Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: Application to Influenza neuraminidase
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| Title: | Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: Application to Influenza neuraminidase |
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| Authors: | Bonnet, Pascal, Bryce, Richard A. R.A.Bryce@manchester.ac.uk |
| Source: | Journal of Molecular Graphics & Modelling. Oct2005, Vol. 24 Issue 2, p147-156. 10p. |
| Subjects: | Influenza, Binding energy, Molecular dynamics, Electrostatics |
| Abstract: | Abstract: We explore a perturbative approach to calculation of binding free energy of multiple ligands, based on a single molecular dynamics simulation of a reference ligand–receptor complex and analysis via a hybrid force field/continuum model potential. The methodology is applied to prediction of relative binding free energies of 10 Influenza neuraminidase inhibitors, using Poisson–Boltzmann and generalised Born models of implicit solvent. These single-step MM-PB/SA and MM-GB/SA approaches predict the experimentally most potent ligand as first- or second-ranked according to total binding free energy. Ranking of inhibitors displays only moderate sensitivity to the choice of reference trajectory and ligand partial charge scheme. When ranked according to total electrostatic binding free energy, correlation with experiment improves (r 2 of 0.72); this may be related to underestimated first solvation shell effects by the implicit water models. Therefore, to increase the generality of this single-step approach as part of a potential computational compound optimisation strategy, further development of the treatment of short-range solvent interactions is warranted. [Copyright &y& Elsevier] |
| Copyright of Journal of Molecular Graphics & Modelling is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 18630138 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: Application to Influenza neuraminidase – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bonnet%2C+Pascal%22">Bonnet, Pascal</searchLink><br /><searchLink fieldCode="AR" term="%22Bryce%2C+Richard+A%2E%22">Bryce, Richard A.</searchLink><i> R.A.Bryce@manchester.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Molecular+Graphics+%26+Modelling%22">Journal of Molecular Graphics & Modelling</searchLink>. Oct2005, Vol. 24 Issue 2, p147-156. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Influenza%22">Influenza</searchLink><br /><searchLink fieldCode="DE" term="%22Binding+energy%22">Binding energy</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Electrostatics%22">Electrostatics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: We explore a perturbative approach to calculation of binding free energy of multiple ligands, based on a single molecular dynamics simulation of a reference ligand–receptor complex and analysis via a hybrid force field/continuum model potential. The methodology is applied to prediction of relative binding free energies of 10 Influenza neuraminidase inhibitors, using Poisson–Boltzmann and generalised Born models of implicit solvent. These single-step MM-PB/SA and MM-GB/SA approaches predict the experimentally most potent ligand as first- or second-ranked according to total binding free energy. Ranking of inhibitors displays only moderate sensitivity to the choice of reference trajectory and ligand partial charge scheme. When ranked according to total electrostatic binding free energy, correlation with experiment improves (r 2 of 0.72); this may be related to underestimated first solvation shell effects by the implicit water models. Therefore, to increase the generality of this single-step approach as part of a potential computational compound optimisation strategy, further development of the treatment of short-range solvent interactions is warranted. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Molecular Graphics & Modelling is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jmgm.2005.06.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 147 Subjects: – SubjectFull: Influenza Type: general – SubjectFull: Binding energy Type: general – SubjectFull: Molecular dynamics Type: general – SubjectFull: Electrostatics Type: general Titles: – TitleFull: Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: Application to Influenza neuraminidase Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bonnet, Pascal – PersonEntity: Name: NameFull: Bryce, Richard A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 10933263 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Journal of Molecular Graphics & Modelling Type: main |
| ResultId | 1 |
