Abstract
Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.
Highlights
Fragment-Based Drug Design (FBDD) is a well-established approach based on the screening of chemical fragments with low molecular weight, which are used to facilitate higher complementarity within a receptor binding site, more efficient exploration of the chemical space (Hall et al, 2015), and, the identification of compounds able to bind efficiently to a particular biological target (Schulz and Hubbard, 2009; Erlanson et al, 2016)
Tolerance spheres for 3D pharmacophore features such as H-bond acceptor (HBA), H-bond donor (HBD), hydrophobic group (HY), aromatic ring (AR), positive ionizable, (PI), and/or negative ionizable (NI) features were considered in the construction of the models if they included the centers of chemical moieties of well-known active compounds with the corresponding chemical nature, as described in (Wolber and Langer, 2005)
While it is tempting to speculate that this result was expected because the number of non-extensive natural product-derived fragment (NPDF) was higher than that of the other two groups of chemical entities, our results demonstrated that their fit scores have the same driving trend
Summary
Fragment-Based Drug Design (FBDD) is a well-established approach based on the screening of chemical fragments with low molecular weight (from 15 to 20 heavy atoms approximately), which are used to facilitate higher complementarity within a receptor binding site, more efficient exploration of the chemical space (Hall et al, 2015), and, the identification of compounds able to bind efficiently to a particular biological target (Schulz and Hubbard, 2009; Erlanson et al, 2016). RECAP rules theoretically handle the cleavage sites within a molecule in two different ways: a classical, extensive (i.e., exhaustive) that generates a collection of fragments as small as possible; and an alternative, non-extensive that generates all possible “intermediate” scaffolds by considering the cleavage sites in a systematic manner (Figure 1). Both types have been previously termed leaf and non-leaf nodes, respectively, by other authors (Fechner and Schneider, 2007). The latter subject is critical because the often sought-after potential of scaffold hopping for both FBDD and pharmacophore modeling largely depends on the diversity of the screening library; structurally complex compounds such as NPs are intended to increase this capability (Grabowski et al, 2008)
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