Targeted design of drug binding sites in the main protease of SARS-CoV-2 reveals potential signatures of adaptation

Bebtelovimab-bound SARS-CoV-2 RBD mutants: resistance profiling and validation with escape mutations, clinical results, and viral genome sequences

The dynamic evolution of SARS-CoV-2 variants necessitates ongoing advancements in therapeutic strategies. Despite the promise of monoclonal antibody (mAb) therapies like bebtelovimab, concerns persist regarding resistance mutations, particularly single-to-multipoint mutations in the receptor-binding domain (RBD). Our study addresses this by employing interface-guided computational protein design to predict potential bebtelovimab-resistance mutations. Through extensive physicochemical analysis, mutational preferences, precision-recall metrics, protein-protein docking, and energetic analyses, combined with all-atom, and coarse-grained molecular dynamics (MD) simulations, we elucidated the structural-dynamics-binding features of the bebtelovimab-RBD complexes. Identification of susceptible RBD residues under positive selection pressure, coupled with validation against bebtelovimab-escape mutations, clinically reported resistance mutations, and viral genomic sequences enhances the translational significance of our findings and contributes to a better understanding of the resistance mechanisms of SARS-CoV-2.

Antimicrobial peptides: Opportunities and challenges in overcoming resistance

Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, antimicrobial peptides have emerged as promising candidates against resistant pathogens. Unlike traditional antibiotics with only one target, these peptides can use different mechanisms to destroy bacteria, with low toxicity to mammalian cells compared to many conventional antibiotics. Antimicrobial peptides (AMPs) have encouraging antibacterial properties and are currently employed in the clinical treatment of pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes the mechanisms of antimicrobial peptides against bacteria, discusses the mechanisms of drug resistance, the limitations and challenges of AMPs in peptide drug applications for combating drug-resistant bacterial infections, and strategies to enhance their capabilities.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Computational Protein Design for COVID-19 Research and Emerging Therapeutics

As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.

Phytonutrient Inhibitors of SARS-CoV-2/NSP5-Encoded Main Protease (Mpro) Autocleavage Enzyme Critical for COVID-19 Pathogenesis

The genomic reshuffling, mutagenicity, and high transmission rate of the SARS-CoV-2 pathogen highlights an urgent need for effective antiviral interventions for COVID-19 control. Targeting the highly conserved viral genes and/or gene-encoded viral proteins such as main proteinase (M^pro), RNA-dependent RNA polymerase (RdRp) and helicases are plausible antiviral approaches to prevent replication and propagation of the SARS-CoV-2 infection. Coronaviruses (CoVs) are prone to extensive mutagenesis; however, any genetic alteration to its highly conserved M^pro enzyme is often detrimental to the viral pathogen. Therefore, inhibitors that target the M^pro enzyme could reduce the risk of mutation-mediated drug resistance and provide effective antiviral protection. Several existing antiviral drugs and dietary bioactives are currently repurposed to treat COVID-19. Dietary bioactives from three ayurvedic medicinal herbs, 18 β-glycyrrhetinic acid (ΔG = 8.86 kcal/mol), Solanocapsine (ΔG = 8.59 kcal/mol), and Vasicoline (ΔG = 7.34 kcal/mol), showed high-affinity binding to M^pro enzyme than the native N3 inhibitor (ΔG = 5.41 kcal/mol). Flavonoids strongly inhibited SARS-CoV-2 M^pro with comparable or higher potency than the antiviral drug, remdesivir. Several tannin hydrolysates avidly bound to the receptor-binding domain and catalytic dyad (His₄₁ and Cys₁₄₅) of SARS-CoV-2 M^pro through H-bonding forces. Quercetin binding to M^pro altered the thermostability of the viral protein through redox-based mechanism and inhibited the viral enzymatic activity. Interaction of quercetin-derivatives with the M^pro seem to be influenced by the 7-OH group and the acetoxylation of sugar moiety on the ligand molecule. Based on pharmacokinetic and ADMET profiles, several phytonutrients could serve as a promising redox nutraceutical for COVID-19 management.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen’s protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein–drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

A computational essential dynamics approach to investigate structural influences of ligand binding on Papain like protease from SARS-CoV-2

Papain like protease (PLpro) is a cysteine protease from the coronaviridae family of viruses. Coronaviruses possess a positive sense, single-strand RNA, leading to the translation of two viral polypeptides containing viral structural, non-structural and accessory proteins. PLpro is responsible for the cleavage of nsp1–3 from the viral polypeptide. PLpro also possesses deubiquitinating and deISGlyating activity, which sequesters the virus from the host's immune system. This indispensable attribute of PLpro makes it a protein of interest as a drug target. The present study aims to analyze the structural influences of ligand binding on PLpro. First, PLpro was screened against the ZINC-in-trials library, from which four lead compounds were identified based on estimated binding affinity and interaction patterns. Next, based on molecular docking results, ZINC000000596945, ZINC000064033452 and VIR251 (control molecule) were subjected to molecular dynamics simulation. The study evaluated global and essential dynamics analyses utilising principal component analyses, dynamic cross-correlation matrix, free energy landscape and time-dependant essential dynamics to predict the structural changes observed in PLpro upon ligand binding in a simulated environment. The MM/PBSA-based binding free energy calculations of the two selected molecules, ZINC000000596945 (−41.23 ± 3.70 kcal/mol) and ZINC000064033452 (−25.10 ± 2.65 kcal/mol), displayed significant values which delineate them as potential inhibitors of PLpro from SARS-CoV-2.

