Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

Deep mutational scanning of the RNase III-like domain in Trypanosoma brucei RNA editing protein KREPB4

Kinetoplastid pathogens including Trypanosoma brucei, T. cruzi, and Leishmania species, are early diverged, eukaryotic, unicellular parasites. Functional understanding of many proteins from these pathogens has been hampered by limited sequence homology to proteins from other model organisms. Here we describe the development of a high-throughput deep mutational scanning approach in T. brucei that facilitates rapid and unbiased assessment of the impacts of many possible amino acid substitutions within a protein on cell fitness, as measured by relative cell growth. The approach leverages several molecular technologies: cells with conditional expression of a wild-type gene of interest and constitutive expression of a library of mutant variants, degron-controlled stabilization of I-SceI meganuclease to mediate highly efficient transfection of a mutant allele library, and a high-throughput sequencing readout for cell growth upon conditional knockdown of wild-type gene expression and exclusive expression of mutant variants. Using this method, we queried the effects of amino acid substitutions in the apparently non-catalytic RNase III-like domain of KREPB4 (B4), which is an essential component of the RNA Editing Catalytic Complexes (RECCs) that carry out mitochondrial RNA editing in T. brucei. We measured the impacts of thousands of B4 variants on bloodstream form cell growth and validated the most deleterious variants containing single amino acid substitutions. Crucially, there was no correlation between phenotypes and amino acid conservation, demonstrating the greater power of this method over traditional sequence homology searching to identify functional residues. The bloodstream form cell growth phenotypes were combined with structural modeling, RECC protein proximity data, and analysis of selected substitutions in procyclic form T. brucei. These analyses revealed that the B4 RNaseIII-like domain is essential for maintenance of RECC integrity and RECC protein abundances and is also involved in changes in RECCs that occur between bloodstream and procyclic form life cycle stages.

Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations 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 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 dynamic 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 molecular dynamics 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 molecular dynamics 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.

Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

Sequence signatures within the genome of SARS-CoV-2 can be used to predict host source

We conducted an in silico analysis to better understand the potential factors impacting host adaptation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer, humans, and mink due to the strong evidence of sustained transmission within these hosts. Classification models trained on single nucleotide and amino acid differences between samples effectively identified white-tailed deer-, human-, and mink-derived SARS-CoV-2. For example, the balanced accuracy score of Extremely Randomized Trees classifiers was 0.984 ± 0.006. Eighty-eight commonly identified predictive mutations are found at sites under strong positive and negative selective pressure. A large fraction of sites under selection (86.9%) or identified by machine learning (87.1%) are found in genes other than the spike. Some locations encoded by these gene regions are predicted to be B- and T-cell epitopes or are implicated in modulating the immune response suggesting that host adaptation may involve the evasion of the host immune system, modulation of the class-I major-histocompatibility complex, and the diminished recognition of immune epitopes by CD8+ T cells. Our selection and machine learning analysis also identified that silent mutations, such as C7303T and C9430T, play an important role in discriminating deer-derived samples across multiple clades. Finally, our investigation into the origin of the B.1.641 lineage from white-tailed deer in Canada discovered an additional human sequence from Michigan related to the B.1.641 lineage sampled near the emergence of this lineage. These findings demonstrate that machine-learning approaches can be used in combination with evolutionary genomics to identify factors possibly involved in the cross-species transmission of viruses and the emergence of novel viral lineages.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus capable of infecting and establishing itself in human and wildlife populations, such as white-tailed deer. This fact highlights the importance of developing novel ways to identify genetic factors that contribute to its spread and adaptation to new host species. This is especially important since these populations can serve as reservoirs that potentially facilitate the re-introduction of new variants into human populations. In this study, we apply machine learning and phylogenetic methods to uncover biomarkers of SARS-CoV-2 adaptation in mink and white-tailed deer. We find evidence demonstrating that both non-synonymous and silent mutations can be used to differentiate animal-derived sequences from human-derived ones and each other. This evidence also suggests that host adaptation involves the evasion of the immune system and the suppression of antigen presentation. Finally, the methods developed here are general and can be used to investigate host adaptation in viruses other than SARS-CoV-2.

A Comparative Analysis of Innate Immune Responses and the Structural Characterization of Spike from SARS-CoV-2 Gamma Variants and Subvariants

The SARS-CoV-2 P.1 variant, responsible for an outbreak in Manaus, Brazil, is distinguished by 12 amino acid differences in the S protein, potentially increasing its ACE-2 affinity and immune evasion capability. We investigated the innate immune response of this variant compared to the original B.1 strain, particularly concerning cytokine production. Blood samples from three severe COVID-19 patients were analyzed post-infection with both strains. Results showed no significant difference in cytokine production of mononuclear cells and neutrophils for either variant. While B.1 had higher cytopathogenicity, neither showed viral replication in mononuclear cells. Structural analyses of the S protein highlighted physicochemical variations, which might be linked to the differences in infectivity between the strains. Our studies point to the increased infectivity of P.1 could stem from altered immunogenicity and receptor-binding affinity.

