A variant in human AIOLOS impairs adaptive immunity by interfering with IKAROS.

In the present study, we report a human-inherited, impaired, adaptive immunity disorder, which predominantly manifested as a B cell differentiation defect, caused by a heterozygous IKZF3 missense variant, resulting in a glycine-to-arginine replacement within the DNA-binding domain of the encoded AIOLOS protein. Using mice that bear the corresponding variant and recapitulate the B and T cell phenotypes, we show that the mutant AIOLOS homodimers and AIOLOS-IKAROS heterodimers did not bind the canonical AIOLOS-IKAROS DNA sequence. In addition, homodimers and heterodimers containing one mutant AIOLOS bound to genomic regions lacking both canonical motifs. However, the removal of the dimerization capacity from mutant AIOLOS restored B cell development. Hence, the adaptive immunity defect is caused by the AIOLOS variant hijacking IKAROS function. Heterodimeric interference is a new mechanism of autosomal dominance that causes inborn errors of immunity by impairing protein function via the mutation of its heterodimeric partner.

RhoG facilitates a conformational transition in the guanine nucleotide exchange factor complex DOCK5/ELMO1 to an open state.

The dedicator of cytokinesis (DOCK)/engulfment and cell motility (ELMO) complex serves as a guanine nucleotide exchange factor (GEF) for the GTPase Rac. RhoG, another GTPase, activates the ELMO-DOCK-Rac pathway during engulfment and migration. Recent cryo-EM structures of the DOCK2/ELMO1 and DOCK2/ELMO1/Rac1 complexes have identified closed and open conformations that are key to understanding the autoinhibition mechanism. Nevertheless, the structural details of RhoG-mediated activation of the DOCK/ELMO complex remain elusive. Herein, we present cryo-EM structures of DOCK5/ELMO1 alone and in complex with RhoG and Rac1. The DOCK5/ELMO1 structure exhibits a closed conformation similar to that of DOCK2/ELMO1, suggesting a shared regulatory mechanism of the autoinhibitory state across DOCK-A/B subfamilies (DOCK1-5). Conversely, the RhoG/DOCK5/ELMO1/Rac1 complex adopts an open conformation that differs from that of the DOCK2/ELMO1/Rac1 complex, with RhoG binding to both ELMO1 and DOCK5. The alignment of the DOCK5 phosphatidylinositol (3,4,5)-trisphosphate binding site with the RhoG C-terminal lipidation site suggests simultaneous binding of RhoG and DOCK5/ELMO1 to the plasma membrane. Structural comparison of the apo and RhoG-bound states revealed that RhoG facilitates a closed-to-open state conformational change of DOCK5/ELMO1. Biochemical and surface plasmon resonance (SPR) assays confirm that RhoG enhances the Rac GEF activity of DOCK5/ELMO1 and increases its binding affinity for Rac1. Further analysis of structural variability underscored the conformational flexibility of the DOCK5/ELMO1/Rac1 complex core, potentially facilitating the proximity of the DOCK5 GEF domain to the plasma membrane. These findings elucidate the structural mechanism underlying the RhoG-induced allosteric activation and membrane binding of the DOCK/ELMO complex.

A novel consensus-based computational pipeline for screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including Omicron variant.

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to evolve carrying flexible amino acid substitutions in the spike protein's receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility, and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with 3-fold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero- dimeric and trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concerns (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variant's RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow_3D11, and C118)-that may likely neutralize the recently emerged Omicron variant-facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.

Insights into the evolutionary forces that shape the codon usage in the viral genome segments encoding intrinsically disordered protein regions.

