|Title||Prediction of ligands to universally conserved binding sites of the influenza A virus nuclear export protein|
The nuclear export protein (NEP) of the influenza A virus exports viral ribonucleoproteins to the host cell cytoplasm following nuclear transcription. In this work conservation analysis of 3000 protein sequences and molecular modelling of full-length NEP identified ligand binding sites overlapping with high sequence conservation. Two binding hot spots were identified close to the first nuclear export signal and several hot spots overlapped with highly conserved amino acids such as Arg42, Asp43, Lys39, Ile80, Gln101 and Val109. Virtual screening with ~43,000 compounds against a binding site showed affinities of up to −8.95 kcal/mol, while ~1700 approved drugs showed affinities of up to −8.31 kcal/mol. A drug-like compounds predicted was ZINC01564229 that could be used as probe to investigate NEP function or as a new drug lead. The approved drugs Nandrolone phenylpropionate and Estropipate were predicted to bind with high affinity and may be investigated for repurposing as anti-influenza drugs. Importance: The influenza A virus causes respiratory illness in humans and farm animals annually across the world. Antigenic shifts and drifts in the surface proteins lead to genome diversity and unpredictable pandemics and epidemics. The high evolution rate of the RNA genome can also limit the effectiveness of antivirals and is the cause of emerging resistance. From a human health perspective, it is important that compounds identified as potential influenza replication inhibitors remain effective long-term. This work presents results which are based on computational predictions that reveal interactions between available compounds and regions of the influenza A nuclear export protein which display high conservation. Due to a low probability of highly conserved regions undergoing genomic changes, these compounds may serve as ideal leads for new antivirals.
|Keywords||influenza, antivirals, sequence conservation, virtual screening|
|Journal citation||537, pp. 97-103|
|Digital Object Identifier (DOI)||https://doi.org/10.1016/j.virol.2019.08.013|
|Published online||14 Aug 2019|
|Published in print||Nov 2019|