What makes SARS-CoV-2 unique? Focusing on the spike protein

Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) seriously threatens public health and safety. Genetic variants determine the expression of SARS-CoV-2 structural proteins, which are associated with enhanced transmissibility, enhanced virulence, and immune escape. Vaccination is encouraged as a public health intervention, and different types of vaccines are used worldwide. However, new variants continue to emerge, especially the Omicron complex, and the neutralizing antibody responses are diminished significantly. In this review, we outlined the uniqueness of SARS-CoV-2 from three perspectives. First, we described the detailed structure of the spike (S) protein, which is highly susceptible to mutations and contributes to the distinct infection cycle of the virus. Second, we systematically summarized the immunoglobulin G epitopes of SARS-CoV-2 and highlighted the central role of the nonconserved regions of the S protein in adaptive immune escape. Third, we provided an overview of the vaccines targeting the S protein and discussed the impact of the nonconserved regions on vaccine effectiveness. The characterization and identification of the structure and genomic organization of SARS-CoV-2 will help elucidate its mechanisms of viral mutation and infection and provide a basis for the selection of optimal treatments. The leaps in advancements regarding improved diagnosis, targeted vaccines and therapeutic remedies provide sound evidence showing that scientific understanding, research, and technology evolved at the pace of the pandemic.

COVID-19: From emerging variants to vaccination

The vigorous spread of SARS-CoV-2 resulted in the rapid infection of millions of people worldwide and devastation of not only public healthcare, but also social, educational, and economic infrastructures. The evolution of SARS-CoV-2 over time is due to the mutations that occurred in the genome during each replication. These mutated forms of SARS-CoV-2, otherwise known as variants, were categorized as variants of interest (VOI) or variants of concern (VOC) based on the increased risk of transmissibility, disease severity, immune escape, decreased effectiveness of current social measures, and available vaccines and therapeutics. The swift development of COVID-19 vaccines has been a great success for biomedical research, and billions of vaccine doses, including boosters, have been administered worldwide. BNT162b2 vaccine (Pfizer-BioNTech), mRNA-1273 (Moderna), ChAdOx1 nCoV-19 (AstraZeneca), and Janssen (Johnson & Johnson) are the four major COVID-19 vaccines that received early regulatory authorization based on their efficacy. However, some SARS-CoV-2 variants resulted in higher resistance to available vaccines or treatments. It has been four years since the first reported infection of SARS-CoV-2, yet the Omicron variant and its subvariants are still infecting people worldwide. Despite this, COVID-19 vaccines are still expected to be effective at preventing severe disease, hospitalization, and death from COVID. In this review, we provide a comprehensive overview of the COVID-19 pandemic focused on evolution of VOC and vaccination strategies against them.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Broadly neutralizing antibodies against emerging delta-coronaviruses

Porcine deltacoronavirus (PDCoV) spillovers were recently detected in children with acute undifferentiated febrile illness, underscoring recurrent zoonoses of divergent coronaviruses. To date, no vaccines or specific therapeutics are approved for use in humans against PDCoV. To prepare for possible future PDCoV epidemics, we isolated human spike (S)-directed monoclonal antibodies from transgenic mice and found that two of them, designated PD33 and PD41, broadly neutralized a panel of PDCoV variants. Cryo-electron microscopy structures of PD33 and PD41 in complex with the PDCoV receptor-binding domain and S ectodomain trimer provide a blueprint of the epitopes recognized by these mAbs, rationalizing their broad inhibitory activity. We show that both mAbs inhibit PDCoV by competitively interfering with host APN binding to the PDCoV receptor-binding loops, explaining the mechanism of viral neutralization. PD33 and PD41 are candidates for clinical advancement, which could be stockpiled to prepare for possible future PDCoV outbreaks.

PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information

The dynamic evolution of the severe acute respiratory syndrome coronavirus 2 virus is primarily driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. This study introduces a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIEST's proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step in utilizing deep-learning methodologies for anticipatory viral mutation analysis and pandemic response.

Genotypic-phenotypic landscape computation based on first principle and deep learning

The relationship between genotype and fitness is fundamental to evolution, but quantitatively mapping genotypes to fitness has remained challenging. We propose the Phenotypic-Embedding theorem (P-E theorem) that bridges genotype-phenotype through an encoder-decoder deep learning framework. Inspired by this, we proposed a more general first principle for correlating genotype-phenotype, and the P-E theorem provides a computable basis for the application of first principle. As an application example of the P-E theorem, we developed the Co-attention based Transformer model to bridge Genotype and Fitness model, a Transformer-based pre-train foundation model with downstream supervised fine-tuning that can accurately simulate the neutral evolution of viruses and predict immune escape mutations. Accordingly, following the calculation path of the P-E theorem, we accurately obtained the basic reproduction number (${R}_0$) of SARS-CoV-2 from first principles, quantitatively linked immune escape to viral fitness and plotted the genotype-fitness landscape. The theoretical system we established provides a general and interpretable method to construct genotype-phenotype landscapes, providing a new paradigm for studying theoretical and computational biology.

CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses

Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.

Antigenic drift expands viral escape pathways from imprinted host humoral immunity

An initial virus exposure can imprint antibodies such that future responses to antigenically drifted strains are dependent on the identity of the imprinting strain. Subsequent exposure to antigenically distinct strains followed by affinity maturation can guide immune responses toward generation of cross-reactive antibodies. How viruses evolve in turn to escape these imprinted broad antibody responses is unclear. Here, we used clonal antibody lineages from two human donors recognizing conserved influenza virus hemagglutinin (HA) epitopes to assess viral escape potential using deep mutational scanning. We show that even though antibody affinity maturation does restrict the number of potential escape routes in the imprinting strain through repositioning the antibody variable domains, escape is still readily observed in drifted strains and attributed to epistatic networks within HA. These data explain how influenza virus continues to evolve in the human population by escaping even broad antibody responses.

Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning

Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of -110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (-101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the "ML-Guided Design Correctly Predicts Combinatorial Effects Strategy" compared to the "ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy". To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development.

Soluble ACE2 correlates with severe COVID-19 and can impair antibody responses

Identifying immune modulators that impact neutralizing antibody responses against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is of great relevance. We postulated that high serum concentrations of soluble angiotensin-converting enzyme 2 (sACE2) might mask the spike and interfere with antibody maturation toward the SARS-CoV-2-receptor-binding motif (RBM). We tested 717 longitudinal samples from 295 COVID-19 patients and showed a 2- to 10-fold increase of enzymatically active sACE2 (a-sACE2), with up to 1 μg/mL total sACE2 in moderate and severe patients. Fifty percent of COVID-19 sera inhibited ACE2 activity, in contrast to 1.3% of healthy donors and 4% of non-COVID-19 pneumonia patients. A mild inverse correlation of a-sACE2 with RBM-directed serum antibodies was observed. In silico, we show that sACE2 concentrations measured in COVID-19 sera can disrupt germinal center formation and inhibit timely production of high-affinity antibodies. We suggest that sACE2 is a biomarker for COVID-19 and that soluble receptors may contribute to immune suppression informing vaccine design.

AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.

Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features

In the ENSEMBLE randomized, placebo-controlled phase 3 trial (NCT04505722), estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were determined from 484 vaccine and 1,067 placebo recipients who acquired COVID-19. In this set of prespecified analyses, we show that in Latin America, VE was significantly lower against Lambda vs. Reference and against Lambda vs. non-Lambda [family-wise error rate (FWER) p < 0.05]. VE differed by residue match vs. mismatch to the vaccine-insert at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20); significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 antibody-epitope escape scores and 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccinee sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against the most distant viruses.

Rapid Degradation of the Human ACE2 Receptor Upon Binding and Internalization of SARS-Cov-2-Spike-RBD Protein

It is widely accepted that the SARS-CoV-2 betacoronavirus infects humans through binding the human Angiotensin Receptor 2 (ACE2) that lines the nasal cavity and lungs, followed by import into a cell utilizing the Transmembrane Protease, Serine 2 (TMPRSS2) cofactor. ACE2 binding is mediated by an approximately 200-residue portion of the SARS-CoV-2 extracellular spike protein, the receptor binding domain (RBD). Robust interactions are shown using a novel cell-based assay between an RBD membrane tethered-GFP fusion protein and the membrane bound ACE2-Cherry fusion protein. Several observations were not predicted including, quick and sustained interactions leading to internalization of RBD fusion protein into the ACE2 cells and rapid downregulation of the ACE2-Cherry fluorescence. Targeted mutation in the RBD disulfide Loop 4 led to a loss of internalization for several variants tested. However, a secreted RBD did not cause ACE2 downregulation of ACE2-Cherry fluorescence. Thus, the membrane associated form of RBD found on the viral coat may have long-term system wide consequences on ACE2 expressing cells.

Effects of genetic variation on the structure of RNA and protein

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

Spike-Seq: An amplicon-based high-throughput sequencing approach for the sensitive detection and characterization of SARS-CoV-2 genetic variations in environmental samples

Ever since the outbreak of COVID-19 disease in Wuhan, China, different variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified. Wastewater-based epidemiology (WBE), an approach that has been successfully applied in numerous case studies worldwide, offers a cost-effective and rapid way for monitoring trends of SARS-Cov-2 in the community level without selection bias. Despite being a gold-standard procedure, WBE is a challenging approach due to the sample instability and the moderate efficiency of SARS-CoV-2 concentration in wastewater. In the present study, we introduce Spike-Seq, a custom amplicon-based approach for the S gene sequencing of SARS-CoV-2 in wastewater samples, which enables not only the accurate identification of the existing Spike-related genetic markers, but also the estimation of their frequency in the investigated samples. The implementation of Spike-Seq involves the combination of nested PCR-based assays that efficiently amplify the entire nucleotide sequence of the S gene and next-generation sequencing, which enables the variant detection and the estimation of their frequency. In the framework of the current work, Spike-Seq was performed to investigate the mutational profile of SARS-CoV-2 in samples from the Wastewater Treatment Plant (WWTP) of Athens, Greece, which originated from multiple timepoints, ranging from March 2021 until July 2022. Our findings demonstrate that Spike-Seq efficiently detected major genetic markers of B.1.1.7 (Alpha), B.1.617.2 (Delta) as well as B.1.1.529 (Omicron) variants in wastewater samples and provided their frequency levels, showing similar variant distributions with the published clinical data from the National Public Health organization. The presented approach can prove to be a useful tool for the detection of SARS-CoV-2 in challenging wastewater samples and the identification of the existing genetic variants of S gene.

