Development of a Structural Model for NF-κB Inhibition of Sesquiterpene Lactones Using Self-Organizing Neural Networks

Abstract

A variety of sesquiterpene lactones (SLs) possess considerable anti-inflammatory activity. Several studies have shown that they exert this effect in part by inhibiting the activation of the transcription factor NF-kappaB. In the present study we elaborated on the investigation of a data set of 103 structurally diverse SLs for which we had previously developed several different QSAR equations dependent on the skeletal type. Use of 3D structure descriptors resulted in a single model for the entire data set. In particular, local radial distribution functions (L-RDF) were used that centered on the methylene-carbonyl substructure believed to be the site of attack of cysteine-38 of the p65/NF-kappaB subunit. The model was developed by using a counterpropagation neural network (CPGNN), attesting to the power of this method for establishing structure-activity-relationships. The investigations shed more light onto the influence of the chemical structure on NF-kappaB inhibitory activity.