Identification and characterization of ErbB4 kinase inhibitors for effective breast cancer therapy.

The overexpression of ErbB4 is associated with aggressive disease biology and reduced the survival of breast cancer patients. We have used ErbB4 receptor as a novel drug target to spearhead the rational drug design. The present study is divided into two parts. In the first part, we have exploited the hidden information inside ErbB4 kinase receptor both at sequence and structural level. PSI-BLAST algorithm is used to search similar sequences against ErbB4 kinase sequence. Top 15 sequences with high identity were selected for finding conserved and variable regions among sequences using multiple sequence alignment. In the second part, available 3 D structure of ErbB4 kinase is curated using loop modeling, and anomalies in the modeled structure is improved by energy minimization. The resultant structure is validated by analyzing dihedral angles by Ramachandran plot analysis. Furthermore, the potential binding sites were detected by using DoGSite and CASTp server. The similarity-search criterion is used for the preparation of our in-house database of drugs from DrugBank database. In total, 409 drugs yet to be tested against ErbB4 kinase is used for screening purpose. Virtual screening results in identification of 11 compounds with better binding affinity than lapatinib and canertinib. Study of protein-ligand interactions reveals information about amino acid residues; Lys726, Thr771, Met774, Cys778, Arg822, Thr835, Asp836 and Phe837 at the binding pocket. The physicochemical properties and bioactivity score calculation of selected compounds suggest them as biological active. This study presents a rich array that assist in expediting new drug discovery for breast cancer.