De novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2

Repurposing a plant peptide cyclase for targeted lysine acylation

Transpeptidases are powerful tools for protein engineering but are largely restricted to acting at protein backbone termini. Alternative enzymatic approaches for internal protein labelling require bulky recognition motifs or non-proteinogenic reaction partners, potentially restricting which proteins can be modified or the types of modification that can be installed. Here we report a strategy for labelling lysine side chain ε-amines by repurposing an engineered asparaginyl ligase, which naturally catalyses peptide head-to-tail cyclization, for versatile isopeptide ligations that are compatible with peptidic substrates. We find that internal lysines with an adjacent leucine residue mimic the conventional N-terminal glycine-leucine substrate. This dipeptide motif enables efficient intra- or intermolecular ligation through internal lysine side chains, minimally leaving an asparagine C-terminally linked to the lysine side chain via an isopeptide bond. The versatility of this approach is demonstrated by the chemoenzymatic synthesis of peptides with non-native C terminus-to-side chain topology and the conjugation of chemically modified peptides to recombinant proteins.

Tertiary amide bond formation by an engineered asparaginyl ligase

Transpeptidases are powerful tools for site-specific protein modification, enabling the production of tailored biologics to investigate protein function and aiding the development of next-generation therapeutics and diagnostics. Although protein labelling at the N- or C-terminus is readily accomplished using a range of established transpeptidases, these reactions are generally limited to forming products that are linked by a standard (secondary) amide bond. Here we show that, unlike other widely used transpeptidases, an engineered asparaginyl ligase is able to efficiently synthesise tertiary amide bonds by accepting diverse secondary amine nucleophiles. These reactions proceed efficiently under mild conditions (near-neutral pH) and allow the optimal recognition elements for asparaginyl ligases (P1 Asn and P2'' Leu) to be preserved. Certain products, particularly proline-containing products, were found to be protected from recognition by the enzyme, allowing for straightforward sequential labelling of proteins. Additionally, incorporation of 4-azidoproline enables one-pot dual labelling directly at the ligation junction. These capabilities further expand the chemical diversity of asparaginyl ligase-catalysed reactions and provide an alternative approach for straightforward, successive modification of protein substrates.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

SARS-CoV-2 immunity in animal models

The COVID-19 pandemic, which was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide health crisis due to its transmissibility. SARS-CoV-2 infection results in severe respiratory illness and can lead to significant complications in affected individuals. These complications encompass symptoms such as coughing, respiratory distress, fever, infectious shock, acute respiratory distress syndrome (ARDS), and even multiple-organ failure. Animal models serve as crucial tools for investigating pathogenic mechanisms, immune responses, immune escape mechanisms, antiviral drug development, and vaccines against SARS-CoV-2. Currently, various animal models for SARS-CoV-2 infection, such as nonhuman primates (NHPs), ferrets, hamsters, and many different mouse models, have been developed. Each model possesses distinctive features and applications. In this review, we elucidate the immune response elicited by SARS-CoV-2 infection in patients and provide an overview of the characteristics of various animal models mainly used for SARS-CoV-2 infection, as well as the corresponding immune responses and applications of these models. A comparative analysis of transcriptomic alterations in the lungs from different animal models revealed that the K18-hACE2 and mouse-adapted virus mouse models exhibited the highest similarity with the deceased COVID-19 patients. Finally, we highlighted the current gaps in related research between animal model studies and clinical investigations, underscoring lingering scientific questions that demand further clarification.

Enhanced binding and inhibition of SARS-CoV-2 by a plant-derived ACE2 protein containing a fused mu tailpiece

Infectious diseases such as Coronavirus disease 2019 (COVID-19) and Middle East respiratory syndrome (MERS) present an increasingly persistent crisis in many parts of the world. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The angiotensin-converting enzyme 2 (ACE2) is a crucial cellular receptor for SARS-CoV-2 infection. Inhibition of the interaction between SARS-CoV-2 and ACE2 has been proposed as a target for the prevention and treatment of COVID-19. We produced four recombinant plant-derived ACE2 isoforms with or without the mu tailpiece (μ-tp) of immunoglobulin M (IgM) and the KDEL endoplasmic reticulum retention motif in a plant expression system. The plant-derived ACE2 isoforms bound whole SARS-CoV-2 virus and the isolated receptor binding domains of SARS-CoV-2 Alpha, Beta, Gamma, Delta, and Omicron variants. Fusion of μ-tp and KDEL to the ACE2 protein (ACE2 μK) had enhanced binding activity with SARS-CoV-2 in comparison with unmodified ACE2 protein derived from CHO cells. Furthermore, the plant-derived ACE2 μK protein exhibited no cytotoxic effects on Vero E6 cells and effectively inhibited SARS-CoV-2 infection. The efficient and rapid scalability of plant-derived ACE2 μK protein offers potential for the development of preventive and therapeutic agents in the early response to future viral outbreaks.

