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Identification by virtual screening and in vitro testing of human DOPA decarboxylase inhibitors.

Daidone F, Montioli R, Paiardini A, Cellini B, Macchiarulo A, Giardina G, Bossa F, Borri Voltattorni C - PLoS ONE (2012)

Bottom Line: Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD.Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects.To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemical Sciences A Rossi Fanelli, University of Rome La Sapienza, Rome, Italy.

ABSTRACT
Dopa decarboxylase (DDC), a pyridoxal 5'-phosphate (PLP) enzyme responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson's disease (PD). PD is a neurodegenerative disease mainly due to a progressive loss of dopamine-producing cells in the midbrain. Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD. Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects. Therefore, the main goals of this study were (a) to use virtual screening to identify potential human DDC inhibitors and (b) to evaluate the reliability of our virtual-screening (VS) protocol by experimentally testing the "in vitro" activity of selected molecules. Starting from the crystal structure of the DDC-carbidopa complex, a new VS protocol, integrating pharmacophore searches and molecular docking, was developed. Analysis of 15 selected compounds, obtained by filtering the public ZINC database, yielded two molecules that bind to the active site of human DDC and behave as competitive inhibitors with K(i) values ≥10 µM. By performing in silico similarity search on the latter compounds followed by a substructure search using the core of the most active compound we identified several competitive inhibitors of human DDC with K(i) values in the low micromolar range, unable to bind free PLP, and predicted to not cross the blood-brain barrier. The most potent inhibitor with a K(i) value of 500 nM represents a new lead compound, targeting human DDC, that may be the basis for lead optimization in the development of new DDC inhibitors. To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery.

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Pharmacophore hypothesis of a competive inhibitor of DDC, superimposed on the 1JS6 – carbidopa complex.The chemical features of the final pharmacophore essential for the binding of a competitive inhibitor to DDC are shown as spheres. Carbidopa is represented as balls and sticks. PLP, water molecules and residues interacting with carbidopa are also shown and labeled.
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pone-0031610-g001: Pharmacophore hypothesis of a competive inhibitor of DDC, superimposed on the 1JS6 – carbidopa complex.The chemical features of the final pharmacophore essential for the binding of a competitive inhibitor to DDC are shown as spheres. Carbidopa is represented as balls and sticks. PLP, water molecules and residues interacting with carbidopa are also shown and labeled.

Mentions: Then, within a set of already experimentally tested compounds [20], those showing no chirality center were included in a training set. On the basis of assays performed on a pig kidney extract [20], they were clustered in inactive (31 compounds causing a loss of DDC activity <10% at 2.2 mM concentration), poorly active (33 compounds causing a loss of DDC activity ≥10% at 2.2 mM concentration), moderately active (29 compounds causing a loss of DDC activity ≥10% at a concentration in the range 220–440 µM), and highly active (9 compounds causing a loss of DDC activity ≥10% at a concentration <110 µM). All active molecules (i.e., poorly, moderately and highly active compounds) were analyzed with the previously developed PH and the conformations of the matching molecules were processed in order to retrieve consensus chemical features (coordinates, radius and feature type). The latter, in turn, were included in the PH (as detailed in the “Materials and Methods section”). For this purpose, the annotation points of the active compounds matching the PH were clustered in ten chemical features (C1–C10, Table 1). C1–C8 coincided with F1–F8 in terms of position (root mean square deviation (RMSD): 0.13 Å) and type of feature, even if the radii of the spheres were slightly different (Table 1). Moreover, two new chemical features were identified: a hydrogen bond acceptor projection feature (C9) roughly pointing at His302 and a hydrogen bond acceptor projection feature (C10) pointing at two structural water molecules (hydrogen bonded to the carboxylate moiety of carbidopa in the DDC-carbidopa complex). The two new projection features, C9 and C10, were then added to the initial PH, and the radii of all the chemical features, F1 to F8, were subsequently adapted according to the values calculated by the consensus function for the C1–C8 features. Therefore, in order to assess which features could be regarded as essential for the subsequent pharmacophore searches (PSs), we generated several alternative PHs, each containing all but one feature, and compared their performance by carrying out PSs of the final training set (initial training set plus 1950 decoys; see “Materials and Methods section” for further details). The final obtained PH (Fig. 1), in which F1, F3 and C9 were set as essential, retrieved 48% active (30% poorly active, 55% moderately active and 88% highly active) and 6% inactive compounds, thus clearly showing a marked improvement as compared to the initial PH, with 27% active (12% poorly actives, 34% moderately active and 55% highly active) and 3% inactive compounds.


