Inhibition of ACE2-S Protein Interaction by a Short Functional Peptide with a Boomerang Structure.

Considering the high evolutionary rate and great harmfulness of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is imperative to develop new pharmacological antagonists. Human angiotensin-converting enzyme-2 (ACE2) functions as a primary receptor for the spike protein (S protein) of SARS-CoV-2. Thus, a novel functional peptide, KYPAY (K5), with a boomerang structure, was developed to inhibit the interaction between ACE2 and the S protein by attaching to the ACE2 ligand-binding domain (LBD). The inhibition property of K5 was evaluated via molecular simulations, cell experiments, and adsorption kinetics analysis. The molecular simulations showed that K5 had a high affinity for ACE2 but a low affinity for the cell membrane. The umbrella sampling (US) simulations revealed a significant enhancement in the binding potential of this functional peptide to ACE2. The fluorescence microscopy and cytotoxicity experiments showed that K5 effectively prevented the interaction between ACE2 and the S protein without causing any noticeable harm to cells. Further flow cytometry research indicated that K5 successfully hindered the interaction between ACE2 and the S protein, resulting in 78% inhibition at a concentration of 100 µM. This work offers an innovative perspective on the development of functional peptides for the prevention and therapy of SARS-CoV-2.

Structure and function of the SIT1 proline transporter in complex with the COVID-19 receptor ACE2.

Proline is widely known as the only proteogenic amino acid with a secondary amine. In addition to its crucial role in protein structure, the secondary amino acid modulates neurotransmission and regulates the kinetics of signaling proteins. To understand the structural basis of proline import, we solved the structure of the proline transporter SIT1 in complex with the COVID-19 viral receptor ACE2 by cryo-electron microscopy. The structure of pipecolate-bound SIT1 reveals the specific sequence requirements for proline transport in the SLC6 family and how this protein excludes amino acids with extended side chains. By comparing apo and substrate-bound SIT1 states, we also identify the structural changes that link substrate release and opening of the cytoplasmic gate and provide an explanation for how a missense mutation in the transporter causes iminoglycinuria.

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

Mutations in the Receptor Binding Domain of Severe Acute Respiratory Coronavirus-2 Omicron Variant Spike Protein Significantly Stabilizes Its Conformation.

The Omicron variant and its sub-lineages are the only current circulating SARS-CoV-2 viruses worldwide. In this study, the conformational stability of the isolated Receptor Binding Domain (RBD) of Omicron's spike protein is examined in detail. The parent Omicron lineage has over ten mutations in the ACE2 binding region of the RBD that are specifically associated with its ß hairpin loop domain. It is demonstrated through biophysical molecular computations that the mutations in the ß hairpin loop domain significantly increase the intra-protein interaction energies of intra-loop and loop-RBD interactions. The interaction energy increases include the formation of new hydrogen bonds in the ß hairpin loop domain that help stabilize this critical ACE2 binding region. Our results also agree with recent experiments on the stability of Omicron's core ß barrel domain, outside of its loop domain, and help demonstrate the overall conformational stability of the Omicron RBD. It is further shown here through dynamic simulations that the unbound state of the Omicron RBD remains closely aligned with the bound state configuration, which was not observed for the wild-type RBD. Overall, these studies demonstrate the significantly increased conformational stability of Omicron over its wild-type configuration and raise a number of questions on whether conformational stability could be a positive selection feature of SARS-CoV-2 viral mutational changes.

Non-Glycosylated SARS-CoV-2 Omicron BA.5 Receptor Binding Domain (RBD) with a Native-like Conformation Induces a Robust Immune Response with Potent Neutralization in a Mouse Model.

The Omicron BA.5 variant of SARS-CoV-2 is known for its high transmissibility and its capacity to evade immunity provided by vaccine protection against the (original) Wuhan strain. In our prior research, we successfully produced the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein in an E. coli expression system. Extensive biophysical characterization indicated that, even without glycosylation, the RBD maintained native-like conformational and biophysical properties. The current study explores the immunogenicity and neutralization capacity of the E. coli-expressed Omicron BA.5 RBD using a mouse model. Administration of three doses of the RBD without any adjuvant elicited high titer antisera of up to 7.3 × 105 and up to 1.6 × 106 after a booster shot. Immunization with RBD notably enhanced the population of CD44+CD62L+ T cells, indicating the generation of T cell memory. The in vitro assays demonstrated the antisera's protective efficacy through significant inhibition of the interaction between SARS-CoV-2 and its human receptor, ACE2, and through potent neutralization of a pseudovirus. These findings underscore the potential of our E. coli-expressed RBD as a viable vaccine candidate against the Omicron variant of SARS-CoV-2.

Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations.

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 µM, 6.7 µM, and 7.6 µM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 µM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 µM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms.

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

In Vitro and in Silico Study of New Biscoumarin Glycosides from Paramignya trimera against Angiotensin-Converting Enzyme 2 (ACE-2) for Preventing SARS-CoV-2 Infection.