Discovery and development of COVID-19 vaccine from laboratory to clinic

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

In Silico Design and Evaluation of a Novel Therapeutic Agent Against the Spike Protein as a Novel Treatment Strategy for COVID-19 Treatment

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance.

AIMS

In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically.

METHODS

Various in silico software, bioinformatics, and patent databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein.

RESULTS

The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively.

CONCLUSIONS

In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

Susceptibility and Resistance of SARS-CoV-2 Variants to LCB1 and Its Multivalent Derivatives

LCB1 is a computationally designed three-helix miniprotein that precisely targets the spike (S) receptor-binding motif (RBM) of SARS-CoV-2, exhibiting remarkable antiviral efficacy; however, emerging SARS-CoV-2 variants could substantially compromise its neutralization effectiveness. In this study, we constructed two multivalent LCB1 fusion proteins termed LCB1T and LCB1T-Fc, and characterized their potency in inhibiting SARS-CoV-2 pseudovirus and authentic virus in vitro. In the inhibition of various SARS-CoV-2 variants, the two LCB1 fusion proteins exhibited markedly improved inhibitory activities compared to LCB1 as anticipated; however, it was observed that relative to the D614G mutation hosting variant, the variants Delta, Lambda, and Omicron BQ.1.1, XBB, XBB.1.5, and EG.5.1 caused various degrees of resistance to the two fusion proteins' inhibition, with XBB, XBB.1.5, and EG.5.1 variants showing high-level resistance. Moreover, we demonstrated that bat coronavirus RaTG13 and pangolin coronavirus PCoV-GD/PCoV-GX were highly sensitive to two LCB1 fusion proteins, but not LCB1, inhibition. Importantly, our findings revealed a notable decrease in the blocking capacity of the multivalent LCB1 inhibitor on the interaction between the virus's RBD/S and the cell receptor ACE2 when confronted with the XBB variant compared to WT and the Omicron BA.1 variant. In conclusion, our studies provide valuable insights into the antiviral profiling of multivalent LCB1 inhibitors and offer a promising avenue for the development of novel broad-spectrum antiviral therapeutics.

Molecular PET/CT mapping of rhACE2 distribution and quantification in organs to aid in SARS-CoV-2 targeted therapy

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a significant threat to public health. Angiotensin-converting enzyme 2 (ACE2) is a key receptor for SARS-CoV-2 infection. Recombinant human ACE2 (RhACE2), as a soluble supplement for human ACE2, can competitively block SARS-CoV-2 infection. In this study, a mouse organ in situ rhACE2 high aggregation model was constructed for the first time, and in vivo real-time positron emission tomography (PET) imaging of rhACE2 in the mouse model was performed using an ACE2-specific agent 68 Ga-HZ20. This radiotracer exhibits reliable radiochemical properties in vitro and maintains a high affinity for rhACE2 in vivo. In terms of probe uptake, 68 Ga-HZ20 showed a good target-to-nontarget ratio and was rapidly cleared from the circulatory system and excreted by the kidneys and urinary system. PET imaging with this radiotracer can noninvasively and accurately monitor the content and distribution of rhACE2 in the body, which clarifies that rhACE2 can aggregate in multiple organs, suggesting the preventive and therapeutic potential of rhACE2 for SARS-CoV-2 and COVID-19.

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".

Comparative Study of the Mutations Observed in the SARS-CoV-2 RBD Variants of Concern and Their Impact on the Interaction with the ACE2 Protein

SARS-CoV-2 strains have made an appearance across the globe, causing over 757 million cases and over 6.85 million deaths at the time of writing. The emergence of these variants shows the amplitude of genetic variation to which the wild-type strains have been subjected. The rise of the different SARS-CoV-2 variants resulting from such genetic modification has significantly affected COVD-19's major impact on proliferation, virulence, and clinics. With the emergence of the variants of concern, the spike protein has been identified as a possible therapeutic target due to its critical role in binding to human cells and pathogenesis. These mutations could be linked to functional heterogeneity and use a different infection strategy. For example, the Omicron variant's multiple mutations should be carefully examined, as they represent one of the most widely spread strains and hint to us that there may be more genetic changes in the virus. As a result, we applied a common protocol where we reconstructed SARS-CoV-2 variants of concern and performed molecular dynamics simulations to study the stability of the ACE2-RBD complex in each variant. We also carried out free energy calculations to compare the binding and biophysical properties of the different SARS-CoV-2 variants when they interact with ACE2. Therefore, we were able to obtain consistent results and uncover new crucial residues that were essential for preserving a balance between maintaining a high affinity for ACE2 and the capacity to evade RBD-targeted antibodies. Our detailed structural analysis showed that SARS-CoV-2 variants of concern show a higher affinity for ACE2 compared to the Wuhan strain. Additionally, residues K417N and E484K/A might play a crucial role in antibody evasion, whereas Q498R and N501Y are specifically mutated to strengthen RBD affinity to ACE2 and, thereby, increase the viral effect of the COVID-19 virus.

