Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2

A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

Neutralizing antibodies to block viral entry and for identification of entry inhibitors

Neutralizing antibodies (NAbs) are naturally produced by our immune system to combat viral infections. Clinically, neutralizing antibodies with potent efficacy and high specificity have been extensively used to prevent and treat a wide variety of viral infections, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Human Immunodeficiency Virus (HIV), Dengue Virus (DENV) and Hepatitis B Virus (HBV). An overwhelmingly large subset of clinically effective NAbs operates by targeting viral envelope proteins to inhibit viral entry into the host cell. Binding of viral envelope protein to the host receptor is a critical rate limiting step triggering a cascade of downstream events, including endocytosis, membrane fusion and pore formation to allow viral entry. In recent years, improved structural knowledge on these processes have allowed researchers to also leverage NAbs as an indispensable tool in guiding discovery of novel antiviral entry inhibitors, providing drug candidates with high efficacy and pan-genus specificity. This review will summarize the latest progresses on the applications of NAbs as effective entry inhibitors and as important tools to develop antiviral therapeutics by high-throughput drug screenings, rational design of peptidic entry inhibitor mimicking NAbs and in silico computational modeling approaches.

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".

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

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.

Advances in developing ACE2 derivatives against SARS-CoV-2

Extensive immune evasion of SARS-CoV-2 rendered therapeutic antibodies ineffective in the COVID-19 pandemic. Propagating SARS-CoV-2 variants are characterised by immune evasion capacity through key amino acid mutations, but can still bind human angiotensin-converting enzyme 2 (ACE2) through the spike protein and are, thus, sensitive to ACE2-mimicking decoys as inhibitors. In this Review, we examine advances in the development of ACE2 derivatives from the past 3 years, including the recombinant ACE2 proteins, ACE2-loaded extracellular vesicles, ACE2-mimicking antibodies, and peptide or mini-protein mimetics of ACE2. Several ACE2 derivatives are granted potent neutralisation efficacy against SARS-CoV-2 variants that rival or surpass endogenous antibodies by various auxiliary techniques such as chemical modification and practical recombinant design. The derivatives also represent enhanced production efficiency and improved bioavailability. In addition to these derivatives of ACE2, new effective therapeutics against SARS-CoV-2 variants are expected to be developed.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD)

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures

MOTIVATION

Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem.

RESULTS

Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery.

AVAILABILITY AND IMPLEMENTATION

https://github.com/xxiexuezhi/helix_gan.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of All-d-Peptide Inhibitors of SARS-CoV-2 3C-like Protease

During the replication process of SARS-CoV-2, the main protease of the virus [3-chymotrypsin-like protease (3CL^pro)] plays a pivotal role and is essential for the life cycle of the pathogen. Numerous studies have been conducted so far, which have confirmed 3CL^pro as an attractive drug target to combat COVID-19. We describe a novel and efficient next-generation sequencing (NGS) supported phage display selection strategy for the identification of a set of SARS-CoV-2 3CL^pro targeting peptide ligands that inhibit the 3CL protease, in a competitive or noncompetitive mode, in the low μM range. From the most efficient l-peptides obtained from the phage display, we designed all-d-peptides based on the retro-inverso (ri) principle. They had IC₅₀ values also in the low μM range and in combination, even in the sub-micromolar range. Additionally, the combination with Rutinprivir decreases 10-fold the IC₅₀ value of the competitive inhibitor. The inhibition modes of these d-ri peptides were the same as their respective l-peptide versions. Our results demonstrate that retro-inverso obtained all-d-peptides interact with high affinity and inhibit the SARS-CoV-2 3CL protease, thus reinforcing their potential for further development toward therapeutic agents. The here described d-ri peptides address limitations associated with current l-peptide inhibitors and are promising lead compounds. Further optimization regarding pharmacokinetic properties will allow the development of even more potent d-peptides to be used for the prevention and treatment of COVID-19.

The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines.