In Vietnam, the stems and roots of the Rutaceous plant Paramignya trimera (Oliv.) Burkill (known locally as "Xáo tam phân") are widely used to treat liver diseases such as viral hepatitis and acute and chronic cirrhosis. In an effort to search for Vietnamese natural compounds capable of inhibiting coronavirus based on molecular docking screening, two new dimeric coumarin glycosides, namely cis-paratrimerin B (1) and cis-paratrimerin A (2), and two previously identified coumarins, the trans-isomers paratrimerin B (3) and paratrimerin A (4), were isolated from the roots of P. trimera and tested for their anti-angiotensin-converting enzyme 2 (ACE-2) inhibitory properties in vitro. It was discovered that ACE-2 enzyme was inhibited by cis-paratrimerin B (1), cis-paratrimerin A (2), and trans-paratrimerin B (3), with IC50 values of 28.9, 68, and 77 µM, respectively. Docking simulations revealed that four biscoumarin glycosides had good binding energies (∆G values ranging from -10.6 to -14.7 kcal/mol) and mostly bound to the S1' subsite of the ACE-2 protein. The key interactions of these natural ligands include metal chelation with zinc ions and multiple H-bonds with Ser128, Glu145, His345, Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots occur naturally in both cis- and trans-diastereomeric forms. The biscoumarin glycosides Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots hold potential for further studies as natural ACE-2 inhibitors for preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants.

Human infection with the coronavirus disease 2019 (COVID-19) is mediated by the binding of the spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the human angiotensin-converting enzyme 2 (ACE2). The frequent mutations in the receptor-binding domain (RBD) of the spike protein induced the emergence of variants with increased contagion and can hinder vaccine efficiency. Hence, it is crucial to better understand the binding mechanisms of variant RBDs to human ACE2 and develop efficient methods to characterize this interaction. In this work, we present an approach that uses machine learning to analyze the molecular dynamics simulations of RBD variant trajectories bound to ACE2. Along with the binding free energy calculation, this method was used to characterize the major differences in ACE2-binding capacity of three SARS-CoV-2 RBD variants-namely the original Wuhan strain, Omicron BA.1, and the more recent Omicron BA.5 sublineages. Our analyses assessed the differences in binding free energy and shed light on how it affects the infectious rates of different variants. Furthermore, this approach successfully characterized key binding interactions and could be deployed as an efficient tool to predict different binding inhibitors to pave the way for new preventive and therapeutic strategies.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

Untangling the Evolution of the Receptor-Binding Motif of SARS-CoV-2.

The spike protein determines the host-range specificity of coronaviruses. In particular, the Receptor-Binding Motif in the spike protein from SARS-CoV-2 contains the amino acids involved in molecular recognition of the host Angiotensin Converting Enzyme 2. Therefore, to understand how SARS-CoV-2 acquired its capacity to infect humans it is necessary to reconstruct the evolution of this important motif. Early during the pandemic, it was proposed that the SARS-CoV-2 Receptor-Binding Domain was acquired via recombination with a pangolin infecting coronavirus. This proposal was challenged by an alternative explanation that suggested that the Receptor-Binding Domain from SARS-CoV-2 did not originated via recombination with a coronavirus from a pangolin. Instead, this alternative hypothesis proposed that the Receptor-Binding Motif from the bat coronavirus RaTG13, was acquired via recombination with an unidentified coronavirus. And as a consequence of this event, the Receptor-Binding Domain from the pangolin coronavirus appeared as phylogenetically closer to SARS-CoV-2. Recently, the genomes from coronaviruses from Cambodia (bat_RShST182/200) and Laos (BANAL-20-52/103/247) which are closely related to SARS-CoV-2 were reported. However, no detailed analysis of the evolution of the Receptor-Binding Motif from these coronaviruses was reported. Here we revisit the evolution of the Receptor-Binding Domain and Motif in the light of the novel coronavirus genome sequences. Specifically, we wanted to test whether the above coronaviruses from Cambodia and Laos were the source of the Receptor-Binding Domain from RaTG13. We found that the Receptor-Binding Motif from these coronaviruses is phylogenetically closer to SARS-CoV-2 than to RaTG13. Therefore, the source of the Receptor-Binding Domain from RaTG13 is still unidentified. In accordance with previous studies, our results are consistent with the hypothesis that the Receptor-Binding Motif from SARS-CoV-2 evolved by vertical inheritance from a bat-infecting population of coronaviruses.

Pseudotyped virus infection of multiplexed ACE2 libraries reveals SARS-CoV-2 variant shifts in receptor usage.