Single-molecule force stability of the SARS-CoV-2-ACE2 interface in variants-of-concern

Mutations in SARS-CoV-2 have shown effective evasion of population immunity and increased affinity to the cellular receptor angiotensin-converting enzyme 2 (ACE2). However, in the dynamic environment of the respiratory tract, forces act on the binding partners, which raises the question of whether not only affinity but also force stability of the SARS-CoV-2-ACE2 interaction might be a selection factor for mutations. Using magnetic tweezers, we investigate the impact of amino acid substitutions in variants of concern (Alpha, Beta, Gamma and Delta) and on force-stability and bond kinetic of the receptor-binding domain-ACE2 interface at a single-molecule resolution. We find a higher affinity for all of the variants of concern (>fivefold) compared with the wild type. In contrast, Alpha is the only variant of concern that shows higher force stability (by 17%) compared with the wild type. Using molecular dynamics simulations, we rationalize the mechanistic molecular origins of this increase in force stability. Our study emphasizes the diversity of contributions to the transmissibility of variants and establishes force stability as one of the several factors for fitness. Understanding fitness advantages opens the possibility for the prediction of probable mutations, allowing a rapid adjustment of therapeutics, vaccines and intervention measures.

Molecular insights into the adaptive evolution of SARS-CoV-2 spike protein

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially damaged the global economy and human health. The spike (S) protein of coronaviruses plays a pivotal role in viral entry by binding to host cell receptors. Additionally, it acts as the primary target for neutralizing antibodies in those infected and is the central focus for currently utilized or researched vaccines. During the virus's adaptation to the human host, the S protein of SARS-CoV-2 has undergone significant evolution. As the COVID-19 pandemic has unfolded, new mutations have arisen and vanished, giving rise to distinctive amino acid profiles within variant of concern strains of SARS-CoV-2. Notably, many of these changes in the S protein have been positively selected, leading to substantial alterations in viral characteristics, such as heightened transmissibility and immune evasion capabilities. This review aims to provide an overview of our current understanding of the structural implications associated with key amino acid changes in the S protein of SARS-CoV-2. These research findings shed light on the intricate and dynamic nature of viral evolution, underscoring the importance of continuous monitoring and analysis of viral genomes. Through these molecular-level investigations, we can attain deeper insights into the virus's adaptive evolution, offering valuable guidance for designing vaccines and developing antiviral drugs to combat the ever-evolving viral threats.

High-throughput screening of genetic and cellular drivers of syncytium formation induced by the spike protein of SARS-CoV-2

Mapping mutations and discovering cellular determinants that cause the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to induce infected cells to form syncytia would facilitate the development of strategies for blocking the formation of such cell-cell fusion. Here we describe high-throughput screening methods based on droplet microfluidics and the size-exclusion selection of syncytia, coupled with large-scale mutagenesis and genome-wide knockout screening via clustered regularly interspaced short palindromic repeats (CRISPR), for the large-scale identification of determinants of cell-cell fusion. We used the methods to perform deep mutational scans in spike-presenting cells to pinpoint mutable syncytium-enhancing substitutions in two regions of the spike protein (the fusion peptide proximal region and the furin-cleavage site). We also used a genome-wide CRISPR screen in cells expressing the receptor angiotensin-converting enzyme 2 to identify inhibitors of clathrin-mediated endocytosis that impede syncytium formation, which we validated in hamsters infected with SARS-CoV-2. Finding genetic and cellular determinants of the formation of syncytia may reveal insights into the physiological and pathological consequences of cell-cell fusion.

Genetic characteristics of SARS-CoV-2 virus variants observed upon three waves of the COVID-19 pandemic in Ukraine between February 2021-January 2022

The aim

of our study was to identify and characterize the SARS-CoV-2 variants in COVID-19 patients' samples collected from different regions of Ukraine to determine the relationship between SARS-CoV-2 phylogenetics and COVID-19 epidemiology.

Patients and methods

Samples were collected from COVID-19 patients during 2021 and the beginning of 2022 (401 patients). The SARS-CoV-2 genotyping was performed by parallel whole genome sequencing.