Intrinsically disordered regions/proteins (IDRs) are abundant across all the domains of life, where they perform important regulatory roles and supplement the biological functions of structured proteins/regions (SRs). Despite the multifunctionality features of IDRs, several interrogations on the evolution of viral genomic regions encoding IDRs in diverse viral proteins remain unreciprocated. To fill this gap, we benchmarked the findings of two most widely used and reliable intrinsic disorder prediction algorithms (IUPred2A and ESpritz) to a dataset of 6108 reference viral proteomes to unravel the multifaceted evolutionary forces that shape the codon usage in the viral genomic regions encoding for IDRs and SRs. We found persuasive evidence that the natural selection predominantly governs the evolution of codon usage in regions encoding IDRs by most of the viruses. In addition, we confirm not only that codon usage in regions encoding IDRs is less optimized for the protein synthesis machinery (transfer RNAs pool) of their host than for those encoding SRs, but also that the selective constraints imposed by codon bias sustain this reduced optimization in IDRs. Our analysis also establishes that IDRs in viruses are likely to tolerate more translational errors than SRs. All these findings hold true, irrespective of the disorder prediction algorithms used to classify IDRs. In conclusion, our study offers a novel perspective on the evolution of viral IDRs and the evolutionary adaptability to multiple taxonomically divergent hosts.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Evolutionary and codon usage preference insights into spike glycoprotein of SARS-CoV-2.

Interaction of SARS-CoV-2 spike glycoprotein with the ACE2 cell receptor is very crucial for virus attachment to human cells. Selected mutations in SARS-CoV-2 S-protein are reported to strengthen its binding affinity to mammalian ACE2. The N501T mutation in SARS-CoV-2-CTD furnishes better support to hotspot 353 in comparison with SARS-CoV and shows higher affinity for receptor binding. Recombination analysis exhibited higher recombination events in SARS-CoV-2 strains, irrespective of their geographical origin or hosts. Investigation further supports a common origin among SARS-CoV-2 and its predecessors, SARS-CoV and bat-SARS-like-CoV. The recombination events suggest a constant exchange of genetic material among the co-infecting viruses in possible reservoirs and human hosts before SARS-CoV-2 emerged. Furthermore, a comprehensive analysis of codon usage bias (CUB) in SARS-CoV-2 revealed significant CUB among the S-genes of different beta-coronaviruses governed majorly by natural selection and mutation pressure. Various indices of codon usage of S-genes helped in quantifying its adaptability in other animal hosts. These findings might help in identifying potential experimental animal models for investigating pathogenicity for drugs and vaccine development experiments.

ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures.

MOTIVATION: An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins. RESULTS: The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design. AVAILABILITY AND IMPLEMENTATION: http://github.com/KYZ-LSB/ProTerS-FitFun. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FPredX: Interpretable models for the prediction of spectral maxima, brightness, and oligomeric states of fluorescent proteins.

Fluorescent protein (FP) design is among the challenging protein design problems due to the tradeoffs among multiple properties to be optimized. Despite the accumulated efforts in design and characterization, progress has been slow in gaining a full understanding of sequence-property relationships to tackle the multiobjective design problem in FPs. In this study, we approach this problem by developing FPredX, a collection of gradient-boosted decision tree models, which mapped FP sequences to four major design targets of FPs, including excitation maximum, emission maximum, brightness, and oligomeric state. By training using one-hot encoded multiple aligned sequences with hyperparameters optimization in each model, FPredX models showed excellent prediction performance for all target properties compared with existing methods. We further interpreted the FPredX models by comparing the importance of positions along the aligned FP sequence to the predictive performance and suggested positions, which showed differential importance deemed by FPredX models to the prediction of each target property.

A loss-of-function variant in SUV39H2 identified in autism-spectrum disorder causes altered H3K9 trimethylation and dysregulation of protocadherin ß-cluster genes in the developing brain.

