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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.


Structural representation of intermediate binding configurations along the gating mechanism.(a) DADME (in CPK representation) on the PNP surface. No specific or transient interactions with Glu259 were identified at this stage of binding. (b) DADME interacting with PNP before gate opening and entrance into the enzyme. At this stage, an H-bond with Thr242 and a transient interaction with Glu259 could be identified. (c) DADME entering the binding site of PNP right after the gate opening. Here the ligand is quite well stabilized by specific interactions with Pro190 (H-bond) and with Phe200 (parallel π–π stacking). (d) DADME into the PNP binding pocket assuming the conformation of the bound state, that is, that observed in ensemble A (see Fig. 3).
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f5: Structural representation of intermediate binding configurations along the gating mechanism.(a) DADME (in CPK representation) on the PNP surface. No specific or transient interactions with Glu259 were identified at this stage of binding. (b) DADME interacting with PNP before gate opening and entrance into the enzyme. At this stage, an H-bond with Thr242 and a transient interaction with Glu259 could be identified. (c) DADME entering the binding site of PNP right after the gate opening. Here the ligand is quite well stabilized by specific interactions with Pro190 (H-bond) and with Phe200 (parallel π–π stacking). (d) DADME into the PNP binding pocket assuming the conformation of the bound state, that is, that observed in ensemble A (see Fig. 3).

Mentions: Three different binding routes were obtained and named upper, frontal and gating (see Supplementary Movies 2–4 for representative movies of each binding route and Fig. 5 for representative configurations of the entrance via the gating mechanism. See also Supplementary Figs 6,7, and 8 for the clustering of every single observed path). Notably, there was not an exclusive relationship between entrance pathways and final ensembles, that is, each binding route could lead to ensemble A, B, or C. Upper and frontal routes were intuitive and quite similar: in both cases, the α-helix facing the binding site partially lost its kink and allowed the ligand to enter the binding site either from above the phosphate or from a ‘frontal’ entrance, located at the interface between two monomers. The third binding path (gating) was somewhat unexpected: the ligand passed through a gap between the α-helix and the loop facing the binding site. This passage did not always require the α-helix to lose its kink. The gating route led to the final binding configuration state, where an RMSD of 0.59 Å versus the crystallographic structure was observed.


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)

Structural representation of intermediate binding configurations along the gating mechanism.(a) DADME (in CPK representation) on the PNP surface. No specific or transient interactions with Glu259 were identified at this stage of binding. (b) DADME interacting with PNP before gate opening and entrance into the enzyme. At this stage, an H-bond with Thr242 and a transient interaction with Glu259 could be identified. (c) DADME entering the binding site of PNP right after the gate opening. Here the ligand is quite well stabilized by specific interactions with Pro190 (H-bond) and with Phe200 (parallel π–π stacking). (d) DADME into the PNP binding pocket assuming the conformation of the bound state, that is, that observed in ensemble A (see Fig. 3).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Structural representation of intermediate binding configurations along the gating mechanism.(a) DADME (in CPK representation) on the PNP surface. No specific or transient interactions with Glu259 were identified at this stage of binding. (b) DADME interacting with PNP before gate opening and entrance into the enzyme. At this stage, an H-bond with Thr242 and a transient interaction with Glu259 could be identified. (c) DADME entering the binding site of PNP right after the gate opening. Here the ligand is quite well stabilized by specific interactions with Pro190 (H-bond) and with Phe200 (parallel π–π stacking). (d) DADME into the PNP binding pocket assuming the conformation of the bound state, that is, that observed in ensemble A (see Fig. 3).
Mentions: Three different binding routes were obtained and named upper, frontal and gating (see Supplementary Movies 2–4 for representative movies of each binding route and Fig. 5 for representative configurations of the entrance via the gating mechanism. See also Supplementary Figs 6,7, and 8 for the clustering of every single observed path). Notably, there was not an exclusive relationship between entrance pathways and final ensembles, that is, each binding route could lead to ensemble A, B, or C. Upper and frontal routes were intuitive and quite similar: in both cases, the α-helix facing the binding site partially lost its kink and allowed the ligand to enter the binding site either from above the phosphate or from a ‘frontal’ entrance, located at the interface between two monomers. The third binding path (gating) was somewhat unexpected: the ligand passed through a gap between the α-helix and the loop facing the binding site. This passage did not always require the α-helix to lose its kink. The gating route led to the final binding configuration state, where an RMSD of 0.59 Å versus the crystallographic structure was observed.

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.