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor

The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.

De novo design of a stapled peptide targeting SARS-CoV-2 spike protein receptor-binding domain

Although effective vaccines have been developed against SARS-CoV-2, many regions in the world still have low rates of vaccination and new variants with mutations in the viral spike protein have reduced the effectiveness of most available vaccines and treatments. There is an urgent need for a drug to cure this disease and prevent infection. The SARS-CoV-2 virus enters the host cell through protein-protein interaction between the virus's spike protein and the host's angiotensin converting enzyme (ACE2). Using protein design software and molecular dynamics simulations, we have designed a 17-residue peptide (pep39), that binds to the spike protein receptor-binding domain (RBD) and blocks interaction of spike protein with ACE2. We have confirmed the binding activity of the designed peptide for the original spike protein and the delta variant spike protein using micro-cantilever and bio-layer interferometry (BLI) based methods. We also confirmed that pep39 strongly inhibits SARS-CoV-2 virus replication in Vero E6 cells. Taken together these data suggest that a newly designed spike protein RBD blocking peptide pep39 has a potential as a SARS-CoV-2 virus inhibitor.

De Novo Human Angiotensin-Converting Enzyme 2 Decoy NL-CVX1 Protects Mice From Severe Disease After Severe Acute Respiratory Syndrome Coronavirus 2 Infection

The emergence of novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need to investigate alternative approaches to prevent infection and treat patients with coronavirus disease 2019. Here, we report the preclinical efficacy of NL-CVX1, a de novo decoy that blocks virus entry into cells by binding with nanomolar affinity and high specificity to the receptor-binding domain of the SARS-CoV-2 spike protein. Using a transgenic mouse model of SARS-CoV-2 infection, we showed that a single prophylactic intranasal dose of NL-CVX1 conferred complete protection from severe disease following SARS-CoV-2 infection. Multiple therapeutic administrations of NL-CVX1 also protected mice from succumbing to infection. Finally, we showed that infected mice treated with NL-CVX1 developed both anti-SARS-CoV-2 antibodies and memory T cells and were protected against reinfection a month after treatment. Overall, these observations suggest NL-CVX1 is a promising therapeutic candidate for preventing and treating severe SARS-CoV-2 infections.

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.

Thermodynamic and kinetic analysis of the LAO binding protein and its isolated domains reveal non-additivity in stability, folding and function

Substrate-binding proteins (SBPs) are used by organisms from the three domains of life for transport and signalling. SBPs are composed of two domains that collectively trap ligands with high affinity and selectivity. To explore the role of the domains and the integrity of the hinge region between them in the function and conformation of SBPs, here, we describe the ligand binding, conformational stability and folding kinetics of the Lysine Arginine Ornithine (LAO) binding protein from Salmonella thiphimurium and constructs corresponding to its two independent domains. LAO is a class II SBP formed by a continuous and a discontinuous domain. Contrary to the expected behaviour based on their connectivity, the discontinuous domain shows a stable native-like structure that binds l-arginine with moderate affinity, whereas the continuous domain is barely stable and shows no detectable ligand binding. Regarding folding kinetics, studies of the entire protein revealed the presence of at least two intermediates. While the unfolding and refolding of the continuous domain exhibited only a single intermediate and simpler and faster kinetics than LAO, the folding mechanism of the discontinuous domain was complex and involved multiple intermediates. These findings suggest that in the complete protein the continuous domain nucleates folding and that its presence funnels the folding of the discontinuous domain avoiding nonproductive interactions. The strong dependence of the function, stability and folding pathway of the lobes on their covalent association is most likely the result of the coevolution of both domains as a single unit.

Topological deep learning based deep mutational scanning

High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Exploration of novel αβ-protein folds through de novo design

A fundamental question in protein evolution is whether nature has exhaustively sampled nearly all possible protein folds throughout evolution, or whether a large fraction of the possible folds remains unexplored. To address this question, we defined a set of rules for β-sheet topology to predict novel αβ-folds and carried out a systematic de novo protein design exploration of the novel αβ-folds predicted by the rules. The designs for all eight of the predicted novel αβ-folds with a four-stranded β-sheet, including a knot-forming one, folded into structures close to the design models. Further, the rules predicted more than 10,000 novel αβ-folds with five- to eight-stranded β-sheets; this number far exceeds the number of αβ-folds observed in nature so far. This result suggests that a vast number of αβ-folds are possible, but have not emerged or have become extinct due to evolutionary bias.

