Prediction of adverse effects of sulforaphane by in silico testing of targeted genes, protein- protein interactions and molecular classes

Abstract

Alternative form of cancer treatment includes targeting natural compounds such as sulfur-rich dietary phytochemical sulforaphane (SFN). However, data on SFN safety, interactions on the protein level and target of SFN in human organism are limited. The aim of this study was to elucidate the target interactions of SFN in human body in order to rationalize possible side-effects and predict off-targets by using in silico approach. STITCH database (http://stitch.embl.de) was used to obtain the information about chemical–protein interactions, while Metascape (https://metascape.org/) highlighted protein-protein interaction enrichment (PPIE). SwissTargetPrediction (http://www.swisstargetprediction.ch/) indicated the target molecule classes of SFN in human. Human genes that had the strongest interaction with SFN were NQO1, NFE2L2, CASP3, HSP90AA1, MAPK14, HDAC6, HPGDS, KEAP1, GSTA1 and GSTM1. PPIE analysis singled out fluid shear stress and atherosclerosis, NRF2 pathway and chemical carcinogenesis - reactive oxygen species (ROS) as the most significant interactions. The most represented class of SFN targeted molecules in human organism were enzymes (26.7%). Epidermal growth factor receptor erbB1, macrophage migration inhibitory factor, nitric oxide synthase (inducible) showed the highest probability target rate. In our previous study, we pointed out that the genome of cancer patients could affect SFN safety. The current study provides a set of target genes, emphasizes the importance of oxidative stress in the suggested genetic interactions and predicts classes of target molecules, which should further be examined.