35 research outputs found

    Potent inhibitors of the Plasmodium falciparum enzymes plasmepsin I and II devoid of cathepsin D inhibitory activity.

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    The hemoglobin-degrading aspartic proteases plasmepsin I (Plm I) and plasmepsin II (Plm II) of the malaria parasite Plasmodium falciparum have lately emerged as putative drug targets. A series of C(2)-symmetric compounds encompassing the 1,2-dihydroxyethylene scaffold and a variety of elongated P1/P1' side chains were synthesized via microwave-assisted palladium-catalyzed coupling reactions. Binding affinity calculations with the linear interaction energy method and molecular dynamics simulations reproduced the experimental binding data obtained in a Plm II assay with very good accuracy. Bioactive conformations of the elongated P1/P1' chains were predicted and agreed essentially with a recent X-ray structure. The compounds exhibited picomolar to nanomolar inhibition constants for the plasmepsins and no measurable affinity to the human enzyme cathepsin D. Some of the compounds also demonstrated significant inhibition of parasite growth in cell culture. To the best of our knowledge, these plasmepsin inhibitors represent the most selective reported to date and constitute promising lead compounds for further optimization

    Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? An evaluation of the linear interaction energy (LIE) method

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    An extensive evaluation of the linear interaction energy (LIE) method for the prediction of binding affinity of docked compounds has been performed, with an emphasis on its applicability in lead optimization. An automated setup is presented, which allows for the use of the method in an industrial setting. Calculations are performed for four realistic examples, retinoic acid receptor γ, matrix metalloprotease 3, estrogen receptor α, and dihydrofolate reductase, focusing on different aspects of the procedure. The obtained LIE models are evaluated in terms of the root-mean-square (RMS) errors from experimental binding free energies and the ability to rank compounds appropriately. The results are compared to the best empirical scoring function, selected from a set of 10 scoring functions. In all cases, good LIE models can be obtained in terms of free-energy RMS errors, although reasonable ranking of the ligands of dihydrofolate reductase proves difficult for both the LIE method and scoring functions. For the other proteins, the LIE model results in better predictions than the best performing scoring function. These results indicate that the LIE approach, as a tool to evaluate docking results, can be a valuable asset in computational lead optimization programs. © 2006 American Chemical Society
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