@article{open2521,
           month = {December},
           title = {Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network.},
          author = {Shivalika Pathania and Vinay Randhawa and Manoj Kumar},
       publisher = {Taylor \& Francis},
            year = {2019},
           pages = {1--18},
         journal = {Journal of biomolecular structure \& dynamics},
        keywords = {Nipah virus; attachment glycoprotein; chemical-protein interaction network; entry inhibitor; molecular docking},
             url = {http://crdd.osdd.net/open/2521/},
        abstract = {Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads - Nilotinib, Deslanoside and Acetyldigitoxin - on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.Communicated by Ramaswamy H. Sarma.}
}