Design and selection of peptides to block the SARS-CoV-2 receptor binding domain by molecular docking

Synergistic peptide combinations designed to suppress SARS-CoV-2

The SARS-CoV-2, responsible for the COVID-19 pandemic, poses a significant threat to global healthcare. Peptide and peptide-based inhibitors, known for their safety, efficacy, and selectivity, have recently emerged as promising candidates for treating late-developing viral infections. In this study, three peptides were selected to target different stages of viral invasion, specifically ACE2 and S protein binding, as well as membrane fusion. The objective was to assess their ability to impede the entry of the SARS-CoV-2 Spike pseudotyped virus. Our findings revealed that a combination of these three peptides demonstrated enhanced antiviral effects. This outcome substantiates the feasibility of developing effective peptide combinations to combat diseases related to SARS-CoV-2. Moreover, the three-peptide combinations, designed to target multiple aspects of SARS-CoV-2 viral entry, exhibited heightened viral inhibition and broad-spectrum antiviral properties.

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Prospecting native and analogous peptides with anti-SARS-CoV-2 potential derived from the trypsin inhibitor purified from tamarind seeds

The study aimed to prospect in silico native and analogous peptides with anti-SARS-CoV-2 potential derived from the trypsin inhibitor purified from tamarind seeds (TTIp). From the most stable theoretical model of TTIp (TTIp 56/287), in silico cleavage was performed for the theoretical identification of native peptides and generation of analogous peptides. The anti-SARS-CoV-2 potential was investigated through molecular dynamics (MD) simulation between the peptides and binding sites of transmembrane serine protease 2 (TMPRSS2), responsible for the entry of SARS-CoV-2 into the host cell. Five native and analogous peptides were obtained and validated through chemical and physical parameters. The best interaction potential energy (IPE) occurred between TMPRSS2 and one of the native peptides obtained by cleavage with trypsin and its analogous peptide. Thus, both peptides showed many hydrophobic residues, a common physical-chemical property among the peptides that inhibit the entry of enveloped viruses, such as SARS-CoV-2, present in specific drugs to treat COVID-19.