Molecular interactions and inhibition of the SARS‐CoV‐2 main protease by a thiadiazolidinone derivative

We report molecular interactions and inhibition of the main protease (M^Pro) of SARS‐CoV‐2, a key enzyme involved in the viral life cycle. By using a thiadiazolidinone (TDZD) derivative as a chemical probe, we explore the conformational dynamics of M^Pro via docking protocols and molecular dynamics simulations in all‐atom detail. We reveal the local and global dynamics of M^Pro in the presence of this inhibitor and confirm the inhibition of the enzyme with an IC₅₀ value of 1.39 ± 0.22 μM, which is comparable to other known inhibitors of this enzyme.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

DFT and MD simulation investigation of favipiravir as an emerging antiviral option against viral protease (3CLpro) of SARS-CoV-2

As per date, around 20 million COVID-19 cases reported from across the globe due to a tiny 125 nm sized virus: SARS-CoV-2 which has created a pandemic and left an unforgettable impact on our world. Besides vaccine, medical community is in a race to identify an effective drug, which can fight against this disease effectively. Favipiravir (F) has recently attracted too much attention as an effective repurposed drug against COVID-19. In the present study, the pertinency of F has been tested as an antiviral option against viral protease (3CLpro) of SARS-CoV-2 with the help of density functional theory (DFT) and MD Simulation. Different electronic properties of F such as atomic charges, molecular electrostatic properties (MEP), chemical reactivity and absorption analysis have been studied by DFT. In order to understand the interaction and stability of inhibitor F against viral protease, molecular docking and MD simulation have been performed. Various output like interaction energies, number of intermolecular hydrogen bonding, binding energy etc. have established the elucidate role of F for the management of CoV-2 virus for which there is no approved therapies till now. Our findings highlighted the need to further evaluate F as a potential antiviral against SARS-CoV-2.

Drug-based therapeutic strategies for COVID-19-infected patients and their challenges

Emerging epidemic-prone diseases have introduced numerous health and economic challenges in recent years. Given current knowledge of COVID-19, herd immunity through vaccines alone is unlikely. In addition, vaccination of the global population is an ongoing challenge. Besides, the questions regarding the prevalence and the timing of immunization are still under investigation. Therefore, medical treatment remains essential in the management of COVID-19. Herein, recent advances from beginning observations of COVID-19 outbreak to an understanding of the essential factors contributing to the spread and transmission of COVID-19 and its treatment are reviewed. Furthermore, an in-depth discussion on the epidemiological aspects, clinical symptoms and most efficient medical treatment strategies to mitigate the mortality and spread rates of COVID-19 is presented.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach

A continual rise in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection causing coronavirus disease (COVID-19) has become a global threat. The main problem comes when SARS-CoV-2 gets mutated with the rising infection and becomes more lethal for humankind than ever. Mutations in the structural proteins of SARS-CoV-2, i.e., the spike surface glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N), and replication machinery enzymes, i.e., main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) creating more complexities towards pathogenesis and the available COVID-19 therapeutic strategies. This study analyzes how a minimal variation in these enzymes, especially in S protein at the genomic/proteomic level, affects pathogenesis. The structural variations are discussed in light of the failure of small molecule development in COVID-19 therapeutic strategies. We have performed in-depth sequence- and structure-based analyses of these proteins to get deeper insights into the mechanism of pathogenesis, structure-function relationships, and development of modern therapeutic approaches. Structural and functional consequences of the selected mutations on these proteins and their association with SARS-CoV-2 virulency and human health are discussed in detail in the light of our comparative genomics analysis.

Interface-based design of the favipiravir-binding site in SARS-CoV-2 RNA-dependent RNA polymerase reveals mutations conferring resistance to chain termination

Favipiravir is a broad-spectrum inhibitor of viral RNA-dependent RNA polymerase (RdRp) currently being used to manage COVID-19. Accumulation of mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RdRp may facilitate antigenic drift, generating favipiravir resistance. Focussing on the chain-termination mechanism utilized by favipiravir, we used high-throughput interface-based protein design to generate > 100 000 designs of the favipiravir-binding site of RdRp and identify mutational hotspots. We identified several single-point mutants and designs having a sequence identity of 97%-98% with wild-type RdRp, suggesting that SARS-CoV-2 can develop favipiravir resistance with few mutations. Out of 134 mutations documented in the CoV-GLUE database, 63 specific mutations were already predicted as resistant in our calculations, thus attaining ˜ 47% correlation with the sequencing data. These findings improve our understanding of the potential signatures of adaptation in SARS-CoV-2 against favipiravir.

