Towards <i>in silico</i> lead optimization: Scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Popov, Veljko M.; Yee, Wai-Yan; Anderson, Amy C.

doi:10.1002/prot.21201

Cited by 26 publications

(22 citation statements)

References 41 publications

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“…13 The chemical space available for a ligand can also be modeled through docking using an ensemble of structures to develop a pharmacophore for a flexible P450 molecule, as has been recently employed for dihydrofolate reductase. 52,53 Computational solvent-mapping studies revealed the importance of local conformational changes to the promiscuity of P450 enzymes. 54 This study rationalizes for the first time the malleability and promiscuity of ligand binding to P450 enzymes by considering the thermodynamic fidelity of the binding pocket.…”

Section: Discussionmentioning

confidence: 99%

Thermodynamic Fidelity of the Mammalian Cytochrome P450 2B4 Active Site in Binding Substrates and Inhibitors

Muralidhara¹,

Sun²,

Negi³

et al. 2008

Journal of Molecular Biology

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Section: Discussionmentioning

confidence: 99%

Thermodynamic Fidelity of the Mammalian Cytochrome P450 2B4 Active Site in Binding Substrates and Inhibitors

Muralidhara¹,

Sun²,

Negi³

et al. 2008

Journal of Molecular Biology

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“…There is now significant evidence showing that the use of an ensemble of target structures during docking improves enrichment and ligand ranking 1,3,10,34–37. There are many approaches to determining the appropriate weight for the individual ensemble member scores.…”

Section: Discussionmentioning

confidence: 99%

Scoring Ensembles of Docked Protein:Ligand Interactions for Virtual Lead Optimization

Paulsen

Anderson

2009

J. Chem. Inf. Model.

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Ensembles of protein structures to simulate protein flexibility are widely used throughout several applications including virtual lead optimization where they have been shown to improve ligand ranking. Yet, there is no established convention for weighting individual scores generated from ensemble members. To investigate the best method for weighting ensemble scores for proper ligand ranking, a series of dihydrofolate reductase inhibitors was docked to ensembles of Candida albicans dihydrofolate reductase (CaDHFR) structures created from a molecular dynamics (MD) simulation. From a single MD simulation, two ensemble collections were generated, one of which was subjected to a minimization procedure to create a group of structures of equal probability. As expected, ligand ranking accuracy was significantly improved when Boltzmann weighting was applied to the energies of this ensemble (60%), relative to that achieved with averaging (36%). However, accuracy was further improved (72%) by averaging docking scores across a minimized ensemble. To examine whether this accuracy results from structural variation in the single trajectory versus the possibility that error is minimized by averaging, a third collection of receptor structures was created in which each member was taken from an independent molecular dynamics simulation after minimization. Comparison of the docking accuracy results from the single trajectory (72%) to this third collection (61%) showed decreased accuracy, suggesting that ligands are more accurately oriented and assessed when docked to the minimized ensemble from a single MD trajectory, an effect that is more than simply error minimization. Averaging docking scores over a minimized ensemble of another target, influenza A neuraminidase, yielded a ligand ranking accuracy of 83%, representing a 24% improvement over other methods tested.

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“…Both methods failed (for details, see SI) to reproduce the X-ray structure of COX-1 bound AA as the energetically lowest configuration. In a recent study, Anderson and coworkers (40) showed reliable correlations between calculated and experimental biological activity when using a scoring function averaged over an ensemble of docking complexes. Similar to this concept, 50 snapshots were generated from the final section of the MD simulation, and the average binding affinity between ligand and COX-1 was computed using the ChemScore scoring function (41).…”

Section: Preparation Of Conditioned Media and Quantitation Of Vitaminmentioning

confidence: 99%

Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases

Jiang

Yin

Lill

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

166

154

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Cyclooxygenase (COX-1/COX-2)-catalyzed eicosanoid formation plays a key role in inflammation-associated diseases. Natural forms of vitamin E are recently shown to be metabolized to long-chain carboxychromanols and their sulfated counterparts. Here we find that vitamin E forms differentially inhibit COX-2-catalyzed prostaglandin E 2 in IL-1␤-stimulated A549 cells without affecting COX-2 expression, showing the relative potency of ␥-tocotrienol Ϸ ␦-tocopherol > ␥-tocopherol Ͼ Ͼ ␣-or ␤-tocopherol. The cellular inhibition is partially diminished by sesamin, which blocks the metabolism of vitamin E, suggesting that their metabolites may be inhibitory. Consistently, conditioned media enriched with long-chain carboxychromanols, but not their sulfated counterparts or vitamin E, reduce COX-2 activity in COX-preinduced cells with 5 M arachidonic acid as substrate. Under this condition, 9-or 13-carboxychromanol, the vitamin E metabolites that contain a chromanol linked with a 9-or 13-carbon-length carboxylated side chain, inhibits COX-2 with an IC 50 of 6 or 4 M, respectively. But 13-carboxychromanol inhibits purified COX-1 and COX-2 much more potently than shorter side-chain analogs or vitamin E forms by competitively inhibiting their cyclooxygenase activity with K i of 3.9 and 10.7 M, respectively, without affecting the peroxidase activity. Computer simulation consistently indicates that 13-carboxychromanol binds more strongly than 9-carboxychromanol to the substrate-binding site of COX-1. Therefore, long-chain carboxychromanols, including 13-carboxychromanol, are novel cyclooxygenase inhibitors, may serve as anti-inflammation and anticancer agents, and may contribute to the beneficial effects of certain forms of vitamin E.cancer ͉ inflammation ͉ PGE2 ͉ tocopherol ͉ tocotrienol C yclooxygenases (COX-1 and COX-2) catalyze the conversion of arachidonic acid (AA) to prostaglandin H 2 (PGH 2 ), the common precursor to prostaglandins and thromboxanes that are important lipid mediators for regulation of many physiological and pathophysiological responses (1). COXs are bifunctional enzymes that carry out two sequential activities-i.e., the cyclooxygenase activity, which leads to the formation of prostaglandin G 2 (PGG 2 ), and the peroxidase activity, which reduces PGG 2 to PGH 2 (2). COX-1 is constitutively expressed in many tissues, including platelets where thromboxanes are generated by this enzyme to promote platelet aggregation. COX-2 is often induced under acute/chronic inflammatory conditions and is mainly responsible for the generation of proinflammatory eicosanoids, including prostaglandin E 2 (PGE 2 ) (3). COX inhibitors, which are nonsteroidal anti-inflammatory drugs (NSAIDs), have been used for the relief of fever, pain, and inflammation (4). Chronic inflammation has been identified as a significant factor in the development of cancer (5). It is well established that NSAIDs are effective chemoprevention agents for cancer (6), although their long-term use has been questioned due to the associated gastrointestinal sid...

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Towards in silico lead optimization: Scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Cited by 26 publications

References 41 publications

Thermodynamic Fidelity of the Mammalian Cytochrome P450 2B4 Active Site in Binding Substrates and Inhibitors

Thermodynamic Fidelity of the Mammalian Cytochrome P450 2B4 Active Site in Binding Substrates and Inhibitors

Scoring Ensembles of Docked Protein:Ligand Interactions for Virtual Lead Optimization

Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases

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