Immunization with V987H-stabilized Spike glycoprotein protects K18-hACE2 mice and golden Syrian hamsters upon SARS-CoV-2 infection.

Safe and effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines are crucial to fight against the coronavirus disease 2019 pandemic. Most vaccines are based on a mutated version of the Spike glycoprotein [K986P/V987P (S-2P)] with improved stability, yield and immunogenicity. However, S-2P is still produced at low levels. Here, we describe the V987H mutation that increases by two-fold the production of the recombinant Spike and the exposure of the receptor binding domain (RBD). S-V987H immunogenicity is similar to S-2P in mice and golden Syrian hamsters (GSH), and superior to a monomeric RBD. S-V987H immunization confer full protection against severe disease in K18-hACE2 mice and GSH upon SARS-CoV-2 challenge (D614G or B.1.351 variants). Furthermore, S-V987H immunized K18-hACE2 mice show a faster tissue viral clearance than RBD- or S-2P-vaccinated animals challenged with D614G, B.1.351 or Omicron BQ1.1 variants. Thus, S-V987H protein might be considered for future SARS-CoV-2 vaccines development.

Virus-like particle-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice.

BACKGROUND: Neoantigens are patient- and tumor-specific peptides that arise from somatic mutations. They stand as promising targets for personalized therapeutic cancer vaccines. The identification process for neoantigens has evolved with the use of next-generation sequencing technologies and bioinformatic tools in tumor genomics. However, in-silico strategies for selecting immunogenic neoantigens still have very low accuracy rates, since they mainly focus on predicting peptide binding to Major Histocompatibility Complex (MHC) molecules, which is key but not the sole determinant for immunogenicity. Moreover, the therapeutic potential of neoantigen-based vaccines may be enhanced using an optimal delivery platform that elicits robust de novo immune responses. METHODS: We developed a novel neoantigen selection pipeline based on existing software combined with a novel prediction method, the Neoantigen Optimization Algorithm (NOAH), which takes into account structural features of the peptide/MHC-I interaction, as well as the interaction between the peptide/MHC-I complex and the TCR, in its prediction strategy. Moreover, to maximize neoantigens' therapeutic potential, neoantigen-based vaccines should be manufactured in an optimal delivery platform that elicits robust de novo immune responses and bypasses central and peripheral tolerance. RESULTS: We generated a highly immunogenic vaccine platform based on engineered HIV-1 Gag-based Virus-Like Particles (VLPs) expressing a high copy number of each in silico selected neoantigen. We tested different neoantigen-loaded VLPs (neoVLPs) in a B16-F10 melanoma mouse model to evaluate their capability to generate new immunogenic specificities. NeoVLPs were used in in vivo immunogenicity and tumor challenge experiments. CONCLUSIONS: Our results indicate the relevance of incorporating other immunogenic determinants beyond the binding of neoantigens to MHC-I. Thus, neoVLPs loaded with neoantigens enhancing the interaction with the TCR can promote the generation of de novo antitumor-specific immune responses, resulting in a delay in tumor growth. Vaccination with the neoVLP platform is a robust alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy.

Novel Spike-stabilized trimers with improved production protect K18-hACE2 mice and golden Syrian hamsters from the highly pathogenic SARS-CoV-2 Beta variant.

Most COVID-19 vaccines are based on the SARS-CoV-2 Spike glycoprotein (S) or their subunits. However, S shows some structural instability that limits its immunogenicity and production, hampering the development of recombinant S-based vaccines. The introduction of the K986P and V987P (S-2P) mutations increases the production and immunogenicity of the recombinant S trimer, suggesting that these two parameters are related. Nevertheless, S-2P still shows some molecular instability and it is produced with low yield. Here we described a novel set of mutations identified by molecular modeling and located in the S2 region of the S-2P that increase its production up to five-fold. Besides their immunogenicity, the efficacy of two representative S-2P-based mutants, S-29 and S-21, protecting from a heterologous SARS-CoV-2 Beta variant challenge was assayed in K18-hACE2 mice (an animal model of severe SARS-CoV-2 disease) and golden Syrian hamsters (GSH) (a moderate disease model). S-21 induced higher level of WH1 and Delta variants neutralizing antibodies than S-2P in K18-hACE2 mice three days after challenge. Viral load in nasal turbinate and oropharyngeal samples were reduced in S-21 and S-29 vaccinated mice. Despite that, only the S-29 protein protected 100% of K18-hACE2 mice from severe disease. When GSH were analyzed, all immunized animals were protected from disease development irrespectively of the immunogen they received. Therefore, the higher yield of S-29, as well as its improved immunogenicity and efficacy protecting from the highly pathogenic SARS-CoV-2 Beta variant, pinpoint the S-29 mutant as an alternative to the S-2P protein for future SARS-CoV-2 vaccine development.

NetCleave: An Open-Source Algorithm for Predicting C-Terminal Antigen Processing for MHC-I and MHC-II.

T cell epitopes presented on the surface of mammalian cells are subjected to a complex network of antigen processing and presentation. Among them, C-terminal antigen processing constitutes one of the main bottlenecks for the generation of epitopes, as it defines the C-terminal end of the final epitope and delimits the peptidome that will be presented downstream. Previously (Amengual-Rigo and Guallar, Sci Rep 111(11):1-8, 2021), we demonstrated that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, which in its turn can be crucial to design peptide-based vaccination strategies. In this chapter, we provide a pipeline to exploit the full capabilities of NetCleave, an open-source and retrainable algorithm for predicting the C-terminal antigen processing for the MHC-I and MHC-II pathways.

NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II.

Antigens presented on the cell surface have been subjected to multiple biological processes. Among them, C-terminal antigen processing constitutes one of the main bottlenecks of the peptide presentation pathways, as it delimits the peptidome that will be subjected downstream. Here, we present NetCleave, an open-source and retrainable algorithm for the prediction of the C-terminal antigen processing for both MHC-I and MHC-II pathways. NetCleave architecture consists of a neural network trained on 46 different physicochemical descriptors of the cleavage site amino acids. Our results demonstrate that prediction of C-terminal antigen processing achieves high accuracy on MHC-I (AUC of 0.91), while it remains challenging for MHC-II (AUC of 0.66). Moreover, we evaluated the performance of NetCleave and other prediction tools for the evaluation of four independent immunogenicity datasets (H2-Db, H2-Kb, HLA-A*02:01 and HLA-B:07:02). Overall, we demonstrate that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, and we provide one of the first evidence that C-terminal processing predictions may help in the discovery of immunogenic peptides.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes.

MOTIVATION: Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. RESULTS: Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code. AVAILABILITY AND IMPLEMENTATION: UEP algorithm can be found at: https://github.com/pepamengual/UEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations.

Human Immunodeficiency Virus 1 (HIV-1) evades adaptive immunity by means of its extremely high mutation rate, which allows the HIV envelope glycoprotein to continuously escape from the action of antibodies. However, some broadly neutralizing antibodies (bNAbs) targeting specific viral regions show the ability to block the infectivity of a large number of viral variants. The discovery of these antibodies opens new avenues in anti-HIV therapy; however, they are still suboptimal tools as their amplitude of action ranges between 50% and 90% of viral variants. In this context, being able to discriminate between sensitive and resistant strains to an antibody would be of great interest for the design of optimal clinical antibody treatments and to engineer potent bNAbs for clinical use. Here, we describe a hierarchical procedure to predict the antibody neutralization efficacy of multiple viral isolates to three well-known anti-CD4bs bNAbs: VRC01, NIH45-46 and 3BNC117. Our method consists of simulating the three-dimensional binding process between the gp120 and the antibody by using Protein Energy Landscape Exploration (PELE), a Monte Carlo stochastic approach. Our results clearly indicate that the binding profiles of sensitive and resistant strains to a bNAb behave differently, showing the latter's weaker binding profiles, that can be exploited for predicting antibody neutralization efficacy in hypermutated HIV-1 strains.

Survivin modulation in the antimelanoma activity of prodiginines.

Melanoma is a type of skin cancer with an elevated incidence of metastasis and chemoresistance. Such features hamper treatment success of these neoplasms, demanding the search for new therapeutic options. Using a two-step resin-based approach, we recently demonstrated that cytotoxic prodiginines bind to the inhibitor of apoptosis protein, survivin. Herein, we explore the role of survivin in melanoma and whether its modulation is related to the antimelanoma properties of three cytotoxic prodiginines (prodigiosin, cyclononylprodigiosin, and nonylprodigiosin) isolated from marine bacteria. In melanoma patients and cell lines, survivin is overexpressed, and higher levels negatively impact survival. All three prodiginines caused a decrease in cell growth with reduced cytotoxicity after 24 h compared to 72 h treatment, suggesting that low concentrations promote cytostatic effects in SK-Mel-19 (BRAF mutant) and SK-Mel-28 (BRAF mutant), but not in SK-Mel-147 (NRAS mutant). An increase in G1 population was observed after 24 h treatment with prodigiosin and cyclononylprodigiosin in SK-Mel-19. Further studies indicate that prodigiosin induced apoptosis and DNA damage, as detected by increased caspase-3 cleavage and histone H2AX phosphorylation, further arguing for the downregulation of survivin. Computer simulations suggest that prodigiosin and cyclononylprodigiosin bind to the BIR domain of survivin. Moreover, knockdown of survivin increased long-term toxicity of prodigiosin, as observed by reduced clonogenic capacity, but did not alter short-term cytotoxicity. In summary, prodiginine treatment provoked cytostatic rather than cytotoxic effects, cell cycle arrest at G0/G1 phase, induction of apoptosis and DNA damage, downregulation of survivin, and decreased clonogenic capacity in survivin knockdown cells.

Automated Design of Efficient and Functionally Diverse Enzyme Repertoires.

Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.

Multiple implications of an active site phenylalanine in the catalysis of aryl-alcohol oxidase.

Aryl-alcohol oxidase (AAO) has demonstrated to be an enzyme with a bright future ahead due to its biotechnological potential in deracemisation of chiral compounds, production of bioplastic precursors and other reactions of interest. Expanding our understanding on the AAO reaction mechanisms, through the investigation of its structure-function relationships, is crucial for its exploitation as an industrial biocatalyst. In this regard, previous computational studies suggested an active role for AAO Phe397 at the active-site entrance. This residue is located in a loop that partially covers the access to the cofactor forming a bottleneck together with two other aromatic residues. Kinetic and affinity spectroscopic studies, complemented with computational simulations using the recently developed adaptive-PELE technology, reveal that the Phe397 residue is important for product release and to help the substrates attain a catalytically relevant position within the active-site cavity. Moreover, removal of aromaticity at the 397 position impairs the oxygen-reduction activity of the enzyme. Experimental and computational findings agree very well in the timing of product release from AAO, and the simulations help to understand the experimental results. This highlights the potential of adaptive-PELE to provide answers to the questions raised by the empirical results in the study of enzyme mechanisms.