Results

The obtained SARS-CoV-2 genotypes showed that three waves of the COVID-19 pandemic in Ukraine were represented by three main variants of concern (VOC), named Alpha, Delta and Omicron; each VOC successfully replaced the earlier variant. The VOC Alpha strain was presented by one B.1.1.7 lineage, while VOC Delta showed a spectrum of 25 lineages that had different prevalence in 19 investigated regions of Ukraine. The VOC Omicron in the first half of the pandemic was represented by 13 lines that belonged to two different clades representing B.1 and B.2 Omicron strains. Each of the three epidemic waves (VOC Alpha, Delta, and Omicron) demonstrated their own course of disease, associated with genetic changes in the SARS-CoV-2 genome. The observed epidemiological features are associated with the genetic characteristics of the different VOCs, such as point mutations, deletions and insertions in the viral genome. A phylogenetic and transmission analysis showed the different mutation rates; there were multiple virus sources with a limited distribution between regions.

Conclusions

The evolution of SARS-CoV-2 virus and high levels of morbidity due to COVID-19 are still registered in the world. Observed multiple virus sourses with the limited distribution between regions indicates the high efficiency of the anti-epidemic policy pursued by the Ministry of Health of Ukraine to prevent the spread of the epidemic, despite the low level of vaccination of the Ukrainian population.

Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

Designed mosaic nanoparticles enhance cross-reactive immune responses in mice

1Using computational methods, we designed 60-mer nanoparticles displaying SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs) by (i) creating RBD sequences with 6 mutations in the SARS-COV-2 WA1 RBD that were predicted to retain proper folding and abrogate antibody responses to variable epitopes (mosaic-2COMs; mosaic-5COM), and (ii) selecting 7 natural sarbecovirus RBDs (mosaic-7COM). These antigens were compared with mosaic-8b, which elicits cross-reactive antibodies and protects from sarbecovirus challenges in animals. Immunizations in naïve and COVID-19 pre-vaccinated mice revealed that mosaic-7COM elicited higher binding and neutralization titers than mosaic-8b and related antigens. Deep mutational scanning showed that mosaic-7COM targeted conserved RBD epitopes. Mosaic-2COMs and mosaic-5COM elicited higher titers than homotypic SARS-CoV-2 Beta RBD-nanoparticles and increased potencies against some SARS-CoV-2 variants than mosaic-7COM. However, mosaic-7COM elicited more potent responses against zoonotic sarbecoviruses and highly mutated Omicrons. These results support using mosaic-7COM to protect against highly mutated SARS-CoV-2 variants and zoonotic sarbecoviruses with spillover potential.

Potential immune evasion of the severe acute respiratory syndrome coronavirus 2 Omicron variants

Coronavirus disease 2019 (COVID-19), which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic. The Omicron variant (B.1.1.529) was first discovered in November 2021 in specimens collected from Botswana, South Africa. Omicron has become the dominant variant worldwide, and several sublineages or subvariants have been identified recently. Compared to those of other mutants, the Omicron variant has the most highly expressed amino acid mutations, with almost 60 mutations throughout the genome, most of which are in the spike (S) protein, especially in the receptor-binding domain (RBD). These mutations increase the binding affinity of Omicron variants for the ACE2 receptor, and Omicron variants may also lead to immune escape. Despite causing milder symptoms, epidemiological evidence suggests that Omicron variants have exceptionally higher transmissibility, higher rates of reinfection and greater spread than the prototype strain as well as other preceding variants. Additionally, overwhelming amounts of data suggest that the levels of specific neutralization antibodies against Omicron variants decrease in most vaccinated populations, although CD4+ and CD8+ T-cell responses are maintained. Therefore, the mechanisms underlying Omicron variant evasion are still unclear. In this review, we surveyed the current epidemic status and potential immune escape mechanisms of Omicron variants. Especially, we focused on the potential roles of viral epitope mutations, antigenic drift, hybrid immunity, and "original antigenic sin" in mediating immune evasion. These insights might supply more valuable concise information for us to understand the spreading of Omicron variants.

The Diverse Nature of the Molecular Interactions That Govern the COV-2 Variants' Cell Receptor Affinity Ranking and Its Experimental Variability

A critical determinant of infectivity and virulence of the most infectious and or lethal variants of concern (VOCs): Wild Type, Delta and Omicron is related to the binding interactions between the receptor-binding domain of the spike and its host receptor, the initial step in cell infection. It is of the utmost importance to understand how mutations of a viral strain, especially those that are in the viral spike, affect the resulting infectivity of the emerging VOC, knowledge that could help us understand the variant virulence and inform the therapies applied or the vaccines developed. For this sake, we have applied a battery of computational protocols of increasing complexity to the calculation of the spike binding affinity for three variants of concern to the ACE2 cell receptor. The results clearly illustrate that the attachment of the spikes of the Delta and Omicron variants to the receptor originates through different molecular interaction mechanisms. All our protocols unanimously predict that the Delta variant has the highest receptor-binding affinity, while the Omicron variant displays a substantial variability in the binding affinity of the spike that relates to the structural plasticity of the Omicron spike-receptor complex. We suggest that the latter result could explain (at least in part) the variability of the in vitro binding results for this VOC and has led us to suggest a reason for the lower virulence of the Omicron variant as compared to earlier strains. Several hypotheses have been developed around this subject.