A Comprehensive Technology Platform for the Rapid Discovery of Peptide Inhibitors against SARS-CoV-2 Pseudovirus Infection

We developed and validated a technology platform for designing and testing peptides inhibiting the infectivity of SARS-CoV-2 spike protein-based pseudoviruses. This platform integrates target evaluation, in silico inhibitor design, peptide synthesis, and efficacy screening. We generated a cyclic peptide library derived from the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor. The cell-free validation process by ELISA competition assays and Surface Plasmon Resonance (SPR) studies revealed that the cyclic peptide c9_05, but not its linear form, binds well to ACE2. Moreover, it effectively inhibited the transduction in HEK293, stably expressing the human ACE2 receptor of pseudovirus particles displaying the SARS-CoV-2 spike in the Wuhan or UK variants. However, the inhibitory efficacy of c9_05 was negligible against the Omicron variant, and it failed to impede the entry of pseudoviruses carrying the B.1.351 (South African) spike. These variants contain three or more mutations known to increase affinity to ACE2. This suggests further refinement is needed for potential SARS-CoV-2 inhibition. Our study hints at a promising approach to develop inhibitors targeting viral infectivity receptors, including SARS-CoV-2's. This platform also promises swift identification and evaluation of inhibitors for other emergent viruses.

Molecular Insights into the Binding Behavior of Imidazolium Ionic Liquids to the Receptor Binding Domain of the SARS-CoV-2 Spike Protein

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is considered as a key target for the design and development of COVID-19 drugs and inhibitors. Due to their unique structure and properties, ionic liquids (ILs) have many special interactions with proteins, showing great potential in biomedicine. Nevertheless, few research studies have been carried out on ILs and the spike RBD protein. Here, we explore the interaction of ILs and the RBD protein through large-scale molecular dynamics simulations (4 μs in total). It was found that IL cations with long alkyl chain lengths (nchain) could spontaneously bind to the cavity region of the RBD protein. The longer the alkyl chain is, the stabler the cations bind to the protein. The binding free energy (ΔG) had the same trend, peaking at nchain = 12 with -101.19 kJ/mol. The cationic chain lengths and their fit to the pocket are decisive factors that influence the binding strength of cations and proteins. The cationic imidazole ring has a high contact frequency with phenylalanine and tryptophan, and the hydrophobic residues phenylalanine, valine, leucine, and isoleucine are the most interacting residues with side chains of cations. Meanwhile, through analysis of the interaction energy, the hydrophobic and π-π interactions are the main contributors to the high affinity between cations and the RBD protein. In addition, the long-chain ILs would also act on the protein through clustering. These studies not only provide insights into the molecular interaction between ILs and the RBD of SARS-CoV-2 but also contribute to the rational design of IL-based drugs, drug carriers, and selective inhibitors as a therapeutic for SARS-CoV-2.

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.

Discovery of Highly Potent Small Molecule Pan-Coronavirus Fusion Inhibitors

The unprecedented pandemic of COVID-19, caused by a novel coronavirus, SARS-CoV-2, and its highly transmissible variants, led to massive human suffering, death, and economic devastation worldwide. Recently, antibody-evasive SARS-CoV-2 subvariants, BQ and XBB, have been reported. Therefore, the continued development of novel drugs with pan-coronavirus inhibition is critical to treat and prevent infection of COVID-19 and any new pandemics that may emerge. We report the discovery of several highly potent small-molecule inhibitors. One of which, NBCoV63, showed low nM potency against SARS-CoV-2 (IC₅₀: 55 nM), SARS-CoV-1 (IC₅₀: 59 nM), and MERS-CoV (IC₅₀: 75 nM) in pseudovirus-based assays with excellent selectivity indices (SI > 900), suggesting its pan-coronavirus inhibition. NBCoV63 showed equally effective antiviral potency against SARS-CoV-2 mutant (D614G) and several variants of concerns (VOCs) such as B.1.617.2 (Delta), B.1.1.529/BA.1 and BA.4/BA.5 (Omicron), and K417T/E484K/N501Y (Gamma). NBCoV63 also showed similar efficacy profiles to Remdesivir against authentic SARS-CoV-2 (Hong Kong strain) and two of its variants (Delta and Omicron), SARS-CoV-1, and MERS-CoV by plaque reduction in Calu-3 cells. Additionally, we show that NBCoV63 inhibits virus-mediated cell-to-cell fusion in a dose-dependent manner. Furthermore, the absorption, distribution, metabolism, and excretion (ADME) data of NBCoV63 demonstrated drug-like properties.

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.

Molecular determinants of μ-conotoxin KIIIA interaction with the human voltage-gated sodium channel NaV1.7

The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between μ-Conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels.

