Revealing the macromolecular targets of complex natural products

Abstract

Natural products have long been a source of useful biological activity for the development of new drugs. Their macromolecular targets are, however, largely unknown, which hampers rational drug design and optimization. Here we present the development and experimental validation of a computational method for the discovery of such targets. The technique does not require three-dimensional target models and may be applied to structurally complex natural products. The algorithm dissects the natural products into fragments and infers potential pharmacological targets by comparing the fragments to synthetic reference drugs with known targets. We demonstrate that this approach results in confident predictions. In a prospective validation, we show that fragments of the potent antitumour agent archazolid A, a macrolide from the myxobacterium Archangium gephyra, contain relevant information regarding its polypharmacology. Biochemical and biophysical evaluation confirmed the predictions. The results obtained corroborate the practical applicability of the computational approach to natural product 'de-orphaning'.

Discovery and development of natural product oridonin-inspired anticancer agents

“…It is known that the polypharmacology and network pharmacological approach have been widely recognized in drug discovery to enhance efficacy and overcome drug resistance of singular-target drugs [98–100]. Such approaches have been successfully applied in the field of natural product research based upon the systematic elucidation and prediction of mechanisms, as well as functional phenotypic measurements in vivo and in vitro [101, 102]. These relevant advances inspire researchers to identify new and unexplored targets of oridonin and its derivatives (e.g.…”

Active Learning for Computational Chemogenomics

“…For example, environmental agencies may consider applying computational chemogenomics as a way to generate hypotheses about the effect of pollutants generated during manufacturing processes [89]. In another application, deorphanization of natural products used in cancer therapy [90,91] can provide the starting CPIs to initiate an actively learned chemogenomic model which generates testable hypotheses of new drug-target interactions in uncharacterized drugs for specific cancer cell lines, such that the results of tested hypotheses are fed back into the model for subsequent hypothesis generation.…”

Hide and seek: Identification and confirmation of small molecule protein targets

“…For example, Reker et al 58 inferred eight novel binding partners for the antiproliferative agent archazolid A using an algorithm that can predict biological targets of any compound of interest by comparing its fragments with the structure of reference drugs which are characterized by known targets. Half of these proteins were dose-dependently affected in assays performed in vitro, but target engagement experiments in living cells are ultimately required to confirm the interaction of archazolid A with the predicted targets.…”