Many Roles of Carbohydrates: A Computational Spotlight on the Coronavirus S Protein Binding

Glycosylation is one of the post-translational modifications with more than 50% of human proteins being glycosylated. The exact nature and chemical composition of glycans are inaccessible to X-ray or cryo-electron microscopy imaging techniques. Therefore, computational modeling studies and molecular dynamics must be used as a "computational microscope". The spike (S) protein of SARS-CoV-2 is heavily glycosylated, and a few glycans play a more functional role "beyond shielding". In this mini-review, we discuss computational investigations of the roles of specific S-protein and ACE2 glycans in the overall ACE2-S protein binding. We highlight different functions of specific glycans demonstrated in myriad computational models and simulations in the context of the SARS-CoV-2 virus binding to the receptor. We also discuss interactions between glycocalyx and the S protein, which may be utilized to design prophylactic polysaccharide-based therapeutics targeting the S protein. In addition, we underline the recent emergence of coronavirus variants and their impact on the S protein and its glycans.

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

The simplicity of protein sequence-function relationships

How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a protein's function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a protein's entire genotype-phenotype map. This method yields a maximally efficient explanation of a protein's genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.

Deciphering the free energy landscapes of SARS-CoV-2 wild type and Omicron variant interacting with human ACE2

The binding of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein to the host cell receptor angiotensin-converting enzyme 2 (ACE2) is the first step in human viral infection. Therefore, understanding the mechanism of interaction between RBD and ACE2 at the molecular level is critical for the prevention of COVID-19, as more variants of concern, such as Omicron, appear. Recently, atomic force microscopy has been applied to characterize the free energy landscape of the RBD-ACE2 complex, including estimation of the distance between the transition state and the bound state, xu. Here, using a coarse-grained model and replica-exchange umbrella sampling, we studied the free energy landscape of both the wild type and Omicron subvariants BA.1 and XBB.1.5 interacting with ACE2. In agreement with experiment, we find that the wild type and Omicron subvariants have similar xu values, but Omicron binds ACE2 more strongly than the wild type, having a lower dissociation constant KD.

Individual ingredients of NP-101 (Thymoquinone formula) inhibit SARS-CoV-2 pseudovirus infection

Thymoquinone TQ, an active ingredient of Nigella Sativa, has been shown to inhibit COVID-19 symptoms in clinical trials. Thymoquinone Formulation (TQF or NP-101) is developed as a novel enteric-coated medication derivative from Nigella Sativa. TQF consists of TQ with a favorable concentration and fatty acids, including palmitic, oleic, and linoleic acids. In this study, we aimed to investigate the roles of individual ingredients of TQF on infection of SARS-CoV-2 variants in-vitro, by utilizing Murine Leukemia Virus (MLV) based pseudovirus particles. We demonstrated that NP-101, TQ, and other individual ingredients, including oleic, linoleic, and palmitic acids inhibited SARS-CoV-2 infection in the MLV-based pseudovirus model. A large, randomized phase 2 study of NP-101 is planned in outpatient COVID-19 patients.

Deep mutational scanning reveals functional constraints and antigenic variability of Lassa virus glycoprotein complex

Lassa virus is estimated to cause thousands of human deaths per year, primarily due to spillovers from its natural host, Mastomys rodents. Efforts to create vaccines and antibody therapeutics must account for the evolutionary variability of Lassa virus's glycoprotein complex (GPC), which mediates viral entry into cells and is the target of neutralizing antibodies. To map the evolutionary space accessible to GPC, we use pseudovirus deep mutational scanning to measure how nearly all GPC amino-acid mutations affect cell entry and antibody neutralization. Our experiments define functional constraints throughout GPC. We quantify how GPC mutations affect neutralization by a panel of monoclonal antibodies and show that all antibodies are escaped by mutations that exist among natural Lassa virus lineages. Overall, our work describes a biosafety-level-2 method to elucidate the mutational space accessible to GPC and shows how prospective characterization of antigenic variation could aid design of therapeutics and vaccines.

Learning the shape of protein microenvironments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

S-Gene Target Failure as an Effective Tool for Tracking the Emergence of Dominant SARS-CoV-2 Variants in Switzerland and Liechtenstein, Including Alpha, Delta, and Omicron BA.1, BA.2, and BA.4/BA.5