Recent evidence has documented the potential roles of histone-modifying enzymes in autism-spectrum disorder (ASD). Aberrant histone H3 lysine 9 (H3K9) dimethylation resulting from genetic variants in histone methyltransferases is known for neurodevelopmental and behavioral anomalies. However, a systematic examination of H3K9 methylation dynamics in ASD is lacking. Here we resequenced nine genes for histone methyltransferases and demethylases involved in H3K9 methylation in individuals with ASD and healthy controls using targeted next-generation sequencing. We identified a novel rare variant (A211S) in the SUV39H2, which was predicted to be deleterious. The variant showed strongly reduced histone methyltransferase activity in vitro. In silico analysis showed that the variant destabilizes the hydrophobic core and allosterically affects the enzyme activity. The Suv39h2-KO mice displayed hyperactivity and reduced behavioral flexibility in learning the tasks that required complex behavioral adaptation, which is relevant for ASD. The Suv39h2 deficit evoked an elevated expression of a subset of protocadherin ß (Pcdhb) cluster genes in the embryonic brain, which is attributable to the loss of H3K9 trimethylation (me3) at the gene promoters. Reduced H3K9me3 persisted in the cerebellum of Suv39h2-deficient mice to an adult stage. Congruently, reduced expression of SUV39H1 and SUV39H2 in the postmortem brain samples of ASD individuals was observed, underscoring the role of H3K9me3 deficiency in ASD etiology. The present study provides direct evidence for the role of SUV39H2 in ASD and suggests a molecular cascade of SUV39H2 dysfunction leading to H3K9me3 deficiency followed by an untimely, elevated expression of Pcdhb cluster genes during early neurodevelopment.

A novel structure-based approach for identification of vertebrate susceptibility to SARS-CoV-2: Implications for future surveillance programmes.

Understanding the origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a highly debatable and unresolved issue for scientific communities all over the world. Understanding the mechanism of virus entry to the host cells is crucial to deciphering the susceptibility profiles of animal species to SARS-CoV-2. The interaction of SARS-CoV-2 ligands (receptor-binding domain on spike protein) with its host cell receptor, angiotensin-converting enzyme 2 (ACE2), is a critical determinant of host range and cross-species transmission. In this study, we developed and implemented a rigorous computational approach for predicting binding affinity between 299 ACE2 orthologs from diverse vertebrate species and the SARS-CoV-2 spike protein. The findings show that the SARS-CoV-2 spike protein can bind to a wide range of vertebrate species carrying evolutionary divergent ACE2, implying a broad host range at the virus entry level, which may contribute to cross-species transmission and further viral evolution. Furthermore, the current study facilitated the identification of genetic determinants that may differentiate susceptible from resistant host species based on the conservation of ACE2-spike protein interacting residues in vertebrate host species known to facilitate SARS-CoV-2 infection; however, these genetic determinants warrant in vivo experimental confirmation. The molecular interactions associated with varied binding affinity of distinct ACE2 isoforms in a specific bat species were identified using protein structure analysis, implying the existence of diversified bat species' susceptibility to SARS-CoV-2. The current study's findings highlight the importance of intensive surveillance programmes aimed at identifying susceptible hosts, especially those with the potential to transmit zoonotic pathogens, in order to prevent future outbreaks.

Comprehensive Intrinsic Disorder Analysis of 6108 Viral Proteomes: From the Extent of Intrinsic Disorder Penetrance to Functional Annotation of Disordered Viral Proteins.

