In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 receptor binding domain with hACE2

The receptor binding motif (RBM) within the S-protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been touted as one of the main targets for vaccine/therapeutic development due to its interaction with the human angiotensin II converting enzyme 2 (hACE2) to facilitate virus entry into the host cell. The mechanism of action is based on the disruption of binding between the RBM and the hACE2 to prevent virus uptake for replication. In this work, we applied in silico approaches to design specific competitive binders for SARS-CoV-2 S-protein receptor binding motif (RBM) by using hACE2 peptidase domain (PD) mutants. Online single point mutation servers were utilised to estimate the effect of PD mutation on the binding affinity with RBM. The PD mutants were then modelled and the binding free energy was calculated. Three PD variants were designed with an increased affinity and interaction with SARS-CoV-2-RBM. It is hope that these designs could serve as the initial work for vaccine/drug development and could eventually interfere the preliminary recognition between SARS-CoV-2 and the host cell. © 2022 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.

Structure-based design of a SARS-CoV-2 Omicron-specific inhibitor.

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) introduced a relatively large number of mutations, including three mutations in the highly conserved heptad repeat 1 (HR1) region of the spike glycoprotein (S) critical for its membrane fusion activity. We show that one of these mutations, N969K induces a substantial displacement in the structure of the heptad repeat 2 (HR2) backbone in the HR1HR2 postfusion bundle. Due to this mutation, fusion-entry peptide inhibitors based on the Wuhan strain sequence are less efficacious. Here, we report an Omicron-specific peptide inhibitor designed based on the structure of the Omicron HR1HR2 postfusion bundle. Specifically, we inserted an additional residue in HR2 near the Omicron HR1 K969 residue to better accommodate the N969K mutation and relieve the distortion in the structure of the HR1HR2 postfusion bundle it introduced. The designed inhibitor recovers the loss of inhibition activity of the original longHR2_42 peptide with the Wuhan strain sequence against the Omicron variant in both a cell-cell fusion assay and a vesicular stomatitis virus (VSV)-SARS-CoV-2 chimera infection assay, suggesting that a similar approach could be used to combat future variants. From a mechanistic perspective, our work suggests the interactions in the extended region of HR2 may mediate the initial landing of HR2 onto HR1 during the transition of the S protein from the prehairpin intermediate to the postfusion state.

VTR: A Web Tool for Identifying Analogous Contacts on Protein Structures and Their Complexes.

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins. Here we show VTR, a new method for the detection of analogous contacts in protein pairs. The VTR web tool performs structural alignment between proteins and detects interactions that occur in similar regions. To evaluate our tool, we proposed three case studies: we 1) compared vertebrate myoglobin and truncated invertebrate hemoglobin; 2) analyzed interactions between the spike protein RBD of SARS-CoV-2 and the cell receptor ACE2; and 3) compared a glucose-tolerant and a non-tolerant ß-glucosidase enzyme used for biofuel production. The case studies demonstrate the potential of VTR for the understanding of functional similarities between distantly sequence-related proteins, as well as the exploration of important drug targets and rational design of enzymes for industrial applications. We envision VTR as a promising tool for understanding differences and similarities between homologous proteins with similar 3D structures but different sequences. VTR is available at http://bioinfo.dcc.ufmg.br/vtr.

Rational Design of Self-Emulsifying Pellet Formulation of Thymol: Technology Development Guided by Molecular-Level Structure Characterization and Ex Vivo Testing.

The growing need for processing natural lipophilic and often volatile substances such as thymol, a promising candidate for topical treatment of intestinal mucosa, led us to the utilization of solid-state nuclear magnetic resonance (ss-NMR) spectroscopy for the rational design of enteric pellets with a thymol self-emulsifying system (SES). The SES (triacylglycerol, Labrasol®, and propylene glycol) provided a stable o/w emulsion with particle size between 1 and 7 µm. The ex vivo experiment confirmed the SES mucosal permeation and thymol delivery to enterocytes. Pellets W90 (MCC, Neusilin®US2, chitosan) were prepared using distilled water (90 g) by the M1-M3 extrusion/spheronisation methods varying in steps number and/or cumulative time. The pellets (705-740 µm) showed mostly comparable properties-zero friability, low intraparticular porosity (0-0.71%), and relatively high density (1.43-1.45%). They exhibited similar thymol release for 6 h (burst effect in 15th min ca. 60%), but its content increased (30-39.6 mg/g) with a shorter process time. The M3-W90 fluid-bed coated pellets (Eudragit®L) prevented undesirable thymol release in stomach conditions (<10% for 3 h). A detailed, ss-NMR investigation revealed structural differences across samples prepared by M1-M3 methods concerning system stability and internal interactions. The suggested formulation and methodology are promising for other lipophilic volatiles in treating intestinal diseases.

Rational Design of Peptides Derived from Odorant-Binding Proteins for SARS-CoV-2-Related Volatile Organic Compounds Recognition.

Peptides are promising molecular-binding elements and have attracted great interest in novel biosensor development. In this study, a series of peptides derived from odorant-binding proteins (OBPs) were rationally designed for recognition of SARS-CoV-2-related volatile organic compounds (VOCs). Ethanol, nonanal, benzaldehyde, acetic acid, and acetone were selected as representative VOCs in the exhaled breath during the COVID-19 infection. Computational docking and prediction tools were utilized for OBPs peptide characterization and analysis. Multiple parameters, including the docking model, binding affinity, sequence specification, and structural folding, were investigated. The results demonstrated a rational, rapid, and efficient approach for designing breath-borne VOC-recognition peptides, which could further improve the biosensor performance for pioneering COVID-19 screening and many other applications.

