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Characterizing loop dynamics and ligand recognition in human- and avian-type influenza neuraminidases via generalized born molecular dynamics and end-point free energy calculations.

ABSTRACT: The comparative dynamics and inhibitor binding free energies of group-1 and group-2 pathogenic influenza A subtype neuraminidase (NA) enzymes are of fundamental biological interest and relevant to structure-based drug design studies for antiviral compounds. In this work, we present seven generalized Born molecular dynamics simulations of avian (N1)- and human (N9)-type NAs in order to probe the comparative flexibility of the two subtypes, both with and without the inhibitor oseltamivir bound. The enhanced sampling obtained through the implicit solvent treatment suggests several provocative insights into the dynamics of the two subtypes, including that the group-2 enzymes may exhibit similar motion in the 430-binding site regions but different 150-loop motion. End-point free energy calculations elucidate the contributions to inhibitor binding free energies and suggest that entropic considerations cannot be neglected when comparing across the subtypes. We anticipate the findings presented here will have broad implications for the development of novel antiviral compounds against both seasonal and pandemic influenza strains.

SUBMITTER: Amaro RE

PROVIDER: S-EPMC2665887 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Characterizing loop dynamics and ligand recognition in human- and avian-type influenza neuraminidases via generalized born molecular dynamics and end-point free energy calculations.

Amaro Rommie E RE Cheng Xiaolin X Ivanov Ivaylo I Xu Dong D McCammon J Andrew JA

Journal of the American Chemical Society 20090401 13

The comparative dynamics and inhibitor binding free energies of group-1 and group-2 pathogenic influenza A subtype neuraminidase (NA) enzymes are of fundamental biological interest and relevant to structure-based drug design studies for antiviral compounds. In this work, we present seven generalized Born molecular dynamics simulations of avian (N1)- and human (N9)-type NAs in order to probe the comparative flexibility of the two subtypes, both with and without the inhibitor oseltamivir bound. Th ...[more]

PMID: 19296611

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Project description:End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal·mol(-1) highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ? of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (? = 1). An increase in ? resulted in significantly better rank-ordering for MM-PBSA (? = 0.91 for ? = 10), but larger ? significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the nonpolar and entropy components, rather than a better representation of the electrostatics. The SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (? = 0.81). Calculations of the configurational entropy using normal-mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo, and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (? = 0.89). Interestingly, filtering MD snapshots by prescoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (? = 1) from ? = 0.40 to ? = 0.81. Finally, the nonpolar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.

Dataset Information

Characterizing loop dynamics and ligand recognition in human- and avian-type influenza neuraminidases via generalized born molecular dynamics and end-point free energy calculations.

Publications

Characterizing loop dynamics and ligand recognition in human- and avian-type influenza neuraminidases via generalized born molecular dynamics and end-point free energy calculations.

Similar Datasets

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