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1.
Microbiol Spectr ; 9(2): e0135221, 2021 10 31.
Article in English | MEDLINE | ID: covidwho-1526454

ABSTRACT

The emerging new lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have marked a new phase of coronavirus disease 2019 (COVID-19). Understanding the recognition mechanisms of potent neutralizing monoclonal antibodies (NAbs) against the spike protein is pivotal for developing new vaccines and antibody drugs. Here, we isolated several monoclonal antibodies (MAbs) against the SARS-CoV-2 spike protein receptor-binding domain (S-RBD) from the B cell receptor repertoires of a SARS-CoV-2 convalescent. Among these MAbs, the antibody nCoV617 demonstrates the most potent neutralizing activity against authentic SARS-CoV-2 infection, as well as prophylactic and therapeutic efficacies against the human angiotensin-converting enzyme 2 (ACE2) transgenic mouse model in vivo. The crystal structure of S-RBD in complex with nCoV617 reveals that nCoV617 mainly binds to the back of the "ridge" of RBD and shares limited binding residues with ACE2. Under the background of the S-trimer model, it potentially binds to both "up" and "down" conformations of S-RBD. In vitro mutagenesis assays show that mutant residues found in the emerging new lineage B.1.1.7 of SARS-CoV-2 do not affect nCoV617 binding to the S-RBD. These results provide a new human-sourced neutralizing antibody against the S-RBD and assist vaccine development. IMPORTANCE COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has posed a serious threat to global health and the economy, so it is necessary to find safe and effective antibody drugs and treatments. The receptor-binding domain (RBD) in the SARS-CoV-2 spike protein is responsible for binding to the angiotensin-converting enzyme 2 (ACE2) receptor. It contains a variety of dominant neutralizing epitopes and is an important antigen for the development of new coronavirus antibodies. The significance of our research lies in the determination of new epitopes, the discovery of antibodies against RBD, and the evaluation of the antibodies' neutralizing effect. The identified antibodies here may be drug candidates for the development of clinical interventions for SARS-CoV-2.


Subject(s)
Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , COVID-19/therapy , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/immunology , Animals , Antibodies, Monoclonal/immunology , Antibodies, Monoclonal/therapeutic use , Antibodies, Neutralizing/immunology , Antibodies, Neutralizing/metabolism , Antibodies, Viral/immunology , Antibodies, Viral/metabolism , Binding Sites/immunology , COVID-19 Vaccines/immunology , Crystallography, X-Ray , Disease Models, Animal , Female , Humans , Immunization, Passive/methods , Immunoglobulin G/blood , Mice , Mice, Inbred C57BL , Mice, Transgenic , Protein Interaction Domains and Motifs/immunology , Viral Load/drug effects
2.
Comput Struct Biotechnol J ; 19: 5455-5465, 2021.
Article in English | MEDLINE | ID: covidwho-1439961

ABSTRACT

The key step for SARS-CoV-2 to infect human cells is the membrane fusion triggered by the binding of the viral extracellular Spike protein to the human extracellular receptor, the angiotensin-converting enzyme 2 (ACE2). Although the Cryo-electron microscopy (Cryo-EM) uncovered the static atomic details of ACE2 homodimers, there is still a lack of research on the kinetic and thermodynamic properties of these full-length structures. This information is helpful to understand and interpret the role of ACE2 in the cell entry of SARS-CoV-2. In order to obtain this information, we performed microsecond-scale conventional and accelerated molecular dynamics (MD) simulations of full-length all-atomic systems of the RBD-ACE2 complex, the normal and torsional conformations of the apo-ACE2 homodimer. The comparative analysis of these systems showed that there were differences in their allosteric signal pathways and motion trends. These results may be helpful to further explore the cell entry mechanism of SARS-CoV-2. Moreover, the binding free energy and hydrogen bond distribution analysis of RBD-ACE2 binding interface provided the binding motifs that may be critical to allosteric signal transmission and RBD binding. These multi-conformational binding motifs can be used as targets or templates for the inhibitor design of the cell entry of SARS-CoV-2.

3.
Brief Bioinform ; 22(3)2021 05 20.
Article in English | MEDLINE | ID: covidwho-704435

ABSTRACT

Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.


Subject(s)
Algorithms , Artificial Intelligence , Drug Design , Proteins/chemistry , Software , Databases, Protein , Quantitative Structure-Activity Relationship
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