Transfer learning improves pMHC kinetic stability and immunogenicity predictions.

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

Innate extracellular Hsp70 inflammatory properties are mediated by the interaction of Siglec-E and LOX-1 receptors.

Innate immune responses to cell damage-associated molecular patterns induce a controlled degree of inflammation, ideally avoiding the promotion of intense unwanted inflammatory adverse events. When released by damaged cells, Hsp70 can stimulate different responses that range from immune activation to immune suppression. The effects of Hsp70 are mediated through innate receptors expressed primarily by myeloid cells, such as dendritic cells (DCs). The regulatory innate receptors that bind to extracellular mouse Hsp70 (mHsp70) are not fully characterized, and neither are their potential interactions with activating innate receptors. Here, we describe that extracellular mHsp70 interacts with a receptor complex formed by inhibitory Siglec-E and activating LOX-1 on DCs. We also find that this interaction takes place within lipid microdomains, and Siglec-E acts as a negative regulator of LOX-1-mediated innate activation upon mHsp70 or oxidized LDL binding. Thus, HSP70 can both bind to and modulate the interaction of inhibitory and activating innate receptors on the cell surface. These findings add another dimension of regulatory mechanism to how self-molecules contribute to dampening of exacerbated inflammatory responses.

Variation of modulation and expression of biomarkers associated with inflammation in bariatric surgery patients: A systematic review and meta-analysis.

BACKGROUND: Bariatric surgery is an effective intervention that causes a series of metabolic changes related to inflammatory processes; however, the variation of biomarkers related to these processes is not entirely understood. Our objective was to investigate the variation of modulation and expression of biomarkers associated with inflammation in patients who underwent bariatric surgery. METHODS: We searched the MEDLINE (via PubMed), EMBASE (via Elsevier), Cochrane Central Register of Controlled Trials, Latin American and Caribbean Literature on Health Sciences (via virtual health library), Cumulative Index to Nursing and Allied Health Literature (via EBSCO), Web of Science core collection, and Scopus (via Elsevier) databases, and the gray literature was examined from inception to January 2022. Three pairs of reviewers performed data screening, extraction, and quality assessment independently. Meta-analysis with random effects models was used for general, subgroup, and sensitivity analyses. The I2 statistic was used to assess heterogeneity between studies. RESULTS: In total, 96 articles were included in this systematic review; of these, 87 studies met the criteria for the meta-analysis, involving 3,533 participants. Five biomarkers were included in the meta-analysis (tumor necrosis factor alpha; interleukin 6; leptin; interleukin 1 beta, and lipopolysaccharides). Only leptin showed a significant decrease in the first month after surgery (mean difference -20.71; [95% confidence interval: -28.10 to -13.32, P < .0001; I2 = 66.7%), with moderate heterogeneity. The 12 months after surgery showed a significant decrease in tumor necrosis factor alpha (mean difference -0.89; [95% confidence interval: -1.37 to -0.42], P = .0002; I2 = 94.7%), interleukin 6 (mean difference -1.62; [95% confidence interval: -1.95 to -1.29], P < .0001; I2 = 94.9%), leptin (mean difference -28.63; [95% confidence interval: -34.02 to -23.25], P < .0001; I2 = 92.7%), and interleukin 1 beta (mean difference -2.46; [95% confidence interval: -4.23 to -0.68], P = .006; I2 = 98.3%), all with high heterogeneity. The type of surgery did not show significant differences for the biomarkers at the first month and 12 months, and the results have not changed with high-quality studies. In the 12-month measurement, variations in tumor necrosis factor alpha and leptin were associated with body mass index. CONCLUSION: The findings of this meta-analysis suggest that Roux-en-Y gastric bypass and sleeve gastrectomy bariatric surgeries are associated with a significant reduction in leptin at 1 month after bariatric surgical intervention and tumor necrosis factor alpha, leptin, and interleukin 1 beta after 12 months.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure.

Introduction: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Methods: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. Results and discussion: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules.

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development.

The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.

3pHLA-score improves structure-based peptide-HLA binding affinity prediction.

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.

Short-chain fatty acid acetate triggers antiviral response mediated by RIG-I in cells from infants with respiratory syncytial virus bronchiolitis.