During the SARS-CoV-2 pandemic, the Dr. Risch medical group employed the multiplex TaqPathTM COVID-19 CE-IVD RT-PCR Kit for large-scale routine diagnostic testing in Switzerland and the principality of Liechtenstein. The TaqPath Kit is a widely used multiplex assay targeting three genes (i.e., ORF1AB, N, S). With emergence of the B.1.1.7 (Alpha) variant, a diagnostic flaw became apparent as the amplification of the S-gene target was absent in these samples due to a deletion (ΔH69/V70) in the Alpha variant genome. This S-gene target failure (SGTF) was the earliest indication of a new variant emerging and was also observed in subsequent variants such as Omicron BA.1 and BA4/BA.5. The Delta variant and Omicron BA.2 did not present with SGTF. From September 2020 to November 2022, we investigated the applicability of the SGTF as a surrogate marker for emerging variants such as B.1.1.7, B.1.617.2 (Delta), and Omicron BA.1, BA.2, and BA.4/BA.5 in samples with cycle threshold (Ct) values < 30. Next to true SGTF-positive and SGTF-negative samples, there were also samples presenting with delayed-type S-gene amplification (higher Ct value for S-gene than ORF1ab gene). Among these, a difference of 3.8 Ct values between the S- and ORF1ab genes was found to best distinguish between "true" SGTF and the cycle threshold variability of the assay. Samples above the cutoff were subsequently termed partial SGTF (pSGTF). Variant confirmation was performed by whole-genome sequencing (Oxford Nanopore Technology, Oxford, UK) or mutation-specific PCR (TIB MOLBIOL). In total, 17,724 (7.4%) samples among 240,896 positives were variant-confirmed, resulting in an overall sensitivity and specificity of 93.2% [92.7%, 93.7%] and 99.3% [99.2%, 99.5%], respectively. Sensitivity was increased to 98.2% [97.9% to 98.4%] and specificity lowered to 98.9% [98.6% to 99.1%] when samples with pSGTF were included. Furthermore, weekly logistic growth rates (α) and sigmoid's midpoint (t0) were calculated based on SGTF data and did not significantly differ from calculations based on comprehensive data from GISAID. The SGTF therefore allowed for a valid real-time estimate for the introduction of all dominant variants in Switzerland and Liechtenstein.

To be remembered: B cell memory response against SARS-CoV-2 and its variants in vaccinated and unvaccinated individuals

COVID-19 disease has plagued the world economy and affected the overall well-being and life of most of the people. Natural infection as well as vaccination leads to the development of an immune response against the pathogen. This involves the production of antibodies, which can neutralize the virus during future challenges. In addition, the development of cellular immune memory with memory B and T cells provides long-lasting protection. The longevity of the immune response has been a subject of intensive research in this field. The extent of immunity conferred by different forms of vaccination or natural infections remained debatable for long. Hence, understanding the effectiveness of these responses among different groups of people can assist government organizations in making informed policy decisions. In this article, based on the publicly available data, we have reviewed the memory response generated by some of the vaccines against SARS-CoV-2 and its variants, particularly B cell memory in different groups of individuals.

Inference of annealed protein fitness landscapes with AnnealDCA

The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody Rep-Seq data of immunized mice and screening experiments, assessing the quality of the fitness landscape reconstructions. Our method can be applied to several experimental cases where a population of protein variants undergoes various rounds of selection and sequencing, without relying on the computation of variants enrichment ratios, and thus can be used even in cases of disjoint sequence samples.

Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted

BACKGROUND

SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes.

METHODS

Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs.

FINDINGS

Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner.

INTERPRETATION

Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection.

FUNDING

Full list of funders is provided at the end of the manuscript.

Emergence of SARS and COVID-19 and preparedness for the next emerging disease X

Severe acute respiratory syndrome (SARS) and Coronavirus disease 2019 (COVID-19) are two human Coronavirus diseases emerging in this century, posing tremendous threats to public health and causing great loss to lives and economy. In this review, we retrospect the studies tracing the molecular evolution of SARS-CoV, and we sort out current research findings about the potential ancestor of SARS-CoV-2. Updated knowledge about SARS-CoV-2-like viruses found in wildlife, the animal susceptibility to SARS-CoV-2, as well as the interspecies transmission risk of SARS-related coronaviruses (SARSr-CoVs) are gathered here. Finally, we discuss the strategies of how to be prepared against future outbreaks of emerging or re-emerging coronaviruses.

Prevalence of persistent SARS-CoV-2 in a large community surveillance study

Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.

Challenges in Emerging Vaccines and Future Promising Candidates against SARS-CoV-2 Variants

Since the COVID-19 outbreak, the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus has evolved into variants with varied infectivity. Vaccines developed against COVID-19 infection have boosted immunity, but there is still uncertainty on how long the immunity from natural infection or vaccination will last. The present study attempts to outline the present level of information about the contagiousness and spread of SARS-CoV-2 variants of interest and variants of concern (VOCs). The keywords like COVID-19 vaccine types, VOCs, universal vaccines, bivalent, and other relevant terms were searched in NCBI, Science Direct, and WHO databases to review the published literature. The review provides an integrative discussion on the current state of knowledge on the type of vaccines developed against SARS-CoV-2, the safety and efficacy of COVID-19 vaccines concerning the VOCs, and prospects of novel universal, chimeric, and bivalent mRNA vaccines efficacy to fend off existing variants and other emerging coronaviruses. Genomic variation can be quite significant, as seen by the notable differences in impact, transmission rate, morbidity, and death during several human coronavirus outbreaks. Therefore, understanding the amount and characteristics of coronavirus genetic diversity in historical and contemporary strains can help researchers get an edge over upcoming variants.