Telaprevir is a potential drug for repurposing against SARS-CoV-2: computational and in vitro studies

Drug repurposing is an important approach to the assignment of already approved drugs for new indications. This technique bypasses some steps in the traditional drug approval system, which saves time and lives in the case of pandemics. Direct acting antivirals (DAAs) have repeatedly repurposed from treating one virus to another. In this study, 16 FDA-approved hepatitis C virus (HCV) DAA drugs were studied to explore their activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) human and viral targets. Among the 16 HCV DAA drugs, telaprevir has shown the best in silico evidence to work on both indirect human targets (cathepsin L [CTSL] and human angiotensin-converting enzyme 2 [hACE2] receptor) and direct viral targets (main protease [Mpro]). Moreover, the docked poses of telaprevir inside both hACE2 and Mpro were subjected to additional molecular dynamics simulations monitored by calculating the binding free energy using MM-GBSA. In vitro analysis of telaprevir showed inhibition of SARS-CoV-2 replication in cell culture (IC50 = 11.552 μM, CC50 = 60.865 μM, and selectivity index = 5.27). Accordingly, based on the in silico studies and supported by the presented in vitro analysis, we suggest that telaprevir may be considered for therapeutic development against SARS-CoV-2.

Accelerating COVID-19 Research Using Molecular Dynamics Simulation

The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.

Computational studies reveal Fluorine based quinolines to be potent inhibitors for proteins involved in SARS-CoV-2 assembly

World is witnessing one of the worst pandemics of this century caused by SARS-CoV-2 virus which has affected millions of individuals. Despite rapid efforts to develop vaccines and drugs for COVID-19, the disease is still not under control. Chloroquine (CQ) and Hydroxychloroquine (HCQ) are two very promising inhibitors which have shown positive effect in combating the disease in preliminary experimental studies, but their use was reduced due to severe side-effects. Here, we performed a theoretical investigation of the same by studying the binding of the molecules with SARS-COV-2 Spike protein, the complex formed by Spike and ACE2 human receptor and a human serine protease TMPRSS2 which aids in cleavage of the Spike protein to initiate the viral activation in the body. Both the molecules had shown very good docking energies in the range of -6kcal/mol. Subsequently, we did a high throughput screening for other potential quinoline candidates which could be used as inhibitors. From the large pool of ligand candidates, we shortlisted the top three ligands (binding energy -8kcal/mol). We tested the stability of the docked complexes by running Molecular Dynamics (MD) simulations where we observed the stability of the quinoline analogues with the Spike-ACE2 and TMPRSS2 nevertheless the quinolines were not stable with the Spike protein alone. Thus, although the inhibitors bond very well with the protein molecules their intrinsic binding affinity depends on the protein dynamics. Moreover, the quinolines were stable when bound to electronegative pockets of Spike-ACE2 or TMPRSS2 but not with Viral Spike protein. We also observed that a Fluoride based compound: 3-[3-(Trifluoromethyl)phenyl]quinoline helps the inhibitor to bind with both Spike-ACE2 and TMPRSS2 with equal probability. The molecular details presented in this study would be very useful for developing quinoline based drugs for COVID-19 treatment.

A Novel Therapeutic Peptide Blocks SARS-CoV-2 Spike Protein Binding with Host Cell ACE2 Receptor

Background and Objective

Coronavirus disease 2019 is a novel disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2 virus. It was first detected in December 2019 and has since been declared a pandemic causing millions of deaths worldwide. Therefore, there is an urgent need to develop effective therapeutics against coronavirus disease 2019. A critical step in the crosstalk between the virus and the host cell is the binding of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to the peptidase domain of the angiotensin-converting enzyme 2 (ACE2) receptor present on the surface of host cells.

Methods

An in silico approach was employed to design a 13-amino acid peptide inhibitor (13AApi) against the RBD of the SARS-CoV-2 spike protein. Its binding specificity for RBD was confirmed by molecular docking using pyDockWEB, ClusPro 2.0, and HDOCK web servers. The stability of 13AApi and the SARS-CoV-2 spike protein complex was determined by molecular dynamics simulation using the GROMACS program while the physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of 13AApi were determined using the ExPASy tool and pkCSM server. Finally, in vitro validation of the inhibitory activity of 13AApi against the spike protein was performed by an enzyme-linked immunosorbent assay.

Results

In silico analyses indicated that the 13AApi could bind to the RBD of the SARS-CoV-2 spike protein at the ACE2 binding site with high affinity. In vitro experiments validated the in silico findings, showing that 13AApi could significantly block the RBD of the SARS-CoV-2 spike protein.

Conclusions

Blockage of binding of the SARS-CoV-2 spike protein with ACE2 in the presence of the 13AApi may prevent virus entry into host cells. Therefore, the 13AApi can be utilized as a promising therapeutic agent to combat coronavirus disease 2019.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40268-021-00357-0.