Open Modification Searching of SARS-CoV-2-Human Protein Interaction Data Reveals Novel Viral Modification Sites

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

Beyond the vaccines: a glance at the small molecule and peptide-based anti-COVID19 arsenal

Unprecedented efforts of the researchers have been witnessed in the recent past towards the development of vaccine platforms for the control of the COVID-19 pandemic. Albeit, vaccination stands as a practical strategy to prevent SARS-CoV-2 infection, supplementing the anti-COVID19 arsenal with therapeutic options such as small molecules/peptides and antibodies is being conceived as a prudent strategy to tackle the emerging SARS-CoV-2 variants. Noteworthy to mention that collective efforts from numerous teams have led to the generation of a voluminous library composed of chemically and mechanistically diverse small molecules as anti-COVID19 scaffolds. This review article presents an overview of medicinal chemistry campaigns and drug repurposing programs that culminated in the identification of a plethora of small molecule-based anti-COVID19 drugs mediating their antiviral effects through inhibition of proteases, S protein, RdRp, ACE2, TMPRSS2, cathepsin and other targets. In light of the evidence ascertaining the potential of small molecule drugs to approach conserved proteins required for the viral replication of all coronaviruses, accelerated FDA approvals are anticipated for small molecules for the treatment of COVID19 shortly. Though the recent attempts invested in this direction in pursuit of enrichment of the anti-COVID-19 armoury (chemical tools) are praiseworthy, some strategies need to be implemented to extract conclusive benefits of the recently reported small molecule viz. (i) detailed preclinical investigation of the generated anti-COVID19 scaffolds (ii) in-vitro profiling of the inhibitors against the emerging SARS-CoV-2 variants (iii) development of assays enabling rapid screening of the libraries of anti-COVID19 scaffold (iv) leveraging the applications of machine learning based predictive models to expedite the anti-COVID19 drug discovery campaign (v) design of antibody-drug conjugates.

Identification of antiviral peptide inhibitors for receptor binding domain of SARS-CoV-2 omicron and its sub-variants: an in-silico approach

Omicron, a variant of concern (VOC) of SARS-CoV-2, emerged in South Africa in November 2021. Omicron has been continuously acquiring a series of new mutations, especially in the spike (S) protein that led to high infectivity and transmissibility. Peptides targeting the receptor-binding domain (RBD) of the spike protein by which omicron and its variants attach to the host receptor, angiotensin-converting enzyme (ACE2) can block the viral infection at the first step. This study aims to identify antiviral peptides from the Antiviral peptide database (AVPdb) and HIV-inhibitory peptide database (HIPdb) against the RBD of omicron by using a molecular docking approach. The lead RBD binder peptides obtained through molecular docking were screened for allergenicity and physicochemical criteria (isoelectric point (pI) and net charge) required for peptide-based drugs. The binding affinity of the best five peptide inhibitors with the RBD of omicron was validated further by molecular dynamics (MD) simulation. Our result introduces five antiviral peptides, including AVP1056, AVP1059, AVP1225, AVP1801, and HIP755, that may effectively hinder omicron-host interactions. It is worth mentioning that all the three major sub-variants of omicron, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3), exhibits conserved ACE-2 interacting residues. Hence, the screened antiviral peptides with similar affinity can also interrupt the RBD-mediated invasion of different major sub-variants of omicron. Altogether, these peptides can be considered in the peptide-based therapeutics development for omicron treatment after further experimentation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03258-4.

Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 μM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

Identification, optimization, and biological evaluation of 3-O-β-chacotriosyl ursolic acid derivatives as novel SARS-CoV-2 entry inhibitors by targeting the prefusion state of spike protein

The COVID-19 pandemic generates a global threat to public health and continuously emerging SARS-CoV-2 variants bring a great challenge to the development of both vaccines and antiviral agents. In this study, we identified UA-18 and its optimized analog UA-30 via the hit-to-lead strategy as novel SARS-CoV-2 fusion inhibitors. The lead compound UA-30 showed potent antiviral activity against infectious SARS-CoV-2 (wuhan-HU-1 variant) in Vero-E6 cells and was also effective against infection of diverse pseudotyped SARS-CoV-2 variants with mutations in the S protein including the Omicron and Delta variants. More importantly, UA-30 might target the cavity between S1 and S2 subunits to stabilize the prefusion state of the SARS-CoV-2 S protein, thus leading to interfering with virus-cell membrane fusion. This study offers a set of novel SARS-CoV-2 fusion inhibitors against SARS-CoV-2 and its variants based on the 3-O-β-chacotriosyl UA skeleton.

