Bioinformatics and Drug Discovery

Abstract

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

Combined effect of SAR-endolysin LysKpV475 with polymyxin B and Salmonella bacteriophage phSE-5

“…The greatest bottleneck for endolysin activity against Gram-negative bacteria is the low permeability of the outer membrane [7]. Therefore, a microdilution assay was performed to measure antibiotic interactions [40] to define the fractional inhibitory concentration index (FICI) and the checkerboard assay to quantify possible synergistic or antagonist interactions [41] between LysKpV475 and polymyxin B. The FICI was calculated for K. pneumoniae ATCC BAA-2146 and S .…”