Development of a structural model for NF-kappaB inhibition of sesquiterpene lactones using self-organizing neural networks.
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.
Institut für Pharmazeutische Wissenschaften, Lehrstuhl für Pharmazeutische Biologie und Biotechnologie, Universität Freiburg, 79104 Freiburg, Germany.
Steffen
S
Wagner
Angelika
A
Hofmann
Bettina
B
Siedle
Lothar
L
Terfloth
Irmgard
I
Merfort
Johann
J
Gasteiger
D007783:Lactones / Q000737:chemistry*; D008958:Models, Molecular; D016328:NF-kappa B / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012717:Sesquiterpenes / Q000737:chemistry*
D007783:Lactones; D016328:NF-kappa B; D012717:Sesquiterpenes