Leveraging the therapeutic, biological, and self-assembling potential of peptides for the treatment of viral infections

Peptides and peptide-based materials have an increasing role in the treatment of viral infections through their use as active pharmaceutical ingredients, targeting moieties, excipients, carriers, or structural components in drug delivery systems. The discovery of peptide-based therapeutic compounds, coupled with the development of new stabilization and formulation strategies, has led to a resurgence of antiviral peptide therapeutics over the past two decades. The ability of peptides to bind cell receptors and to facilitate membrane penetration and subsequent intracellular trafficking enables their use in various antiviral systems for improved targeting efficiency and treatment efficacy. Importantly, the self-assembly of peptides into well-defined nanostructures provides a vast library of discrete constructs and supramolecular biomaterials for systemic and local delivery of antiviral agents. We review here the recent progress in exploiting the therapeutic, biological, and self-assembling potential of peptides, peptide conjugates, and their supramolecular assemblies in treating human viral infections, with an emphasis on the treatment strategies for Human Immunodeficiency Virus (HIV).

Rational and Translational Implications of D-Amino Acids for Treatment-Resistant Schizophrenia: From Neurobiology to the Clinics

Schizophrenia has been conceptualized as a neurodevelopmental disorder with synaptic alterations and aberrant cortical–subcortical connections. Antipsychotics are the mainstay of schizophrenia treatment and nearly all share the common feature of dopamine D2 receptor occupancy, whereas glutamatergic abnormalities are not targeted by the presently available therapies. D-amino acids, acting as N-methyl-D-aspartate receptor (NMDAR) modulators, have emerged in the last few years as a potential augmentation strategy in those cases of schizophrenia that do not respond well to antipsychotics, a condition defined as treatment-resistant schizophrenia (TRS), affecting almost 30–40% of patients, and characterized by serious cognitive deficits and functional impairment. In the present systematic review, we address with a direct and reverse translational perspective the efficacy of D-amino acids, including D-serine, D-aspartate, and D-alanine, in poor responders. The impact of these molecules on the synaptic architecture is also considered in the light of dendritic spine changes reported in schizophrenia and antipsychotics’ effect on postsynaptic density proteins. Moreover, we describe compounds targeting D-amino acid oxidase and D-aspartate oxidase enzymes. Finally, other drugs acting at NMDAR and proxy of D-amino acids function, such as D-cycloserine, sarcosine, and glycine, are considered in the light of the clinical burden of TRS, together with other emerging molecules.

In silico screening for oligopeptides useful as capture and reporting probes for interleukin-6 biosensing

IL-6 is an important interleukin associated with inflammation and several diseases such as cancer. Evaluation of its levels in human blood sera is a critical step for an accurate diagnosis of the diseases. Our goal is to design peptides that can selectively bind in different poses with good affinities to IL-6. For this purpose, we started from the crystal structures of different IL-6/protein complexes available in the Protein Data Bank (PDB) to select short peptides in the interaction zones, in which we intentionally introduced point mutations to increase their stability and affinity. To examine their usefulness as capture and reporting probes for the IL-6 biosensing, the five peptides and their interaction with IL-6 were studied in saline aqueous solution. Molecular docking, MD, and MM-PBSA were used to investigate the affinity and stability of these complexes. The conformational changes, the distance between the mass centers, the gyration radii, and the numbers of hydrogen bonds were analyzed to select the most suitable candidates. Three peptides, namely CTE17, CAY15 and CSE25, have the highest affinities presenting significant numbers of residues that have contact frequencies greater than 50% of simulation run time and are the most promising candidates. CTE17 and CSE25 showed they can form a stable sandwich with the target protein. For sake of comparison, we examined the previously known peptides (FND20, INL19 and CEK17) having affinity to IL-6 and the affinity of the lead i.e. CSE25 to two other interleukin family members (IL-4 and to IL-10).

