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Targeting SARS‐CoV‐2 RBD Interface: a Supervised Computational Data‐Driven Approach to Identify Potential Modulators
Author(s) -
Gulotta Maria Rita,
Lombino Jessica,
Perricone Ugo,
De Simone Giada,
Mekni Nedra,
De Rosa Maria,
Diana Patrizia,
Padova Alessandro
Publication year - 2020
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.202000259
Subject(s) - covid-19 , interface (matter) , computational biology , computer science , sars virus , biology , medicine , virology , infectious disease (medical specialty) , bubble , maximum bubble pressure method , parallel computing , outbreak , disease , pathology
Coronavirus disease 2019 (COVID‐19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS‐CoV‐2 Spike (S) glycoprotein to host angiotensin‐converting enzyme 2 (ACE2). The interaction is mediated by the receptor‐binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS‐CoV‐2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi‐molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.

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