Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the past decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of adaptive (smart) therapeutics. Here, a computational strategy is developed to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2). Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), the templates are gradually modified by random mutations, while retaining those mutations that maximize their RBD-binding free energies. In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions. The computational search will provide libraries of optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale.

Dendrimer-Peptide Conjugates for Effective Blockade of the Interactions between SARS-CoV-2 Spike Protein and Human ACE2 Receptor.

The coronavirus disease 2019 (COVID-19) pandemic has threatened the stability of global healthcare, which is becoming an endemic issue. Despite the development of various treatment strategies to fight COVID-19, the currently available treatment options have shown varied efficacy. Herein, we have developed an avidity-based SARS-CoV-2 antagonist using dendrimer-peptide conjugates (DPCs) for effective COVID-19 treatment. Two different peptide fragments obtained from angiotensin-converting enzyme 2 (ACE2) were integrated into a single sequence, followed by the conjugation to poly(amidoamine) (PAMAM) dendrimers. We hypothesized that the strong multivalent binding avidity endowed by dendrimers would help peptides effectively block the interaction between SARS-CoV-2 and ACE2, and this antagonist effect would be dependent upon the generation (size) of the dendrimers. To assess this, binding kinetics of the DPCs prepared from generation 4 (G4) and G7 PAMAM dendrimers to spike protein of SARS-CoV-2 were quantitatively measured using surface plasmon resonance. The larger dendrimer-based DPCs exhibited significantly enhanced binding strength by 3 orders of magnitude compared to the free peptides, whereas the smaller one showed a 12.8-fold increase only. An in vitro assay using SARS-CoV-2-mimicking microbeads also showed the improved SARS-CoV-2 blockade efficiency of the G7-peptide conjugates compared to G4. In addition, the interaction between the DPCs and SARS-CoV-2 was analyzed using molecular dynamics (MD) simulation, providing an insight into how the dendrimer-mediated multivalent binding effect can enhance the SARS-CoV-2 blockade. Our findings demonstrate that the DPCs having strong binding to SARS-CoV-2 effectively block the interaction between ACE2 and SARS-CoV-2, providing a potential as a high-affinity drug delivery system to direct anti-COVID payloads to the virus.

Retrained Generic Antibodies Can Recognize SARS-CoV-2.

The dramatic impact novel viruses can have on humans could be more quickly mitigated if generic antibodies already present in one's system are temporarily retrained to recognize these viruses. This type of intervention can be administered during the early stages of infection, while a specific immune response is being developed. With this idea in mind, double-faced peptide-based boosters were computationally designed to allow recognition of SARS-CoV-2 by Hepatitis B antibodies. One booster face is made of ACE2-mimic peptides that can bind to the receptor binding domain (RBD) of SARS-CoV-2. The other booster face is composed of a Hepatitis B core-antigen, targeting the Hepatitis B antibody fragment. Molecular dynamics simulations revealed that the designed boosters have a highly specific and stable binding to both the RBD and the antibody fragment (AF). This approach can provide a cheap and efficient neutralization of emerging pathogens.

Sulfoglycodendrimer Therapeutics for HIV-1 and SARS-CoV-2.

Hexavalent sulfoglycodendrimers (SGDs) are synthesized as mimics of host cell heparan sulfate proteoglycans (HSPGs) to inhibit the early stages in viral binding/entry of HIV-1 and SARS-CoV-2. Using an HIV neutralization assay, the most promising of the seven candidates are found to have sub-micromolar anti-HIV activities. Molecular dynamics simulations are separately implemented to investigate how/where the SGDs interacted with both pathogens. The simulations revealed that the SGDs: 1) develop multivalent binding with polybasic regions within and outside of the V3 loop on glycoprotein 120 (gp120) for HIV-1, and consecutively bind with multiple gp120 subunits, and 2) interact with basic amino acids in both the angiotensin-converting enzyme 2 (ACE2) and HSPG binding regions of the Receptor Binding Domain (RBD) from SARS-CoV-2. These results illustrate the considerable potential of SGDs as inhibitors in viral binding/entry of both HIV-1 and SARS-CoV-2 pathogens, leading the way for further development of this class of molecules as broad-spectrum antiviral agents.

Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the last decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of
adaptive (smart) therapeutics. Here, we develop a computational strategy to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2). Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), we gradually modify the templates by random mutations, while retaining those mutations that maximize their RBD-binding free energies. In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions. The computational search will provide libraries
of optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale.
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Computational Design of ACE2-Based Peptide Inhibitors of SARS-CoV-2.

Peptide inhibitors against the SARS-CoV-2 coronavirus, currently causing a worldwide pandemic, are designed and simulated. The inhibitors are mostly formed by two sequential self-supporting α-helices (bundle) extracted from the protease domain (PD) of angiotensin-converting enzyme 2 (ACE2), which bind to the SARS-CoV-2 receptor binding domains. Molecular dynamics simulations revealed that the α-helical peptides maintain their secondary structure and provide a highly specific and stable binding (blocking) to SARS-CoV-2. To provide a multivalent binding to the SARS-CoV-2 receptors, many such peptides could be attached to the surfaces of nanoparticle carriers. The proposed peptide inhibitors could provide simple and efficient therapeutics against the COVID-19 disease.