In silico design by docking and molecular dynamics of short peptides that can selectively recognize IL-6 for biosensing purposes.

Broad antiviral peptides against PRRSV based on novel linear epitopes on porcine CD163

PRRSV causes major economic losses to swine industry world-wide, which requires innovative antiviral agents. Porcine scavenger receptor CD163 has been identified as an essential fusion receptor for Porcine reproductive and respiratory Syndrome Virus (PRRSV) infection. In this study, novel antiviral peptides from pCD163 against PRRSV were developed based on broad neutralizing monoclonal antibodies. SRCR-5-9 of pCD163 from baculovirus efficiently binds to PRRSVs of lineage 8 and lineage 1, blocking infection in PAMs. A batch of monoclonal antibodies targeting SRCR-5-9 were generated and characterized. 8H2 and 4H7 block PRRSV infection by the disruption in viral attachment to PAMs. Virus titer reduced 100-1000 folds in average and the virus copy number decreased about 10⁴ folds with these antibodies. Linear epitopes of 8H2 and 4H7 were individually localized in SRCR6 (1-30 aa) and PSTI(1-15aa) of pCD163. Mutations of SRCR6 NI1718KT and PST SS1314AA abolished the recognition of 8H2 and 4H7 to the corresponding region individually. Peptides derived from the linear epitopes displayed a broad inhibitory effect on PRRSVs of different lineages in a dose-dependent manner and further modulated PRRSV-related NF-κB pathway. In conclusion, these findings deepen the understanding in the interaction between PRRSV and pCD163 receptor and provide alternative universal antiviral strategies against PRRSV.

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.

SARS-CoV-2 pan-variant inhibitory peptides deter S1-ACE2 interaction and neutralize delta and omicron pseudoviruses

Approved neutralizing antibodies that target the prototype Spike are losing their potency against the emerging variants of concern (VOCs) of SARS-CoV-2, particularly Omicron. Although SARS-CoV-2 is continuously adapting the host environment, emerging variants recognize the same ACE2 receptor for cell entry. Protein and peptide decoys derived from ACE2 or Spike proteins may hold the pan-variant inhibitory potential. Here, we deployed interactive structure- and pharmacophore-based approaches to design short and stable peptides –Coronavirus Spike Neutralizing Peptides (CSNPs)– capable of neutralizing all SARS-CoV-2 VOCs. After in silico structural stability investigation and free energies perturbation of the isolated and target-bound peptides, nine candidate peptides were evaluated for the biophysical interaction through SPR assay. CSNP1, CSNP2, and Pep1 dose-dependently bind the S1 domain of the prototype Spike, whereas CSNP4 binds both S1 and ACE2. After safety and immunocytochemistry evaluation, peptides were probed for their pan-variant inhibitory effects. CSNP1, CSNP2, and CSNP4 inhibited all VOCs dose-dependently, whereas Pep1 had a moderate effect. CSNP2 and CSNP4 could neutralize the wild-type pseudovirus up to 80 % when treated at 0.5 µM. Furthermore, CSNP4 synergize the neutralization effect of monoclonal antibody and CSNP1 in Delta variant pseudovirus assay as they target different regions on the RBD. Thus, we suggest that CSNPs are SARS-CoV-2 pan-variant inhibitory candidates for COVID-19 therapy, which may pave the way for combating the emerging immune-escaping variants. We also propose that CSNP1/2-CSNP4 peptide cocktail or CSNP1/4 mAbs cocktail with no overlapping epitopes could be effective therapeutic strategies against COVID-19.