Peptide-based direct electrochemical detection of receptor binding domains of SARS-CoV-2 spike protein in pristine samples

RNA isolation and amplification-free user-friendly detection of SARS-CoV-2 is the need of hour especially at resource limited settings. Herein, we devised the peptides of human angiotensin converting enzyme-2 (hACE-2) as bioreceptor at electrode interface for selective targeting of receptor binding domains (RBD) of SARS-CoV-2 spike protein (SP). Disposable carbon-screen printed electrode modified with methylene blue (MB) electroadsorbed graphene oxide (GO) has been constructed as cost-efficient and scalable platform for hACE-2 peptide-based SARS-CoV-2 detection. In silico molecular docking of customized 25 mer peptides with RBD of SARS-CoV-2 SP were validated by AutoDock CrankPep. N-terminal region of ACE-2 showed higher binding affinity of - 20.6 kcal/mol with 15 H-bond, 9 of which were < 3 Å. Electrochemical biosensing of different concentrations of SPs were determined by cyclic voltammetry (CV) and chronoamperometry (CA), enabling a limit of detection (LOD) of 0.58 pg/mL and 0.71 pg/mL, respectively. MB-GO devised hACE-2 peptide platform exert an enhanced current sensitivity of 0.0105 mA/pg mL^-1 cm^-2 (R² = 0.9792) (CV) and 0.45 nA/pg mL^-1 (R² = 0.9570) (CA) against SP in the range of 1 pg/mL to 1 µg/mL. For clinical feasibility, nasopharyngeal and oropharyngeal swab specimens in viral transport medium were directly tested with the prepared peptide biosensor and validated with RT-PCR, promising for point-of-need analysis.

Recently Designed Multivalent Spike Binders Cannot Bind Multivalently─How Do They Achieve Enhanced Avidity to SARS-CoV-2?

The trimeric spike protein of SARS-CoV-2 has been targeted by antibody mimics that bind near or at the receptor-binding domain to neutralize the virus. Several independent studies have reported enhanced binding avidity for dimers and trimers, where binding domains are connected by short peptides. The enhanced avidity of the multivalent constructs was attributed to their simultaneously binding two or three sites within a single spike trimer. We argue here that the 15-20 amino acid peptide linkers, when considered as worm-like-chains, are too short to span the binding sites within a single spike. The enhanced avidity of the multivalent constructs may be explained by a rebinding mechanism, which does not involve multivalent binding.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Vectored Immunoprophylaxis and Treatment of SARS-CoV-2 Infection

Vectored immunoprophylaxis was first developed as a means to establish engineered immunity to HIV through the use of an adeno-associated viral vector expressing a broadly neutralizing antibody. We have applied this concept to establish long-term prophylaxis against SARS-CoV-2 by adeno-associated and lentiviral vectors expressing a high affinity ACE2 decoy receptor. Administration of decoy-expressing AAV vectors based on AAV2.retro and AAV6.2 by intranasal instillation or intramuscular injection protected mice against high-titered SARS-CoV-2 infection. AAV and lentiviral vectored immunoprophylaxis was durable and active against recent SARS-CoV-2 Omicron subvariants. The AAV vectors were also effective when administered up to 24 hours post-infection. Vectored immunoprophylaxis could be of value for immunocompromised individuals for whom vaccination is not practical and as a means to rapidly establish protection from infection. Unlike monoclonal antibody therapy, the approach is expected to remain active despite continued evolution viral variants.

Emerging Dominant SARS-CoV-2 Variants

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants, Omicron (BA.1), BA.2, and BA.4/BA.5, were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. On the basis of newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BP.1, BL*, BA.2.75*, BQ.1*, and particularly BN.1* have a high potential to become the new dominant variants to drive the next surge. Our key projection about these variants dominance made on Oct. 18, 2022 (see arXiv:2210.09485) became reality in late November 2022.

Multivalent IgM scaffold enhances the therapeutic potential of variant-agnostic ACE2 decoys against SARS-CoV-2

As immunological selection for escape mutants continues to give rise to future SARS-CoV-2 variants, novel universal therapeutic strategies against ACE2-dependent viruses are needed. Here we present an IgM-based decavalent ACE2 decoy that has variant-agnostic efficacy. In immuno-, pseudovirus, and live virus assays, IgM ACE2 decoy had potency comparable or superior to leading SARS-CoV-2 IgG-based mAb therapeutics evaluated in the clinic, which were variant-sensitive in their potency. We found that increased ACE2 valency translated into increased apparent affinity for spike protein and superior potency in biological assays when decavalent IgM ACE2 was compared to tetravalent, bivalent, and monovalent ACE2 decoys. Furthermore, a single intranasal dose of IgM ACE2 decoy at 1 mg/kg conferred therapeutic benefit against SARS-CoV-2 Delta variant infection in a hamster model. Taken together, this engineered IgM ACE2 decoy represents a SARS-CoV-2 variant-agnostic therapeutic that leverages avidity to drive enhanced target binding, viral neutralization, and in vivo respiratory protection against SARS-CoV-2.

Deep learning of protein sequence design of protein-protein interactions

MOTIVATION

As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates-yet, it is central to most biological processes.

RESULTS

We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.