Much of our understanding of proteins and proteomes comes from the traditional protein structure-function paradigm. However, in the last 2 decades, both computational and experimental studies have provided evidence that a large fraction of functional proteomes across different domains of life consists of intrinsically disordered proteins, thus triggering a quest to unravel and decipher protein intrinsic disorder. Unlike structured/ordered proteins, intrinsically disordered proteins/regions (IDPs/IDRs) do not possess a well-defined structure under physiological conditions and exist as highly dynamic conformational ensembles. In spite of this peculiarity, these proteins have crucial roles in cell signaling and regulation. To date, studies on the abundance and function of IDPs/IDRs in viruses are rather limited. To fill this gap, we carried out an extensive and thorough bioinformatics analysis of 283 000 proteins from 6108 reference viral proteomes. We analyzed protein intrinsic disorder from multiple perspectives, such as abundance of IDPs/IDRs across diverse virus types, their functional annotations, and subcellular localization in taxonomically divergent hosts. We show that the content of IDPs/IDRs in viral proteomes varies broadly as a function of virus genome types and taxonomically divergent hosts. We have combined the two most commonly used and accurate IDP predictors' results with charge-hydropathy (CH) versus cumulative distribution function (CDF) plots to categorize the viral proteins according to their IDR content and physicochemical properties. Mapping of gene ontology on the disorder content of viral proteins reveals that IDPs are primarily involved in key virus-host interactions and host antiviral immune response downregulation, which are reinforced by the post-translational modifications tied to disorder-enriched viral proteins. The present study offers detailed insights into the prevalence of the intrinsic disorder in viral proteomes and provides appealing targets for the design of novel therapeutics.

The symmetric designer protein Pizza as a scaffold for metal coordination.

Symmetric proteins are currently of interest as they allow creation of larger assemblies and facilitate the incorporation of metal ions in the larger complexes. Recently this was demonstrated by the biomineralization of the cadmium-chloride nanocrystal via the Pizza designer protein. However, the mechanism behind this formation remained unclear. Here, we set out to investigate the mechanism driving the formation of this nanocrystal via truncation, mutation, and circular permutations. In addition, the interaction of other biologically relevant metal ions with these symmetric proteins to form larger symmetric complexes was also studied. The formation of the initial nanocrystal is shown to originate from steric strain, where His 58 induces a different rotameric conformation on His 73, thereby distorting an otherwise perfect planar ring of alternating cadmium and chlorine ions, resulting in the smallest nanocrystal. Similar highly symmetric complexes were also observed for the other biological relevant metal ions. However, the flexibility of the coordinating histidine residues allows each metal ion to adopt its preferred geometry leading to either monomeric or dimeric ß-propeller units, where the metal ions are located at the interface between both propeller units. These results demonstrate that symmetric proteins are not only interesting to generate larger assemblies, but are also the perfect scaffold to create more complex metal based assemblies. Such metal protein assemblies may then find applications in bionanotechnology or biocatalysis.

A multidimensional computational exploration of congenital myasthenic syndrome causing mutations in human choline acetyltransferase.

Missense mutations of human choline acetyltransferase (CHAT) are mainly associated with congenital myasthenic syndrome (CMS). To date, several pathogenic mutations have been reported, but due to the rarity and genetic complexity of CMS and difficult genotype-phenotype correlations, the CHAT mutations, and their consequences are underexplored. In this study, we systematically sift through the available genetic data in search of previously unreported pathogenic mutations and use a dynamic in silico model to provide structural explanations for the pathogenicity of the reported deleterious and undetermined variants. Through rigorous multiparameter analyses, we conclude that mutations can affect CHAT through a variety of different mechanisms: by disrupting the secondary structure, by perturbing the P-loop through long-range allosteric interactions, by disrupting the domain connecting loop, and by affecting the phosphorylation process. This study provides the first dynamic look at how mutations affect the structure and catalytic activity in CHAT and highlights the need for further genomic research to better understand the pathology of CHAT.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking.

In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.

Structure-based virtual screening of highly potent inhibitors of the nematode chitinase CeCht1.

Nematode chitinases play vital roles in various physiological processes, including egg hatching, larva moulting, and reproduction. Small-molecule inhibitors of nematode chitinases have potential applications for controlling nematode pests. On the basis of the crystal structure of CeCht1, a representative chitinase indispensable to the eggshell chitin degradation of the model nematode Caenorhabditis elegans, we have discovered a series of novel inhibitors bearing a (R)-3,4-diphenyl-4,5-dihydropyrrolo[3,4-c]pyrazol-6(2H)-one scaffold by hierarchical virtual screening. The crystal structures of CeCht1 complexed with two of these inhibitors clearly elucidated their interactions with the enzyme active site. Based on the inhibitory mechanism, several analogues with improved inhibitory activities were identified, among which the compound PP28 exhibited the most potent activity with a Ki value of 0.18 µM. This work provides the structural basis for the development of novel nematode chitinase inhibitors.