In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 receptor binding domain with hACE2

The receptor binding motif (RBM) within the S-protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been touted as one of the main targets for vaccine/therapeutic development due to its interaction with the human angiotensin II converting enzyme 2 (hACE2) to facilitate virus entry into the host cell. The mechanism of action is based on the disruption of binding between the RBM and the hACE2 to prevent virus uptake for replication. In this work, we applied in silico approaches to design specific competitive binders for SARS-CoV-2 S-protein receptor binding motif (RBM) by using hACE2 peptidase domain (PD) mutants. Online single point mutation servers were utilised to estimate the effect of PD mutation on the binding affinity with RBM. The PD mutants were then modelled and the binding free energy was calculated. Three PD variants were designed with an increased affinity and interaction with SARS-CoV-2-RBM. It is hope that these designs could serve as the initial work for vaccine/drug development and could eventually interfere the preliminary recognition between SARS-CoV-2 and the host cell.

Structural Insights into the Design of Synthetic Nanobody Libraries.

Single domain antibodies from camelids, or nanobodies, are a unique class of antibody fragments with several advantageous characteristics: small monomeric size, high stability and solubility and easy tailoring for multiple applications. Nanobodies are gaining increasing acceptance as diagnostic tools and promising therapeutic agents in cancer and other diseases. While most nanobodies are obtained from immunized animals of the camelid family, a few synthetic nanobody libraries constructed in recent years have shown the capability of generating high quality nanobodies in terms of affinity and stability. Since this synthetic approach has important advantages over the use of animals, the recent advances are indeed encouraging. Here we review over a dozen synthetic nanobody libraries reported so far and discuss the different approaches followed in their construction and validation, with an emphasis on framework and hypervariable loop design as critical issues defining their potential as high-class nanobody sources.

An overview of progress from empirical to rational design in modern vaccine development, with an emphasis on computational tools and immunoinformatics approaches.

Vaccination remains the most effective strategy for preventing and controlling infectious diseases. Numerous conventional vaccines, especially live attenuated, inactivated (killed) microorganisms and subunit vaccines, lead to an effective induction of protective immune responses, mainly antibody-mediated responses against pathogens. However, it has become known that a wide range of highly dangerous pathogens are uncontrollable via conventional vaccination strategies. Recent advances in molecular biology, immunology, genetics, biochemistry, and bioinformatics have provided new prospects for vaccine development. As a result of these advances, several new strategies for vaccine design, development, and production have appeared. These strategies show advantages over conventional vaccines. In this review, we discuss some of the major novel approaches, including recombinant protein vaccines, live recombinant viral and bacterial vectors, DNA and RNA vaccines, reverse vaccinology and reverse genetics approaches. Moreover, we have described the recent progresses on computational tools and immunoinformatics approaches for identifying, designing, and developing new candidate vaccines.

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution.

BACKGROUND: Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints. RESULTS: We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods. CONCLUSION: Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Targeting Viral Surface Proteins through Structure-Based Design.

The emergence of novel viral infections of zoonotic origin and mutations of existing human pathogenic viruses represent a serious concern for public health. It warrants the establishment of better interventions and protective therapies to combat the virus and prevent its spread. Surface glycoproteins catalyzing the fusion of viral particles and host cells have proven to be an excellent target for antivirals as well as vaccines. This review focuses on recent advances for computational structure-based design of antivirals and vaccines targeting viral fusion machinery to control seasonal and emerging respiratory viruses.

COVID-19: Rational discovery of the therapeutic potential of Melatonin as a SARS-CoV-2 main Protease Inhibitor.

The SARS-CoV-2 spread quickly across the globe. The World Health Organization (WHO) on March 11 declared COVID-19 a pandemic. The mortality rate, hospital disorders and incalculable economic and social damages, besides the unproven efficacy of the treatments evaluated against COVID-19, raised the need for immediate control of this disease. Therefore, the current study employed in silico tools to rationally identify new possible SARS-CoV-2 main protease (Mpro) inhibitors. That is an enzyme conserved among the coronavirus species; hence, the identification of an Mpro inhibitor is to make it a broad-spectrum drug. Molecular docking studies described the binding sites and the interaction energies of 74 Mpro-ligand complexes deposited in the Protein Data Bank (PDB). A structural similarity screening was carried out in order to identify possible Mpro ligands that show additional pharmacological properties against COVID-19. We identified 59 hit compounds and among them, melatonin stood out due to its prominent immunomodulatory and anti-inflammatory activities; it can reduce oxidative stress, defence cell mobility and efficiently combat the cytokine storm and sepsis. In addition, melatonin is an inhibitor of calmodulin, an essential intracellular component to maintain angiotensin-converting enzyme 2 (ACE-2) on the cell surface. Interestingly, one of the most promising hits in our docking study was melatonin. It revealed better interaction energy with Mpro compared to ligands in complexes from PDB. Consequently, melatonin can have response potential in early stages for its possible effects on ACE-2 and Mpro, although it is also promising in more severe stages of the disease for its action against hyper-inflammation. These results definitely do not confirm antiviral activity, but can rather be used as a basis for further preclinical and clinical trials.