In-silico designing of hnRNP B1 inhibitors against lung cancer: a computational approach

Abstract

RNA-binding proteins regulate the maturtarion of mRNA including pre-mRNA splicing, mRNA transport from the nucleus to cytoplasm, the translocation and longevity of transcripts within the cytoplasm as well as translation of the message. One key RNA-binding protein identified in several aspects of this process is heterogeneous ribonuclear ribonucleoprotein B1 (hnRNP B1) and it plays a role in several cellular functions.  HnRNP B1 is overexpressed in several cancers including lung and squamous cell carcinoma of various organs, in addition, its mRNA can be detected in the serum of patients with early stage lung cancer and can therefore be used as a biomarker for cancer detection.  Furthermore, it can be used as a target for treatment. In the present study hnRNP B1 was used as a target receptor for in silico ligand binding studies using a library of 55 small compounds selected from the current knowledge of lung cancer therapeutic drugs available in the market as well as known inhibitors of hnRNP B1. TSAR studies were carried out for selected compounds from BioMed CAChe to determine docking score and then compared with 7 lactate dehydrogenase inhibitor involved in lung cancer whose IC50 value were known. Results from both software analyses indicated that EGCG was a potent candidate ligand of hnRNP B1 and was selected as a parent compound for pharmacophore modifications to improve the binding affinity and activity against hnRNP B1. A library of 158 compounds was generated after modification of EGCG and molecular modeling and docking identified several analogues with increased binding scores. In addition, pharmacophore studies indicated the significant importance of ring D in EGCG and the formation of H-bonds between the modified ligands and amino acid GLU108 of hnRNPB1. Hence the outcomes of study identify a strategy for the identification of new lead molecules bearing drug-like properties. Future studies are required to validate this approach and measure the efficacy of analogues of compounds identified using in silico approaches.