crystallographic pose (ensemble A) the overall free energy difference between bound and unbound is about 13–14 kcal mol−1, whereas ensemble C is a kind of loose pre-binding state. Gating and frontal are well characterized paths, while the upper path exhibits a less featured profile.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: Free energy estimation for the three different binding paths towards different binding poses.(a) Free energy path associated to the gating mechanism leading to ensemble A. (b) Free energy path associated to the frontal mechanism leading to ensemble A. (c) Free energy path associated to the upper mechanism leading to ensemble C. When leading to the crystallographic pose (ensemble A) the overall free energy difference between bound and unbound is about 13–14 kcal mol−1, whereas ensemble C is a kind of loose pre-binding state. Gating and frontal are well characterized paths, while the upper path exhibits a less featured profile.
Mentions: In Fig. 6, the free energy profiles along the three paths (gating, frontal and upper) are reported. The gating mechanism (Fig. 6a) showed two minima before reaching the final binding configuration, which corresponds to ensemble A. These two minima were representative of DADME in the gate pointing towards the solvent and towards the binding site, respectively (see also Fig. 5 for a structural representation of these intermediate states). The final state corresponded to the crystallographic pose, and the free energy difference between the initial (DADME in the solvent) and the final (the crystal pose) states was estimated to be about 13 kcal mol−1, in good agreement with the experimental Ki of 9 pM. Notably, the barrier for breaking the pivotal H-bond network established between DADME and the residues of PNP catalytic site turned out to be about 10 kcal mol−1, in remarkably good agreement with the experimental value reported by Kicska et al.18 on removal of the bi-dentate interaction.

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.