Investigation of Neuraminidase-Substrate Recognition Using Molecular Dynamics and Free Energy Calculations.

Masukawa, Kevin M.; Kollman, Peter A.; Kuntz, Irwin D.

doi:10.1021/jm040021u

Cited by 3 publications

(3 citation statements)

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“…differences in binding site protonation states, 16 differences between binding states in low-temperature crystallographic and room-temperature assay conditions, 43 and differences in internal strain contributions to binding. 44 Compound 4 has most of the same R groups as compound 1, with the difference being the removal of the hydroxyl group at position 2 and a change in bond order, as described previously. The difference between the reference and optimal charges at the four R groups is essentially the same as found in compound 1, except for at the hydroxyl group at position 7, where the group is significantly closer to the optimum (see Figure 4B).…”

Section: Optimal Charges In Lead Progressionmentioning

confidence: 99%

Optimal Charges in Lead Progression: A Structure-Based Neuraminidase Case Study

2006

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Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r 2 ) 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.

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Section: Optimal Charges In Lead Progressionmentioning

confidence: 99%

Optimal Charges in Lead Progression: A Structure-Based Neuraminidase Case Study

2006

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“…Owing to the accuracy of such force-fields, MD simulations have been used to study protein folding [72,73], conformational changes of DNA [74,75], orientation of phospholipids in bilayer membranes [76,77], active transport of drug molecules across membranes [78], physical characteristics of solvents [79] and surfaces [80,81], as well as biomolecular interactions [82][83][84].…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Theoretical and Computational Strategies for the Study of the Molecular Imprinting Process and Polymer Performance

Nicholls

Chavan

Golker

et al. 2015

Molecularly Imprinted Polymers in Biotechnology

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The development of in silico strategies for the study of the molecular imprinting process and the properties of molecularly imprinted materials has been driven by a growing awareness of the inherent complexity of these systems and even by an increased awareness of the potential of these materials for use in a range of application areas. Here we highlight the development of theoretical and computational strategies that are contributing to an improved understanding of the mechanisms underlying molecularly imprinted material synthesis and performance, and even their rational design.

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“…Cytochrome P450 isoforms are major drug-metabolizing enzymes and have become focal points in the study of rapid metabolism and drug-drug interactions [11,12] . Several groups have developed structure-based approaches for the prediction of compounds that would be metabolized by or inhibit P450s, and various homology models of human P450 isoforms have been generated for these purposes as templates for docking to predict drug metabolism [13][14][15][16] .…”

Section: Absorption Distribution Metabolism and Excretion Propertiesmentioning

confidence: 99%

Application of Docking Methods: An Effective In Silico Tool for Drug Design

Kulkarni¹,

Madhavan²

2013

Journal of the Chosun Natural Science

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Using computational approaches we can dock small molecules into the structures of Macromolecular targets and then score their potential complementarity to binding sites is widely used in hit identification and lead optimization techniques. This review seeks to provide the application of docking in structure-based drug design (binding mode prediction, Lead Identification and Lead optimization), and also discussed how to manage errors in docking methodology in order to overcome certain limitations of docking and scoring algorithm.

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Investigation of Neuraminidase-Substrate Recognition Using Molecular Dynamics and Free Energy Calculations.

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Cited by 3 publications

References 0 publications

Optimal Charges in Lead Progression: A Structure-Based Neuraminidase Case Study

Optimal Charges in Lead Progression: A Structure-Based Neuraminidase Case Study

Theoretical and Computational Strategies for the Study of the Molecular Imprinting Process and Polymer Performance

Application of Docking Methods: An Effective In Silico Tool for Drug Design

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