<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Rakesh S.</style></author><author><style face="normal" font="default" size="100%">Jagdale, Shounak S.</style></author><author><style face="normal" font="default" size="100%">Bansode, Sneha B.</style></author><author><style face="normal" font="default" size="100%">Shankar, S. Shiva</style></author><author><style face="normal" font="default" size="100%">Tellis, Meenakshi B.</style></author><author><style face="normal" font="default" size="100%">Pandya, Vaibhav Kumar</style></author><author><style face="normal" font="default" size="100%">Chugh, Anita</style></author><author><style face="normal" font="default" size="100%">Giri, Ashok P.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Mahesh J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovery of potential multi-target-directed ligands by targeting host-specific SARS-CoV-2 structurally conserved main protease</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Biomolecular Structure &amp; Dynamics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Coronavirus</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">hACE-2</style></keyword><keyword><style  face="normal" font="default" size="100%">MPro</style></keyword><keyword><style  face="normal" font="default" size="100%">multi-target-directed ligand</style></keyword><keyword><style  face="normal" font="default" size="100%">protease inhibitor</style></keyword><keyword><style  face="normal" font="default" size="100%">RdRp</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2 virus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the current COVID-19 pandemic. Worldwide this disease has infected over 2.5 million individuals with a mortality rate ranging from 5 to 10%. There are several efforts going on in the drug discovery to control the SARS-CoV-2 viral infection. The main protease (M-Pro) plays a critical role in viral replication and maturation, thus can serve as the primary drug target. To understand the structural evolution of M-Pro, we have performed phylogenetic and Sequence Similarity Network analysis, that depicted divergence of Coronaviridae M-Pro in five clusters specific to viral hosts. This clustering was corroborated with the comparison of M-Pro structures. Furthermore, it has been observed that backbone and binding site conformations are conserved despite variation in some of the residues. These attributes can be exploited to repurpose available viral protease inhibitors against SARS-CoV-2 M-Pro. In agreement with this, we performed screening of similar to 7100 molecules including active ingredients present in the Ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 M-Pro as the primary target. We identified several natural molecules like delta-viniferin, myricitrin, taiwanhomoflavone A, lactucopicrin 15-oxalate, nympholide A, afzelin, biorobin, hesperidin and phyllaemblicin B that strongly binds to SARS-CoV-2 M-Pro. Intrestingly, these molecules also showed strong binding with other potential targets of SARS-CoV-2 infection like viral receptor human angiotensin-converting enzyme 2 (hACE-2) and RNA dependent RNA polymerase (RdRp). We anticipate that our approach for identification of multi-target-directed ligand will provide new avenues for drug discovery against SARS-CoV-2 infection. Communicated by Ramaswamy H. Sarma&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article; Early Access 2020</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;3.549&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shankar, S. Shiva</style></author><author><style face="normal" font="default" size="100%">Banarjee, Reema</style></author><author><style face="normal" font="default" size="100%">Jathar, Swaraj M.</style></author><author><style face="normal" font="default" size="100%">Rajesh, S.</style></author><author><style face="normal" font="default" size="100%">Ramasamy, Sureshkumar</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Mahesh J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Biomolecular Structure &amp; Dynamics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">domains</style></keyword><keyword><style  face="normal" font="default" size="100%">Meteorin</style></keyword><keyword><style  face="normal" font="default" size="100%">meteorin-like</style></keyword><keyword><style  face="normal" font="default" size="100%">missense variants</style></keyword><keyword><style  face="normal" font="default" size="100%">protein-protein interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">structure prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">4522-4536</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.&lt;/p&gt;
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	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;4.4&lt;/p&gt;
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