AVAILABILITY AND IMPLEMENTATION

The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Survival-based CRISPR genetic screens across a panel of permissive cell lines identify common and cell-specific SARS-CoV-2 host factors

SARS-CoV-2 depends on host cell components for infection and replication. Identification of virus-host dependencies offers an effective way to elucidate mechanisms involved in viral infection and replication. If druggable, host factor dependencies may present an attractive strategy for anti-viral therapy. In this study, we performed genome wide CRISPR knockout screens in Vero E6 cells and four human cell lines including Calu-3, UM-UC-4, HEK-293 and HuH-7 to identify genetic regulators of SARS-CoV-2 infection. Our findings identified only ACE2, the cognate SARS-CoV-2 entry receptor, as a common host dependency factor across all cell lines, while other host genes identified were largely cell line specific, including known factors TMPRSS2 and CTSL. Several of the discovered host-dependency factors converged on pathways involved in cell signalling, immune-related pathways, and chromatin modification. Notably, the chromatin modifier gene KMT2C in Calu-3 cells had the strongest impact in preventing SARS-CoV-2 infection when perturbed.

Lung directed antibody gene transfer confers protection against SARS-CoV-2 infection

BACKGROUND

The COVID-19 pandemic continues to be a worldwide threat and effective antiviral drugs and vaccines are being developed in a joint global effort. However, some elderly and immune-compromised populations are unable to raise an effective immune response against traditional vaccines.

AIMS

We hypothesised that passive immunity engineered by the in vivo expression of anti-SARS-CoV-2 monoclonal antibodies (mAbs), an approach termed vectored-immunoprophylaxis (VIP), could offer sustained protection against COVID-19 in all populations irrespective of their immune status or age.

METHODS

We developed three key reagents to evaluate VIP for SARS-CoV-2: (i) we engineered standard laboratory mice to express human ACE2 via rAAV9 in vivo gene transfer, to allow in vivo assessment of SARS-CoV-2 infection, (ii) to simplify in vivo challenge studies, we generated SARS-CoV-2 Spike protein pseudotyped lentiviral vectors as a simple mimic of authentic SARS-CoV-2 that could be used under standard laboratory containment conditions and (iii) we developed in vivo gene transfer vectors to express anti-SARS-CoV-2 mAbs.

CONCLUSIONS

A single intranasal dose of rAAV9 or rSIV.F/HN vectors expressing anti-SARS-CoV-2 mAbs significantly reduced SARS-CoV-2 mimic infection in the lower respiratory tract of hACE2-expressing mice. If translated, the VIP approach could potentially offer a highly effective, long-term protection against COVID-19 for highly vulnerable populations; especially immune-deficient/senescent individuals, who fail to respond to conventional SARS-CoV-2 vaccines. The in vivo expression of multiple anti-SARS-CoV-2 mAbs could enhance protection and prevent rapid mutational escape.

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36% more infectious than BA.2 and are projected to become new dominant variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.

Recombinant ACE2 protein protects against acute lung injury induced by SARS-CoV-2 spike RBD protein

Background

SARS-CoV-2 infection leads to acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Both clinical data and animal experiments suggest that the renin–angiotensin system (RAS) is involved in the pathogenesis of SARS-CoV-2-induced ALI. Angiotensin-converting enzyme 2 (ACE2) is the functional receptor for SARS-CoV-2 and a crucial negative regulator of RAS. Recombinant ACE2 protein (rACE2) has been demonstrated to play protective role against SARS-CoV and avian influenza-induced ALI, and more relevant, rACE2 inhibits SARS-CoV-2 proliferation in vitro. However, whether rACE2 protects against SARS-CoV-2-induced ALI in animal models and the underlying mechanisms have yet to be elucidated.

Methods and Results

Here, we demonstrated that the SARS-CoV-2 spike receptor-binding domain (RBD) protein aggravated lipopolysaccharide (LPS)-induced ALI in mice. SARS-CoV-2 spike RBD protein directly binds and downregulated ACE2, leading to an elevation in angiotensin (Ang) II. AngII further increased the NOX1/2 through AT1R, subsequently causing oxidative stress and uncontrolled inflammation and eventually resulting in ALI/ARDS. Importantly, rACE2 remarkably reversed SARS-CoV-2 spike RBD protein-induced ALI by directly binding SARS-CoV-2 spike RBD protein, cleaving AngI or cleaving AngII.

Conclusion

This study is the first to prove that rACE2 plays a protective role against SARS-CoV-2 spike RBD protein-aggravated LPS-induced ALI in an animal model and illustrate the mechanism by which the ACE2-AngII-AT1R-NOX1/2 axis might contribute to SARS-CoV-2-induced ALI.

