Abstract
In multitarget drug design, it is critical to identify active and inactive compounds against a variety of targets and antitargets. Multitarget strategies thus test the limits of available technology, be that in screening large databases of compounds vs. a large number of targets, or in using in silico methods for understanding and reliably predicting these pharmacological outcomes. In this paper, we have evaluated the potential of several in silico approaches to predict the target, antitarget and physicochemical profile of (S)-blebbistatin, the best-known myosin II ATPase inhibitor, and a series of analogs thereof. Standard and augmented structure-based design techniques could not recover the observed activity profiles. A ligand-based method using molecular fingerprints was, however, able to select actives for myosin II inhibition. Using further ligand- and structure-based methods, we also evaluated toxicity through androgen receptor binding, affinity for an array of antitargets and the ADME profile (including assay-interfering compounds) of the series. In conclusion, in the search for (S)-blebbistatin analogs, the dissimilarity distance of molecular fingerprints to known actives and the computed antitarget and physicochemical profile of the molecules can be used for compound design for molecules with potential as tools for modulating myosin II and motility-related diseases.
Highlights
Multitarget drug design attempts to rationalize interactions with targets and antitargets
Antitarget effects were studied using a battery of antitarget proteins with physiological significance for myosin inhibitors, including the androgen receptor involved in hormonal systems and skeletal muscle differentiation (Rayment et al, 1993; Wannenes et al, 2008), the pregnane X receptor (PXR) involved in efflux of xenobiotics, sulfotransferase (SULT) and cytochrome P450 (CYP) isoforms involved in human metabolism of substances
All docked (S)-blebbistatin analogs occupying the same region of the binding pocket, had orientations highly resembling the crystallographic ligand, and were able to establish two hydrogen bonds between their OH moiety and Leu 262 and Gly 240 in the myosin II binding pocket
Summary
Multitarget drug design attempts to rationalize interactions with targets and antitargets. Computational methods are central to the ability to predict interactions between compounds and targets given their ability to use a large amount of data on both. They help to prioritize compounds for development or help in target profiling. The main reason for this failure is the empirical observation that selectivity and affinity of myosin inhibitors cannot be rationalized from analysis of the residues lining the binding pocket (Sirigu et al, 2016; Verhasselt et al, 2017b,c) Other factors, such as the kinetics of the chemomechanical cycle must play an important role in myosin ligand discrimination. Antitarget effects were studied using a battery of antitarget proteins with physiological significance for myosin inhibitors, including the androgen receptor involved in hormonal systems and skeletal muscle differentiation (Rayment et al, 1993; Wannenes et al, 2008), the pregnane X receptor (PXR) involved in efflux of xenobiotics, sulfotransferase (SULT) and cytochrome P450 (CYP) isoforms involved in human metabolism of substances
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