Inhibition and substrate recognition – a computational approach applied to HIV protease

Vinkers, H. Maarten; Jonge, Marc R. de; Daeyaert, Frits; Heeres, Jan; Koymans, Luc; Lenthe, J. H. Van; Lewi, Paul; Timmerman, Henk; Janßen, Paul

doi:10.1023/b:jcam.0000005748.19093.e8

Cited by 10 publications

(12 citation statements)

References 69 publications

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“…Thus we will repeat the calculations for a welldocumented reference protein to allow comparison with published docking work. To be able to make progress on the possible new target system of the tuberculosis bacterium [129], isocitrate lyase, we will use computational drug design methods [131,132] to design actual drugs using the molecular potential calculated for an empty enzyme. Given that supercomputers remain only a few years ahead of the mainstream, commodity marketplace, the techniques explored may soon find acceptance within the pharmaceutical industry.…”

Section: Dft Calculations On Biomolecules: Exploring the Charge Distrmentioning

confidence: 99%

The GAMESS-UK electronic structure package: algorithms, developments and applications

Guest¹,

Bush²,

Dam³

et al. 2005

Molecular Physics

510

401

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Section: Dft Calculations On Biomolecules: Exploring the Charge Distrmentioning

confidence: 99%

The GAMESS-UK electronic structure package: algorithms, developments and applications

Guest¹,

Bush²,

Dam³

et al. 2005

Molecular Physics

510

401

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show abstract

“…A variety of computational methods are available to calculate binding free energies, including interaction energies, [8–12] docking scoring functions,[13] linear interaction energies (LIE),[14–16] free energy perturbations,[17] thermodynamic integration,[18] and hybrids of these methods. [19, 20] However, the predominant method used for the calculation of ligand binding energies for HIV protease has been the molecular mechanics‐Poisson‐Boltzmann surface area (MM‐PBSA)[21–28] and MM‐generalized‐Born surface area (MM‐GBSA) methods,[26, 29, 30] which combine MM energy and implicit solvation models.…”

Section: Introductionmentioning

confidence: 99%

Effect of atomic charge, solvation, entropy, and ligand protonation state on MM‐PB(GB)SA binding energies of HIV protease

2012

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The molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) and MM-generalized-Born surface area (MM-GBSA) approaches are commonly used in molecular modeling and drug design. Four critical aspects of these approaches have been investigated for their effect on calculated binding energies: (1) the atomic partial charge method used to parameterize the ligand force field, (2) the method used to calculate the solvation free energy, (3) inclusion of entropy estimates, and (4) the protonation state of the ligand. HIV protease has been used as a test case with six structurally different inhibitors covering a broad range of binding strength to assess the effect of these four parameters. Atomic charge methods are demonstrated to effect both the molecular dynamics (MD) simulation and MM-PB(GB)SA binding energy calculation, with a greater effect on the MD simulation. Coefficients of determination and Spearman rank coefficients were used to quantify the performance of the MM-PB(GB)SA methods relative to the experimental data. In general, better performance was achieved using (i) atomic charge models that produced smaller mean absolute atomic charges (Gasteiger, HF/STO-3G and B3LYP/cc-pVTZ), (ii) the MM-GBSA approach over MM-PBSA, while (iii) inclusion of entropy had a slightly positive effect on correlations with experiment. Accurate representation of the ligand protonation state was found to be important. It is demonstrated that these approaches can distinguish ligands according to binding strength, underlining the usefulness of these approaches in computer-aided drug design.

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“…Such a structure was constructed by Vinkers et al, using averaged 3D coordinates from a set of crystallographic data. 9 We describe an approach based on binding energy profiles below.…”

Section: Introductionmentioning

confidence: 99%

Analysis of HIV Wild-Type and Mutant Structures via in Silico Docking against Diverse Ligand Libraries

Chang

Lindstrom

Olson

et al. 2007

J. Chem. Inf. Model.

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The FightAIDS@Home distributed computing project uses AutoDock for an initial virtual screen of HIV protease structures against a broad range of 1771 ligands including both known protease inhibitors and a diverse library of other ligands. The volume of results allows novel large-scale analyses of binding energy "profiles" for HIV structures. Beyond identifying potential lead compounds, these characterizations provide methods for choosing representative wild-type and mutant protein structures from the larger set. From the binding energy profiles of the PDB structures, a principal component analysis based analysis identifies seven "spanning" proteases. A complementary analysis finds that the wild-type protease structure 2BPZ best captures the central tendency of the protease set. Using a comparison of known protease inhibitors against the diverse ligand set yields an AutoDock binding energy "significance" threshold of -7.0 kcal/mol between significant, strongly binding ligands and other weak/nonspecific binding energies. This threshold captures nearly 98% of known inhibitor interactions while rejecting more than 95% of suspected noninhibitor interactions. These methods should be of general use in virtual screening projects and will be used to improve further FightAIDS@Home experiments.

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Inhibition and substrate recognition – a computational approach applied to HIV protease

Cited by 10 publications

References 69 publications

The GAMESS-UK electronic structure package: algorithms, developments and applications

The GAMESS-UK electronic structure package: algorithms, developments and applications

Effect of atomic charge, solvation, entropy, and ligand protonation state on MM‐PB(GB)SA binding energies of HIV protease

Analysis of HIV Wild-Type and Mutant Structures via in Silico Docking against Diverse Ligand Libraries

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