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Novel Regioselective Approach to Cyclize Phage-Displayed Peptides in Combination with Epitope-Directed Selection to Identify a Potent Neutralizing Macrocyclic Peptide for SARS-CoV-2

Using the regioselective cyanobenzothiazole condensation reaction with an N-terminal cysteine and the chloroacetamide reaction with an internal cysteine, a phage-displayed macrocyclic 12-mer peptide library was constructed and subsequently validated. Using this library in combination with iterative selections against two epitopes from the receptor binding domain (RBD) of the novel severe acute respiratory syndrome virus 2 (SARS-CoV-2) Spike protein, macrocyclic peptides that strongly inhibit the interaction between the Spike RBD and angiotensin-converting enzyme 2 (ACE2), the human host receptor of SARS-CoV-2, were identified. The two epitopes were used instead of the Spike RBD to avoid selection of nonproductive macrocyclic peptides that bind RBD but do not directly inhibit its interactions with ACE2. Antiviral tests against SARS-CoV-2 showed that one macrocyclic peptide is highly potent against viral reproduction in Vero E6 cells with an EC50 value of 3.1 μM. The AlphaLISA-detected IC50 value for this macrocyclic peptide was 0.3 μM. The current study demonstrates that two kinetically controlled reactions toward N-terminal and internal cysteines, respectively, are highly effective in the construction of phage-displayed macrocyclic peptides, and the selection based on the SARS-CoV-2 Spike epitopes is a promising methodology in the identification of peptidyl antivirals.

Emerging dominant SARS-CoV-2 variants

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.

Native, engineered and de novo designed ligands targeting the SARS-CoV-2 spike protein

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the deadly coronavirus disease 2019 (Covid-19) and is a concerning hazard to public health. This virus infects cells by establishing a contact between its spike protein (S-protein) and host human angiotensin-converting enzyme 2 (hACE2) receptor, subsequently initiating viral fusion. The inhibition of the interaction between the S-protein and hACE2 has immediately drawn attention amongst the scientific community, and the S-protein was considered the prime target to design vaccines and to develop affinity ligands for diagnostics and therapy. Several S-protein binders have been reported at a fast pace, ranging from antibodies isolated from immunised patients to de novo designed ligands, with some binders already yielding promising in vivo results in protecting against SARS-CoV-2. Natural, engineered and designed affinity ligands targeting the S-protein are herein summarised, focusing on molecular recognition aspects, whilst identifying preferred hot spots for ligand binding. This review serves as inspiration for the improvement of already existing ligands or for the design of new affinity ligands towards SARS-CoV-2 proteins. Lessons learnt from the Covid-19 pandemic are also important to consolidate tools and processes in protein engineering to enable the fast discovery, production and delivery of diagnostic, prophylactic, and therapeutic solutions in future pandemics.

A dimeric proteomimetic prevents SARS-CoV-2 infection by dimerizing the spike protein

Protein tertiary structure mimetics are valuable tools to target large protein-protein interaction interfaces. Here, we demonstrate a strategy for designing dimeric helix-hairpin motifs from a previously reported three-helix-bundle miniprotein that targets the receptor-binding domain (RBD) of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Through truncation of the third helix and optimization of the interhelical loop residues of the miniprotein, we developed a thermostable dimeric helix-hairpin. The dimeric four-helix bundle competes with the human angiotensin-converting enzyme 2 (ACE2) in binding to RBD with 2:2 stoichiometry. Cryogenic-electron microscopy revealed the formation of dimeric spike ectodomain trimer by the four-helix bundle, where all the three RBDs from either spike protein are attached head-to-head in an open conformation, revealing a novel mechanism for virus neutralization. The proteomimetic protects hamsters from high dose viral challenge with replicative SARS-CoV-2 viruses, demonstrating the promise of this class of peptides that inhibit protein-protein interaction through target dimerization.

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.

Comprehensive role of SARS-CoV-2 spike glycoprotein in regulating host signaling pathway

Since the outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, global public health and the economy have suffered unprecedented damage. Based on the increasing related literature, the characteristics and pathogenic mechanisms of the virus, and epidemiological and clinical features of the disease are being rapidly discovered. The spike glycoprotein (S protein), as a key antigen of SARS-CoV-2 for developing vaccines, antibodies, and drug targets, has been shown to play an important role in viral entry, tissue tropism, and pathogenesis. In this review, we summarize the molecular mechanisms of interaction between S protein and host factors, especially receptor-mediated viral modulation of host signaling pathways, and highlight the progression of potential therapeutic targets, prophylactic and therapeutic agents for prevention and treatment of SARS-CoV-2 infection.

