Search bioRxiv⌕ Search

Biology subjects

Nizami, B.

Publications and source records attributed to Nizami, B..

2 recordsLinked to original sources

In Silico Identification Of Novel PRFA Inhibitors To Fight Listeriosis: A Virtual Screening And Molecular Dynamics Studies

Listeria monocytogenes is considered to be one of the most dangerous foodborne pathogens as it can cause listeriosis, a life-threatening human disease. While the incidence of listeriosis is very low its fatality rate is exceptionally high. Because many multi-resistance Listeria monocytogenes strains that do not respond to conventional antibiotic therapy have been recently described, development of new antimicrobials to fight listeriosis is necessary. The positive regulatory factor A (PrfA) is a key homodimeric transcription factor that modulates the transcription of multiple virulence factors which are ultimately responsible of Listeria monocytogenes pathogenicity. In the present manuscript we describe several new potential PrfA inhibitors that were identified after performing ligand-based virtual screening followed by structure-based virtual screening against the wild-type PrfA structure. The three top-scored drug-likeness inhibitors bound to the wild-type PrfA structure were further assessed by Molecular Dynamics simulations. Besides, the three top-scored inhibitors were docked into a constitutive active apoPrfA mutant structure and the corresponding complexes were also simulated. According to the obtained data, PUBChem 87534955 (P875) and PUBChem 58473762 (P584) may not only bind and inhibit wild-type PrfA but the aforementioned apoPrfA mutant as well. Therefore, P875 and P584 might represent good starting points for the development of a completely new set of antimicrobial agents to treat listeriosis.

bioinformatics↗

MODE-TASK: Large-scale protein motion tools

SummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.\n\nAvailability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.\n\nSupplementary informationDocumentation available at http://mode-task.readthedocs.io.

bioinformatics↗