Pairwise compatibility between virus and host proteins can dictate the outcome of infection. During transmission, both inter- and intraspecies variabilities in receptor protein sequences can impact cell susceptibility. Many viruses possess mutable viral entry proteins and the patterns of host compatibility can shift as the viral protein sequence changes. This combinatorial sequence space between virus and host is poorly understood, as traditional experimental approaches lack the throughput to simultaneously test all possible combinations of protein sequences. Here, we created a pseudotyped virus infection assay where a multiplexed target-cell library of host receptor variants can be assayed simultaneously using a DNA barcode sequencing readout. We applied this assay to test a panel of 30 ACE2 orthologs or human sequence mutants for infectability by the original SARS-CoV-2 spike protein or the Alpha, Beta, Gamma, Delta, and Omicron BA1 variant spikes. We compared these results to an analysis of the structural shifts that occurred for each variant spike's interface with human ACE2. Mutated residues were directly involved in the largest shifts, although there were also widespread indirect effects altering interface structure. The N501Y substitution in spike conferred a large structural shift for interaction with ACE2, which was partially recreated by indirect distal substitutions in Delta, which does not harbor N501Y. The structural shifts from N501Y greatly influenced the set of animal orthologs the variant spike was capable of interacting with. Out of the thirteen non-human orthologs, ten exhibited unique patterns of variant-specific compatibility, demonstrating that spike sequence changes during human transmission can toggle ACE2 compatibility and potential susceptibility of other animal species, and cumulatively increase overall compatibilities as new variants emerge. These experiments provide a blueprint for similar large-scale assessments of protein compatibility during entry by diverse viruses. This dataset demonstrates the complex compatibility relationships that occur between variable interacting host and virus proteins.

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.

Biophysical principles predict fitness of SARS-CoV-2 variants.

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity.

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

A structure-function analysis shows SARS-CoV-2 BA.2.86 balances antibody escape and ACE2 affinity.

BA.2.86, a recently described sublineage of SARS-CoV-2 Omicron, contains many mutations in the spike gene. It appears to have originated from BA.2 and is distinct from the XBB variants responsible for many infections in 2023. The global spread and plethora of mutations in BA.2.86 has caused concern that it may possess greater immune-evasive potential, leading to a new wave of infection. Here, we examine the ability of BA.2.86 to evade the antibody response to infection using a panel of vaccinated or naturally infected sera and find that it shows marginally less immune evasion than XBB.1.5. We locate BA.2.86 in the antigenic landscape of recent variants and look at its ability to escape panels of potent monoclonal antibodies generated against contemporary SARS-CoV-2 infections. We demonstrate, and provide a structural explanation for, increased affinity of BA.2.86 to ACE2, which may increase transmissibility.

Two Novel Angiotensin-Converting Enzyme (ACE) Inhibitory and ACE2 Upregulating Peptides from the Hydrolysate of Pumpkin (Cucurbita moschata) Seed Meal.

Pumpkin (Cucurbita moschata) seed meal (PSM), the major byproduct of pumpkin seed oil industry, was used to prepare angiotensin-converting enzyme (ACE) inhibitory and angiotensin-converting enzyme 2 (ACE2) upregulating peptides. These peptides were isolated and purified from the PSM hydrolysate prepared using Neutrase 5.0 BG by ultrafiltration, Sephadex G-15 column chromatography, and reversed-phase high-performance liquid chromatography. Two peptides with significant ACE inhibition activity were identified as SNHANQLDFHP and PVQVLASAYR with IC50 values of 172.07 and 90.69 µM, respectively. The C-terminal tripeptides of the two peptides contained Pro, Phe, and Tyr, respectively, and PVQVLASAYR also had Val in its N-terminal tripeptide, which was a favorable structure for ACE inhibition. Molecular docking results declared that the two peptides could interact with ACE through hydrogen bonds and hydrophobic interactions. Furthermore, the two peptides performed protective function on EA.hy926 cells by decreasing the secretion of endothelin-1, increasing the release of nitric oxide, and regulating the ACE2 activity. In vitro simulated gastrointestinal digestion showed the two peptides exhibited good stability against gastrointestinal enzyme digestion. In conclusion, PSM is a promising material for preparing antihypertensive peptides.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics.

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Analysis the molecular similarity of least common amino acid sites in ACE2 receptor to predict the potential susceptible species for SARS-CoV-2.

SARS-CoV-2 infections in animals have been reported globally. However, the understanding of the complete spectrum of animals susceptible to SARS-CoV-2 remains limited. The virus's dynamic nature and its potential to infect a wide range of animals are crucial considerations for a One Health approach that integrates both human and animal health. This study introduces a bioinformatic approach to predict potential susceptibility to SARS-CoV-2 in both domestic and wild animals. By examining genomic sequencing, we establish phylogenetic relationships between the virus and its potential hosts. We focus on the interaction between the SARS-CoV-2 genome sequence and specific regions of the host species' ACE2 receptor. We analyzed and compared ACE2 receptor sequences from 29 species known to be infected, selecting 10 least common amino acid sites (LCAS) from key binding domains based on similarity patterns. Our analysis included 49 species across primates, carnivores, rodents, and artiodactyls, revealing complete consistency in the LCAS and identifying them as potentially susceptible. We employed the LCAS similarity pattern to predict the likelihood of SARS-CoV-2 infection in unexamined species. This method serves as a valuable screening tool for assessing infection risks in domestic and wild animals, aiding in the prevention of disease outbreaks.