A protein sequence fitness function for identifying natural and nonnatural proteins.

The infinitesimally small sequence space naturally scouted in the millions of years of evolution suggests that the natural proteins are constrained by some functional prerequisites and should differ from randomly generated sequences. We have developed a protein sequence fitness scoring function that implements sequence and corresponding secondary structural information at tripeptide levels to differentiate natural and nonnatural proteins. The proposed fitness function is extensively validated on a dataset of about 210 000 natural and nonnatural protein sequences and benchmarked with existing methods for differentiating natural and nonnatural proteins. The high sensitivity, specificity, and percentage accuracy (0.81%, 0.95%, and 91% respectively) of the fitness function demonstrates its potential application for sampling the protein sequences with higher probability of mimicking natural proteins. Moreover, the four major classes of proteins (α proteins, ß proteins, α/ß proteins, and α + ß proteins) are separately analyzed and ß proteins are found to score slightly lower as compared to other classes. Further, an analysis of about 250 designed proteins (adopted from previously reported cases) helped to define the boundaries for sampling the ideal protein sequences. The protein sequence characterization aided by the proposed fitness function could facilitate the exploration of new perspectives in the design of novel functional proteins.

A structural homology approach for computational protein design with flexible backbone.

MOTIVATION: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. RESULTS: We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. AVAILABILITY AND IMPLEMENTATION: Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. In the absence of any antiviral or immunomodulatory therapies, the disease is spreading at an alarming rate. A possibility of a resurgence of COVID-19 in places where lockdowns have already worked is also developing. Thus, for controlling COVID-19, vaccines may be a better option than drugs. An mRNA-based anti-COVID-19 candidate vaccine has entered a phase 1 clinical trial. However, its efficacy and potency have to be evaluated and validated. Since vaccines have high failure rates, as an alternative, we are presenting a new, designed multi-peptide subunit-based epitope vaccine against COVID-19. The recombinant vaccine construct comprises an adjuvant, cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes joined by linkers. The computational data suggest that the vaccine is non-toxic, non-allergenic, thermostable, with the capability to elicit a humoral and cell-mediated immune response. The stabilization of the vaccine construct is validated with molecular dynamics simulation studies. This unique vaccine is made up of 33 highly antigenic epitopes from three proteins that have a prominent role in host-receptor recognition, viral entry, and pathogenicity. We advocate this vaccine must be synthesized and tested urgently as a public health priority.

Identification of a Selective RelA Inhibitor Based on DSE-FRET Screening Methods.

Nuclear factor-κB (NF-κB) is an important transcription factor involved in various biological functions, including tumorigenesis. Hence, NF-κB has attracted attention as a target factor for cancer treatment, leading to the development of several inhibitors. However, existing NF-κB inhibitors do not discriminate between its subunits, namely, RelA, RelB, cRel, p50, and p52. Conventional methods used to evaluate interactions between transcription factors and DNA, such as electrophoretic mobility shift assay and luciferase assays, are unsuitable for high-throughput screening (HTS) and cannot distinguish NF-κB subunits. We developed a HTS method named DNA strand exchange fluorescence resonance energy transfer (DSE-FRET). This assay is suitable for HTS and can discriminate a NF-κB subunit. Using DSE-FRET, we searched for RelA-specific inhibitors and verified RelA inhibition for 32,955 compounds. The compound A55 (2-(3-carbamoyl-6-hydroxy-4-methyl-2-oxopyridin-1(2H)-yl) acetic acid) selectively inhibited RelA-DNA binding. We propose that A55 is a seed compound for RelA-specific inhibition and could be used in clinical applications.