CoMFA/CoMSIA and Pharmacophore Modelling as a Powerful Tools for Efficient Virtual Screening: Application to Anti-Leishmanial Betulin Derivatives

Ghemtio, Léo; Zhang, Yuezhou; Xhaard, Henri

doi:10.5772/36690

Cited by 9 publications

(10 citation statements)

References 40 publications

(39 reference statements)

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“…However, the CoMSIA method has been developed to make usage of hydrophobic fields in addition to the electrostatic fields. It can improve these inherent deficiencies arising from the CoMFA method [56]. Accordingly, there is 27.1% of hydrophobic field in addition to steric field (10.4%) in the generated CoMSIA model.…”

Section: Resultsmentioning

confidence: 99%

Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

et al. 2016

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11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a potential target for the treatment of numerous human disorders, such as diabetes, obesity, and metabolic syndrome. In this work, molecular modeling studies combining molecular docking, 3D-QSAR, MESP, MD simulations and free energy calculations were performed on pyridine amides and 1,2,4-triazolopyridines as 11β-HSD1 inhibitors to explore structure-activity relationships and structural requirement for the inhibitory activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by docking strategy. The derived pharmacophoric features were further supported by MESP and Mulliken charge analyses using density functional theory. In addition, MD simulations and free energy calculations were employed to determine the detailed binding process and to compare the binding modes of inhibitors with different bioactivities. The binding free energies calculated by MM/PBSA showed a good correlation with the experimental biological activities. Free energy analyses and per-residue energy decomposition indicated the van der Waals interaction would be the major driving force for the interactions between an inhibitor and 11β-HSD1. These unified results may provide that hydrogen bond interactions with Ser170 and Tyr183 are favorable for enhancing activity. Thr124, Ser170, Tyr177, Tyr183, Val227, and Val231 are the key amino acid residues in the binding pocket. The obtained results are expected to be valuable for the rational design of novel potent 11β-HSD1 inhibitors.

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Section: Resultsmentioning

confidence: 99%

Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

et al. 2016

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“…The 3D QSAR CoMFA and CoMSIA methodologies have emerged as fundamental tools for design and molecular optimization of drug candidates targeting GPCRs. The CoMFA methodology provides information of whether differences in steric (Lennard-Jones potential functions) and electrostatic components (Coulomb potential functions) for field calculation of a training set of molecules in a given alignment can be correlated with differences in biological/pharmacological activity [502-504]. A comparable 3D QSAR-based methodology, CoMSIA, was developed based on arbitrary descriptors named similarity indices.…”

Section: G-protein-coupled Receptors As Thera-peutic Targets For Parkmentioning

confidence: 99%

In Silico Studies Targeting G-protein Coupled Receptors for Drug Research Against Parkinson’s Disease

Lemos

Melo

Preto

et al. 2018

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Parkinson's Disease (PD) is a long-term neurodegenerative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel antiparkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated with long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt to counteract the motor side effects associated with dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused on a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure- and ligand-based in silico approaches for the development of small molecules to target these receptors.

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“…Applying an appropriate statistical method for regression analysis, usually by PLS, the 3D-QSAR model is constructed to describe the variation of biological/pharmacological activity with the variation of CoMFA/CoMSIA interaction fields and the predictive ability of 3D-QSAR model is corroborated by cross-validation and prediction of activity of test set. The generated CoMFA/CoMSIA is typically represented as color-coded contoured 3D maps, which displays specific volumes of space where the magnitudes of steric, electrostatic, hydrophobic, hydrogen bond acceptor, and hydrogen bond donor parameters are positively or negatively correlated with the pharmacological activity [484][485][486][487]. This type of graphical representation can be presumed as a model of the binding site in which a training set of ligands is supposed to interact.…”

Section: Application Of Ligand-based and Pharmacophore-based Design Tmentioning

confidence: 99%

“…The external validation consists in the prediction of biological/pharmacological activity using a group of ligands that are not included in the training set (test set) and the same descriptor variables are employed in the generation of the QSAR model[483].The 3D QSAR CoMFA and CoMSIA methodologies have emerged as fundamental tools for design and molecular optimization of drug candidates targeting GPCRs. The CoMFA methodology provides information of whether differences in steric (Lennard-Jones potential functions) and electrostatic components (Coulomb potential functions) for field calculation of a training set of molecules in a given alignment can be correlated with differences in biological/pharmacological activity[484][485][486]. A comparable 3D QSAR-based methodology, CoMSIA, was developed based on arbitrary descriptors named similarity indices.…”

mentioning

confidence: 99%

Computer-Aided Drug Design Approaches to Study Key Therapeutic Targets in Alzheimer’s Disease

et al. 2017

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Parkinson's Disease (PD) is a long-term neurodegenative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel anti-parkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated to long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt to counteract the motor side effects associated to dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused in a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure-and ligand-based in silico approaches for the development of small molecules to target these receptors.

show abstract

CoMFA/CoMSIA and Pharmacophore Modelling as a Powerful Tools for Efficient Virtual Screening: Application to Anti-Leishmanial Betulin Derivatives

Cited by 9 publications

References 40 publications

Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

In Silico Studies Targeting G-protein Coupled Receptors for Drug Research Against Parkinson’s Disease

Computer-Aided Drug Design Approaches to Study Key Therapeutic Targets in Alzheimer’s Disease

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