Structure-guided identification of potent inhibitors of ROS1 kinase for therapeutic development against non-small cell lung cancer

Proto-oncogene tyrosine-protein kinase ROS (ROS1) is a member of the sevenless receptor, which affects epithelial cell differentiation and is highly expressed in a variety of tumor cells. The elevated expression and dysfunction of ROS1 have been involved in various malignancies, such as non-small cell lung cancer (NSCLC), stomach cancer, ovarian, breast cancer, cholangiocarcinoma, colorectal cancer, adenosarcoma, oesophageal cancer, etc. ROS1 has been postulated as a potential drug target in anticancer therapeutics. In this study, we carried out a virtual screening of phytochemicals against ROS1 to identify its potential inhibitors. The virtual screening process was performed on the ROS1 structure, where two phytochemicals, Helioscopinolide C and Taiwanin C, were identified. These compounds resulted from filters like Lipinski rule of five, PAINS filter, binding affinities values, and all-atom molecular dynamics (MD) simulations followed by principal component analysis (PCA) and essential dynamics. The findings of this study highlight the role of ROS1 in multiple physiological candidates and its therapeutic targeting using phytochemicals. This study suggests Helioscopinolide C and Taiwanin C as potential compounds for therapeutic development targeting ROS1-associated non-small cell lung cancer for clinical applications. Further in vitro and in vivo experiments are required to validate these findings.Communicated by Ramaswamy H. Sarma.

Single molecule transcription profiling with AFM

Established techniques for global gene expression profiling, such as microarrays, face fundamental sensitivity constraints. Due to greatly increasing interest in examining minute samples from micro-dissected tissues, including single cells, unorthodox approaches, including molecular nanotechnologies, are being explored in this application. Here, we examine the use of single molecule, ordered restriction mapping, combined with AFM, to measure gene transcription levels from very low abundance samples. We frame the problem mathematically, using coding theory, and present an analysis of the critical error sources that may serve as a guide to designing future studies. We follow with experiments detailing the construction of high density, single molecule, ordered restriction maps from plasmids and from cDNA molecules, using two different enzymes, a result not previously reported. We discuss these results in the context of our calculations.