Identification by virtual screening and in vitro testing of human DOPA decarboxylase inhibitors.

Daidone F, Montioli R, Paiardini A, Cellini B, Macchiarulo A, Giardina G, Bossa F, Borri Voltattorni C - PLoS ONE (2012)

Pharmacophore hypothesis of a competive inhibitor of DDC, superimposed on the 1JS6 – carbidopa complex.The chemical features of the final pharmacophore essential for the binding of a competitive inhibitor to DDC are shown as spheres. Carbidopa is represented as balls and sticks. PLP, water molecules and residues interacting with carbidopa are also shown and labeled.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3285636&req=5

pone-0031610-g001: Pharmacophore hypothesis of a competive inhibitor of DDC, superimposed on the 1JS6 – carbidopa complex.The chemical features of the final pharmacophore essential for the binding of a competitive inhibitor to DDC are shown as spheres. Carbidopa is represented as balls and sticks. PLP, water molecules and residues interacting with carbidopa are also shown and labeled.
Mentions: Then, within a set of already experimentally tested compounds [20], those showing no chirality center were included in a training set. On the basis of assays performed on a pig kidney extract [20], they were clustered in inactive (31 compounds causing a loss of DDC activity <10% at 2.2 mM concentration), poorly active (33 compounds causing a loss of DDC activity ≥10% at 2.2 mM concentration), moderately active (29 compounds causing a loss of DDC activity ≥10% at a concentration in the range 220–440 µM), and highly active (9 compounds causing a loss of DDC activity ≥10% at a concentration <110 µM). All active molecules (i.e., poorly, moderately and highly active compounds) were analyzed with the previously developed PH and the conformations of the matching molecules were processed in order to retrieve consensus chemical features (coordinates, radius and feature type). The latter, in turn, were included in the PH (as detailed in the “Materials and Methods section”). For this purpose, the annotation points of the active compounds matching the PH were clustered in ten chemical features (C1–C10, Table 1). C1–C8 coincided with F1–F8 in terms of position (root mean square deviation (RMSD): 0.13 Å) and type of feature, even if the radii of the spheres were slightly different (Table 1). Moreover, two new chemical features were identified: a hydrogen bond acceptor projection feature (C9) roughly pointing at His302 and a hydrogen bond acceptor projection feature (C10) pointing at two structural water molecules (hydrogen bonded to the carboxylate moiety of carbidopa in the DDC-carbidopa complex). The two new projection features, C9 and C10, were then added to the initial PH, and the radii of all the chemical features, F1 to F8, were subsequently adapted according to the values calculated by the consensus function for the C1–C8 features. Therefore, in order to assess which features could be regarded as essential for the subsequent pharmacophore searches (PSs), we generated several alternative PHs, each containing all but one feature, and compared their performance by carrying out PSs of the final training set (initial training set plus 1950 decoys; see “Materials and Methods section” for further details). The final obtained PH (Fig. 1), in which F1, F3 and C9 were set as essential, retrieved 48% active (30% poorly active, 55% moderately active and 88% highly active) and 6% inactive compounds, thus clearly showing a marked improvement as compared to the initial PH, with 27% active (12% poorly actives, 34% moderately active and 55% highly active) and 3% inactive compounds.

Bottom Line: Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD.Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects.To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemical Sciences A Rossi Fanelli, University of Rome La Sapienza, Rome, Italy.

ABSTRACT
Dopa decarboxylase (DDC), a pyridoxal 5'-phosphate (PLP) enzyme responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson's disease (PD). PD is a neurodegenerative disease mainly due to a progressive loss of dopamine-producing cells in the midbrain. Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD. Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects. Therefore, the main goals of this study were (a) to use virtual screening to identify potential human DDC inhibitors and (b) to evaluate the reliability of our virtual-screening (VS) protocol by experimentally testing the "in vitro" activity of selected molecules. Starting from the crystal structure of the DDC-carbidopa complex, a new VS protocol, integrating pharmacophore searches and molecular docking, was developed. Analysis of 15 selected compounds, obtained by filtering the public ZINC database, yielded two molecules that bind to the active site of human DDC and behave as competitive inhibitors with K(i) values ≥10 µM. By performing in silico similarity search on the latter compounds followed by a substructure search using the core of the most active compound we identified several competitive inhibitors of human DDC with K(i) values in the low micromolar range, unable to bind free PLP, and predicted to not cross the blood-brain barrier. The most potent inhibitor with a K(i) value of 500 nM represents a new lead compound, targeting human DDC, that may be the basis for lead optimization in the development of new DDC inhibitors. To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery.

Show MeSH
Related in: MedlinePlus