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Front Immunol ; 13: 820336, 2022.
Article in English | MEDLINE | ID: covidwho-1933641


The continuous spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world has raised unprecedented challenges to the human society. Antibodies and nanobodies possessing neutralization activity represent promising drug candidates. In this study, we report the identification and characterization of a potent SARS-CoV-2 neutralizing nanobody that targets the viral spike receptor-binding domain (S-RBD). The nanobody, termed as Nb-007, engages SARS-CoV-2 S-RBD with the two-digit picomolar binding affinity and shows outstanding virus entry-inhibition activity. The complex structure of Nb-007 bound to SARS-CoV-2 S-RBD reveals an epitope that is partially overlapping with the binding site for the human receptor of angiotensin-converting enzyme 2 (ACE2). The nanobody therefore exerts neutralization by competing with ACE2 for S-RBD binding, which is further ascertained by our in-vitro biochemical analyses. Finally, we also show that Nb-007 reserves promising, though compromised, neutralization activity against the currently-circulating Delta variant and that fusion of the nanobody with Fc dramatically increases its entry-inhibition capacity. Taken together, these data have paved the way of developing Nb-007 as a drug-reserve for potential treatment of SARS-CoV-2 related diseases.

COVID-19 , SARS-CoV-2 , Angiotensin-Converting Enzyme 2 , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/drug therapy , Humans , Receptors, Virus/metabolism , Spike Glycoprotein, Coronavirus
Nonlinear Dyn ; 101(3): 1527-1543, 2020.
Article in English | MEDLINE | ID: covidwho-706095


COVID-19 was declared as a pandemic by the World Health Organization on March 11, 2020. Here, the dynamics of this epidemic is studied by using a generalized logistic function model and extended compartmental models with and without delays. For a chosen population, it is shown as to how forecasting may be done on the spreading of the infection by using a generalized logistic function model, which can be interpreted as a basic compartmental model. In an extended compartmental model, which is a modified form of the SEIQR model, the population is divided into susceptible, exposed, infectious, quarantined, and removed (recovered or dead) compartments, and a set of delay integral equations is used to describe the system dynamics. Time-varying infection rates are allowed in the model to capture the responses to control measures taken, and distributed delay distributions are used to capture variability in individual responses to an infection. The constructed extended compartmental model is a nonlinear dynamical system with distributed delays and time-varying parameters. The critical role of data is elucidated, and it is discussed as to how the compartmental model can be used to capture responses to various measures including quarantining. Data for different parts of the world are considered, and comparisons are also made in terms of the reproductive number. The obtained results can be useful for furthering the understanding of disease dynamics as well as for planning purposes.