Analysis of inhibitor binding in influenza virus neuraminidase

Smith, B.J.

doi:10.1110/ps.41801

Cited by 93 publications

(111 citation statements)

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“…This method improves the time of calculation than if all of trajectories saved were calculated. The results are known similar to other studies that calculated the binding free energy of NA-inhibitor complexes with different approaches [25,26]. In the explicit solvation systems, complex molecules calculated without any implicit solvation energetic contribution while in the implicit solvation, the calculation executed with the addition of GBMV implicit solvation energy.…”

Section: The Hydrogen Bonds Between Na Residues and Sasupporting

confidence: 85%

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Pathogenicity characterization with implicit and explicit molecular dynamics simulation

Herlambang¹,

Saleh²

2011

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The contribution of water molecules in molecular dynamics simulation (MDS) is unquestionably high, particularly for enzymatic interaction which occurred in the cytoplasmic environment. The addition of water molecules to the system will surely influence different direct interaction between active site residues and substrate. We try to theoretically investigate to what extent the pathogenicity characterization will varies in different neuraminidase-sialic acid complex systems. The heating dynamics simulations were produced with and without TIP3P water molecules. The periodic boundary system was made for explicitly added TIP3P water molecules and generalized born molecular volume (GBMV) energy contribution was added for implicit solvent system. Both complexes, neuraminidase-sialic acid of A/Tokyo/3/67 and A/Pennsylvania/10218/ 84, which have a different pathogenicity levels were minimized and simulated. The result shows more residues produced hydrogen bonds with substrate when water molecules were not added to the system. The binding free energies also show differences. Overall, even the values of energy is different, but an implicit solvent provides the similar result (HPAI complex has higher activity than LPAI for both systems) in characterization of pathogenic virus neuraminidase activity.

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Section: The Hydrogen Bonds Between Na Residues and Sasupporting

confidence: 85%

“…The results show the values of -1.37 kcal/mol and 17.97 kcal/mol for HPAI and LPAI, respectively. Compared by other studies [25,26,31], the calculation with GBMV is at their results range (-21.7 kcal/mol to 7.3 kcal/mol). This is indicate that our…”

Section: Discussionmentioning

confidence: 66%

Pathogenicity characterization with implicit and explicit molecular dynamics simulation

Herlambang¹,

Saleh²

2011

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“…Eleven receptor-ligand complex structures were used as starting points for the neuraminidase calculations (PDB identifiers 1BJI, 1F8B, 1F8C, 1F8D, 1F8E, 1L7F, 1MWE, 1NNC, 2QWI, 2QWJ, 2QWK). [15][16][17][18][19][20] Cocrystal structures were prepared using Pfizer in-house software that automates standard protein preparation tasks. Manual preparation or publicly available tools can also be used.…”

Section: Methodsmentioning

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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“…The acquired results were not far from the results of other studies that calculated the binding free energy of NA-inhibitor complexes with different approaches [39,40].…”

Section: Discussionmentioning

confidence: 47%