Soluble Angiotensin-Converting Enzyme 2 Protein Improves Survival and Lowers Viral Titers in Lethal Mouse Model of Severe Acute Respiratory Syndrome Coronavirus Type 2 Infection with the Delta Variant

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) utilizes angiotensin-converting enzyme 2 (ACE2) as its main receptor for cell entry. We bioengineered a soluble ACE2 protein termed ACE2 618-DDC-ABD that has increased binding to SARS-CoV-2 and prolonged duration of action. Here, we investigated the protective effect of this protein when administered intranasally to k18-hACE2 mice infected with the aggressive SARS-CoV-2 Delta variant. k18-hACE2 mice were infected with the SARS-CoV-2 Delta variant by inoculation of a lethal dose (2 × 104 PFU). ACE2 618-DDC-ABD (10 mg/kg) or PBS was administered intranasally six hours prior and 24 and 48 h post-viral inoculation. All animals in the PBS control group succumbed to the disease on day seven post-infection (0% survival), whereas, in contrast, there was only one casualty in the group that received ACE2 618-DDC-ABD (90% survival). Mice in the ACE2 618-DDC-ABD group had minimal disease as assessed using a clinical score and stable weight, and both brain and lung viral titers were markedly reduced. These findings demonstrate the efficacy of a bioengineered soluble ACE2 decoy with an extended duration of action in protecting against the aggressive Delta SARS-CoV-2 variant. Together with previous work, these findings underline the universal protective potential against current and future emerging SARS-CoV-2 variants.

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 discovery 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 binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a 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.

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

Molecular Dynamics Simulations on Spike Protein Mutants Binding with Human β Defensin Type 2

Human β defensin type 2 (hBD-2), a cationic cysteine-rich peptide secreted from the human innate immune system, can bind Spike-RBD at the same site as receptor ACE2, thus blocking viral entry into ACE2-expressing cells. In order to find out the impact of CoV-2 mutations on hBD-2's antiviral activity, it is important to investigate the binding and interaction of hBD-2 with RBD mutants. All-atom molecular dynamics simulations were conducted on typical RBD mutants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N, and N501Y-E484K-K417N, binding with hBD-2. Starting from the stable binding structure of hBD-2 and wt-RBD and ClusPro and HADDOCK docking-predicted initial structures, the RBD variants bound with hBD-2 simulations were set up, and NAMD simulations were conducted. Based on the structure and dynamics analysis, it was found that most RBD variants can still form a similar number of hydrogen bonds with hBD-2, in addition to having a similar-sized buried surface area (BSA) and a similar binding interface to the RBD wildtype. However, the RBD triple mutant formed a less stable binding structure with hBD-2 than other variants. Additionally, the free energy perturbation (FEP) method was applied to calculate the contribution of key mutant residues to the binding and the free energy change caused by the mutations. The result shows that N439K, K417N, and the trimutation increase the binding free energy of RBD with hBD-2; thus, RBD should bind less stably with hBD-2. E484K decreases the binding free energy, thus it should bind more stably with hBD-2, while N501Y, S477N, T478I, and P479S almost do not change the binding free energy with hBD-2. The MM-GBSA method predicted the binding interaction energy which shows that the trimutant should be able to escape the binding with hBD-2 but N501Y should not. The result can provide insight into understanding the functional mechanism of hBD-2 combating SARS-CoV-2 mutants.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Development of highly sensitive one-step reverse transcription-quantitative PCR for SARS-CoV-2 detection in wastewater

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is a major public health concern that has highlighted the need to monitor circulating strains to better understand the coronavirus disease 2019 (COVID-19) pandemic. This study was carried out to monitor SARS-CoV-2 RNA and its variant-specific mutations in wastewater using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). One-step RT-qPCR using the SARS-CoV-2 Detection RT-qPCR Kit for Wastewater (Takara Bio), which amplified two N-gene regions simultaneously using CDC N1 and N2 assays with a single fluorescence dye, demonstrated better performance in detecting SARS-CoV-2 RNA (positive ratio, 66 %) compared to two-step RT-qPCR using CDC N1 or N2 assay (40 % each, and 52 % when combined), with significantly lower Ct values. The one-step RT-qPCR assay detected SARS-CoV-2 RNA in 59 % (38/64) of influent samples collected from a wastewater treatment plant in Japan between January 2021 and March 2022. The correlation between the concentration of SARS-CoV-2 RNA in the wastewater and the number of COVID-19 cases reported each day for 7 days pre- and post-sampling was significant (p < 0.05, r = 0.76 ± 0.03). Thirty-one influent samples which showed two-well positive for SARS-CoV-2 RNA were further tested by six mutations site-specific one-step RT-qPCR (E484K, L452R, N501Y, T478K, G339D, and E484A mutations). The N501Y mutation was detected between March and June 2021 but was replaced by the L452R and T478K mutations between July and October 2021, reflecting the shift from Alpha to Delta variants in the study region. The G339D and E484A mutations were identified in January 2022 and later when the incidence of the Omicron variant peaked. These findings indicate that wastewater-based epidemiology has the epidemiological potential to complement clinical tests to track the spread of COVID-19 and monitor variants circulating in communities.