Nuclear receptors constitute a super family of protein hormones that serve as transcription factors. They typically
reside in the cytosol and, after ligand binding, migrate to the nucleus to exert their biological action. Ligands are
lipophilic, small molecules including retinoids, steroids, thyroxine, and vitamin D. Nuclear receptors being important
regulators of gene expression, constitute 13% of proteins targeted by various drugs. Thus it becomes important to identify
the ligand binding pockets on these proteins. Support Vector Machine (SVM) classifier was built to identify nuclear receptor
ligand binding pockets. Positive dataset consisted of the ligand binding pockets of known nuclear receptor-ligand
complex structures. Negative dataset consisted of ligand binding pockets of proteins other than nuclear receptors and nonligand
binding pockets of nuclear receptors. SVM model yielded a 10 fold cross-validation accuracy of 96% using linear
kernel. Also, it is helpful to find out the class of nuclear receptor in order to design a “class-specific” drug. In case of the
multiclass nuclear receptor dataset comprising of nuclear receptors belonging to three different classes, SVM model for
classification yielded an average 10-fold cross validation accuracy of 92 % for this dataset. SVM algorithm identifies and
classifies nuclear receptor binding pockets with excellent accuracy. Top ranked features indicate the hydrophobic nature
of ligand binding pocket of nuclear receptors. Conserved Leucine and phenylalanine residues form a distinguishing feature
of these binding pockets. Along-with identification of NR binding pockets, important top ranked features are listed
which would be useful in screening of possible drug molecules with NRs as molecular targets.
fpocket, ligand binding pockets, nuclear hormone receptors, nucleRDB, shell-wise features, support vector machines.
Informatics Centre, Shiv Nadar University, Uttar Pradesh-201314, India.