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Inhibitor ranking through QM based chelation calculations for virtual screening of HIV-1 RNase H inhibition.

Poongavanam V, Steinmann C, Kongsted J - PLoS ONE (2014)

Bottom Line: The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function.After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database.By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark.

ABSTRACT
Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furthermore, full protein fragment molecular orbital (FMO) calculations were conducted and subsequently analysed for individual residue stabilization/destabilization energy contributions to the overall binding affinity in order to better understand the true and false predictions. After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives. Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process.

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Comparison of Binding mode.Binding mode of ZINC72194123 (blue) and ZINC03871633 (yellow) is shown in ball and stick model with bound ligand (green). Important residues are highlighted, including magnesium ions (green sphere) and water molecules (cyan sphere).
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pone-0098659-g007: Comparison of Binding mode.Binding mode of ZINC72194123 (blue) and ZINC03871633 (yellow) is shown in ball and stick model with bound ligand (green). Important residues are highlighted, including magnesium ions (green sphere) and water molecules (cyan sphere).

Mentions: In order to assess the chelation calculation as a useful approach for virtual screening, we further tested the chelation calculation (scenario 1) with the ZINC Pharmer as a compound source [47]. The overall workflow of the virtual screening is shown in Figure 6A. Based on the previous study [18], we defined a five-point pharmacophore query for compound filtration in the ZINC Pharmer web tool, which results in 16905 compounds. After ligand preprocessing steps such as the 3D conformation generation (using the OMEGA tool from the OpenEye suite)[19], prediction of protonation states (using the Epik tool from the Schrodinger suite) and drug-like filter (using the FILTER program from the OpenEye [48]) all the compounds were docked into the RNH active site using Glide SP. Out of 3659 docked compounds, only 517 compounds had high docking scores (less than −6.00 kcal/mol), which were then manually checked with ADMET (absorption, distribution, metabolism, excretion and toxicity) filtration (properties generated from the QuickProp tool from the Schrodinger suite)[49] which results in 107 compounds. This set of compounds was used for the chelation calculation (using scenario 1). Of the 107 compounds, only six compounds had favorable chelation energy (a chelation energy less than zero kcal/mol) (Figure 6B). Hits such as ZINC72194123 (common name Baicalein) and ZINC03871633 have favorable docking score (<−7.00 kcal/mol) and chelation energy (<0 kcal/mol). The binding mode of these compounds revealed that both compounds are strongly coordinated with magnesium ions and hydrogen bond/π with His539. The overall structural features of these hits are very similar to the known RNH inhibitors as the majority of inhibitors possess a three-oxygen pharmcophore that strongly binds with magnesium ions, e.g. pyrimidinone, diketo, tropolone, N-hydroxyimide, diones [16]. The binding mode of the top hits is shown in Figure 7. In addition, from the literature we found that, Baicalein (a flavonoid compound) originally was isolated from Chinese herbal medicine (Scutellaria baicalensis Georgi) and potentially inhibits HIV-1 replication through various inhibition mechanisms e.g., integrase (possess similar binding site as RNH)[50], reverse transcriptase [51] and HIV-1 env [52] inhibitors. More importantly, Baicalein has not yet been reported as a RNH inhibitor, however, it has been reported as a nonspecific reverse transcriptase inhibitor. Therefore, we believe that Baicalein reverse transcriptase inhibition activity may be due to the effect of RNH inhibition and not polymerase inhibition, as this compound strongly binds with magnesium ions as it is binds with intergase enzyme as shown before [50].


Inhibitor ranking through QM based chelation calculations for virtual screening of HIV-1 RNase H inhibition.

Poongavanam V, Steinmann C, Kongsted J - PLoS ONE (2014)

Comparison of Binding mode.Binding mode of ZINC72194123 (blue) and ZINC03871633 (yellow) is shown in ball and stick model with bound ligand (green). Important residues are highlighted, including magnesium ions (green sphere) and water molecules (cyan sphere).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4045755&req=5

pone-0098659-g007: Comparison of Binding mode.Binding mode of ZINC72194123 (blue) and ZINC03871633 (yellow) is shown in ball and stick model with bound ligand (green). Important residues are highlighted, including magnesium ions (green sphere) and water molecules (cyan sphere).
Mentions: In order to assess the chelation calculation as a useful approach for virtual screening, we further tested the chelation calculation (scenario 1) with the ZINC Pharmer as a compound source [47]. The overall workflow of the virtual screening is shown in Figure 6A. Based on the previous study [18], we defined a five-point pharmacophore query for compound filtration in the ZINC Pharmer web tool, which results in 16905 compounds. After ligand preprocessing steps such as the 3D conformation generation (using the OMEGA tool from the OpenEye suite)[19], prediction of protonation states (using the Epik tool from the Schrodinger suite) and drug-like filter (using the FILTER program from the OpenEye [48]) all the compounds were docked into the RNH active site using Glide SP. Out of 3659 docked compounds, only 517 compounds had high docking scores (less than −6.00 kcal/mol), which were then manually checked with ADMET (absorption, distribution, metabolism, excretion and toxicity) filtration (properties generated from the QuickProp tool from the Schrodinger suite)[49] which results in 107 compounds. This set of compounds was used for the chelation calculation (using scenario 1). Of the 107 compounds, only six compounds had favorable chelation energy (a chelation energy less than zero kcal/mol) (Figure 6B). Hits such as ZINC72194123 (common name Baicalein) and ZINC03871633 have favorable docking score (<−7.00 kcal/mol) and chelation energy (<0 kcal/mol). The binding mode of these compounds revealed that both compounds are strongly coordinated with magnesium ions and hydrogen bond/π with His539. The overall structural features of these hits are very similar to the known RNH inhibitors as the majority of inhibitors possess a three-oxygen pharmcophore that strongly binds with magnesium ions, e.g. pyrimidinone, diketo, tropolone, N-hydroxyimide, diones [16]. The binding mode of the top hits is shown in Figure 7. In addition, from the literature we found that, Baicalein (a flavonoid compound) originally was isolated from Chinese herbal medicine (Scutellaria baicalensis Georgi) and potentially inhibits HIV-1 replication through various inhibition mechanisms e.g., integrase (possess similar binding site as RNH)[50], reverse transcriptase [51] and HIV-1 env [52] inhibitors. More importantly, Baicalein has not yet been reported as a RNH inhibitor, however, it has been reported as a nonspecific reverse transcriptase inhibitor. Therefore, we believe that Baicalein reverse transcriptase inhibition activity may be due to the effect of RNH inhibition and not polymerase inhibition, as this compound strongly binds with magnesium ions as it is binds with intergase enzyme as shown before [50].

Bottom Line: The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function.After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database.By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark.

ABSTRACT
Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furthermore, full protein fragment molecular orbital (FMO) calculations were conducted and subsequently analysed for individual residue stabilization/destabilization energy contributions to the overall binding affinity in order to better understand the true and false predictions. After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives. Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process.

Show MeSH