Protein Scaffold-Based Multimerization of Soluble ACE2 Efficiently Blocks SARS-CoV-2 Infection In Vitro and In Vivo

Soluble ACE2 (sACE2) decoys are promising agents to inhibit SARS-CoV-2, as their efficiency is unlikely to be affected by escape mutations. However, their success is limited by their relatively poor potency. To address this challenge, multimeric sACE2 consisting of SunTag or MoonTag systems is developed. These systems are extremely effective in neutralizing SARS-CoV-2 in pseudoviral systems and in clinical isolates, perform better than the dimeric or trimeric sACE2, and exhibit greater than 100-fold neutralization efficiency, compared to monomeric sACE2. SunTag or MoonTag fused to a more potent sACE2 (v1) achieves a sub-nanomolar IC₅₀ , comparable with clinical monoclonal antibodies. Pseudoviruses bearing mutations for variants of concern, including delta and omicron, are also neutralized efficiently with multimeric sACE2. Finally, therapeutic treatment of sACE2(v1)-MoonTag provides protection against SARS-CoV-2 infection in an in vivo mouse model. Therefore, highly potent multimeric sACE2 may offer a promising treatment approach against SARS-CoV-2 infections.

Computational Design of Miniprotein Inhibitors Targeting SARS-CoV-2 Spike Protein

The ongoing pandemic of COVID-19 caused by SARS-CoV-2 has become a global health problem. There is an urgent need to develop therapeutic drugs, effective therapies, and vaccines to prevent the spread of the virus. The virus first enters the host cell through the interaction between the receptor binding domain (RBD) of spike protein and the peptidase domain (PD) of the angiotensin-converting enzyme 2 (ACE2). Therefore, blocking the binding of RBD and ACE2 is a promising strategy to inhibit the invasion and infection of the virus in the host cell. In the study, we designed several miniprotein inhibitors against SARS-CoV-2 by single/double/triple-point mutant, based on the initial inhibitor LCB3. Molecular dynamics (MD) simulations and trajectory analysis were performed for an in-depth analysis of the structural stability, essential protein motions, and per-residue energy decomposition involved in the interaction of inhibitors with the RBD. The results showed that the inhibitors have adapted the protein RBD in the binding interface, thereby forming stable complexes. These inhibitors display low binding free energy in the MM/PBSA calculations, substantiating their strong interaction with RBD. Moreover, the binding affinity of the best miniprotein inhibitor, H6Y-M7L-L17F mutant, to RBD was ∼45 980 times (ΔG = RT ln Ki) higher than that of the initial inhibitor LCB3. Following H6Y-M7L-L17F mutant, the inhibitors with strong binding activity are successively H6Y-L17F, L17F, H6Y, and F30Y mutants. Our research proves that the miniprotein inhibitors can maintain their secondary structure and have a highly stable blocking (binding) effect on SARS-CoV-2. This study proposes novel miniprotein mutant inhibitors with enhanced binding to spike protein and provides potential guidance for the rational design of new SARS-CoV-2 spike protein inhibitors.

Clinical grade ACE2 as a universal agent to block SARS-CoV-2 variants

The recent emergence of multiple SARS-CoV-2 variants has caused considerable concern due to both reduced vaccine efficacy and escape from neutralizing antibody therapeutics. It is, therefore, paramount to develop therapeutic strategies that inhibit all known and future SARS-CoV-2 variants. Here, we report that all SARS-CoV-2 variants analyzed, including variants of concern (VOC) Alpha, Beta, Gamma, Delta, and Omicron, exhibit enhanced binding affinity to clinical grade and phase 2 tested recombinant human soluble ACE2 (APN01). Importantly, soluble ACE2 neutralized infection of VeroE6 cells and human lung epithelial cells by all current VOC strains with markedly enhanced potency when compared to reference SARS-CoV-2 isolates. Effective inhibition of infections with SARS-CoV-2 variants was validated and confirmed in two independent laboratories. These data show that SARS-CoV-2 variants that have emerged around the world, including current VOC and several variants of interest, can be inhibited by soluble ACE2, providing proof of principle of a pan-SARS-CoV-2 therapeutic.

Scaffolding protein functional sites using deep learning

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Development of an aerosol intervention for COVID-19 disease: Tolerability of soluble ACE2 (APN01) administered via nebulizer

As ACE2 is the critical SARS-CoV-2 receptor, we hypothesized that aerosol administration of clinical grade soluble human recombinant ACE2 (APN01) will neutralize SARS-CoV-2 in the airways, limit spread of infection in the lung, and mitigate lung damage caused by deregulated signaling in the renin-angiotensin (RAS) and Kinin pathways. Here, after demonstrating in vitro neutralization of SARS-CoV-2 by APN01, and after obtaining preliminary evidence of its tolerability and preventive efficacy in a mouse model, we pursued development of an aerosol formulation. As a prerequisite to a clinical trial, we evaluated both virus binding activity and enzymatic activity for cleavage of Ang II following aerosolization. We report successful aerosolization for APN01, retaining viral binding as well as catalytic RAS activity. Dose range-finding and IND-enabling repeat-dose aerosol toxicology testing were conducted in dogs. Twice daily aerosol administration for two weeks at the maximum feasible concentration revealed no notable toxicities. Based on these results, a Phase I clinical trial in healthy volunteers has now been initiated (NCT05065645), with subsequent Phase II testing planned for individuals with SARS-CoV-2 infection.