BACKGROUND: Gut microbiota-derived short-chain fatty-acid (SFCA) acetate protects mice against RSV A2 strain infection by increasing interferon-ß production and expression of interferon-stimulated genes (ISGs). However, the role of SFCA in RSV infection using strains isolated from patients is unknown. METHODS: We first used RSV clinical strains isolated from infants hospitalized with RSV bronchiolitis to investigate the effects of in vitro SCFA-acetate treatment of human pulmonary epithelial cells. We next examined whether SCFA-acetate treatment is beneficial in a mouse model of RSV infection using clinical isolates. We sought to investigate the relationship of gut microbiota and fecal acetate with disease severity among infants hospitalized with RSV bronchiolitis, and whether treating their respiratory epithelial cells with SCFA-acetate ex-vivo impacts viral load and ISG expression. We further treated epithelial cells from SARS-CoV-2 infected patients with SCFA-acetate. FINDINGS: In vitro pre-treatment of A549 cells with SCFA-acetate reduced RSV infection with clinical isolates and increased the expression of RIG-I and ISG15. Animals treated with SCFA-acetate intranasally recovered significantly faster, with reduction in the RSV clinical isolates viral load, and increased lung expression of IFNB1 and the RIG-I. Experiments in RIG-I knockout A549 cells demonstrated that the protection relies on RIG-I presence. Gut microbial profile was associated with bronchiolitis severity and with acetate in stool. Increased SCFA-acetate levels were associated with increasing oxygen saturation at admission, and shorter duration of fever. Ex-vivo treatment of patients' respiratory cells with SCFA-acetate reduced RSV load and increased expression of ISGs OAS1 and ISG15, and virus recognition receptors MAVS and RIG-I, but not IFNB1. These SCFA-acetate effects were not found on cells from SARS-CoV-2 infected patients. INTERPRETATION: SCFA-acetate reduces the severity of RSV infection and RSV viral load through modulation of RIG-I expression. FUNDING: FAPERGS (FAPERGS/MS/CNPq/SESRS no. 03/2017 - PPSUS 17/2551-0001380-8 and COVID-19 20/2551-0000258-6); CNPq 312504/2017-9; CAPES) - Finance Code 001.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

Association between TNF-α and IFN-γ levels and severity of acute viral bronchiolitis.

Acute bronchiolitis caused by the respiratory syncytial virus triggers an inflammatory response with the production and release of several pro-inflammatory cytokines. Evidence suggests that their levels are associated with the severity of the infection. This systematic review and meta-analysis aim to assess whether the levels of TNF-α and IFN-γ are associated with the severity of acute viral bronchiolitis. We searched MEDLINE libraries (via PUBMED), EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), Scientific Electronic Library Online (SciELO), Latin American Caribbean Health Sciences Literature (LILACS), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and the gray literature through April 2020. Random effect models were used for general and subgroup analysis. In total, six studies were included with a total of 744 participants. The mean TNF-α levels between the severe group did not differ from the control group 0.14 (95% CI: -0.53 to 0.82, I2 = 91%, p < 0.01); the heterogeneity was high. The results remained insignificant when the analyses were performed including only studies with high quality 0.25 (95% CI: -0.46 to 0.96, I2 = 92%, p < 0.01) I2 = 95%, p = 0.815), when TNF-α was nasal 0.60 (95% CI: -0.49 to 1.69), I2 = 94%, p < 0.01), or serum -0.08 (95% CI: -0.48 to 0.31), I2 = 29%, p = 0.24). In the analysis of studies measuring IFN-γ, there was also no significance of -0.67 (95% CI: -1.56 to 0.22, I2 = 76%, p = 0.04). In conclusion, this meta-analysis suggests that the most severe patients do not have different mean TNF-α and IFN-γ values ​than patients with mild disease, but the heterogeneity of the studies was high. Supplemental data for this article is available online at https://doi.org/10.1080/08830185.2021.1889534.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

MOTIVATION: Recent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp). RESULTS: Ensembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material. AVAILABILITY: dinc-covid.kavrakilab.org. SUPPLEMENTARY INFORMATION: Details on methods for ensemble generation and docking are provided as supplementary data online. CONTACT: geancarlo.zanatta@ufc.br , kavraki@rice.edu.

Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2.

Although not being the first viral pandemic to affect humankind, we are now for the first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants arising in different countries. Such persistent spread is in part enabled by public resistance to vaccination in some countries, and limited access to vaccines in other countries. The limited vaccination coverage, the continued risk for resistant variants, and the existence of natural reservoirs for coronaviruses, highlight the importance of developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin (BCG) vaccination programs could be associated with a reduced number and/or severity of COVID-19 cases. Preliminary studies have provided evidence for this relationship and further investigation is being conducted in ongoing clinical trials. The protection against SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes, which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the surface of infected cells. In order to identify potential targets of T cell cross-reactivity, we implemented an in silico strategy combining sequence-based and structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled peptide-HLA structures for the screened targets, and (iii) new computational methods for structure-based screenings that can be used by others in future studies. Our study expands the list of BCG peptides potentially involved in T cell cross-reactivity with SARS-CoV-2-derived peptides, and identifies multiple high-density "neighborhoods" of cross-reactive peptides which could be driving heterologous immunity induced by BCG vaccination, therefore providing insights for future vaccine development efforts.

Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G.

HLA-G is considered to be an immune checkpoint molecule, a function that is closely linked to the structure and dynamics of the different HLA-G isoforms. Unfortunately, little is known about the structure and dynamics of these isoforms. For instance, there are only seven crystal structures of HLA-G molecules, being all related to a single isoform, and in some cases lacking important residues associated to the interaction with leukocyte receptors. In addition, they lack information on the dynamics of both membrane-bound HLA-G forms, and soluble forms. We took advantage of in silico strategies to disclose the dynamic behavior of selected HLA-G forms, including the membrane-bound HLA-G1 molecule, soluble HLA-G1 dimer, and HLA-G5 isoform. Both the membrane-bound HLA-G1 molecule and the soluble HLA-G1 dimer were quite stable. Residues involved in the interaction with ILT2 and ILT4 receptors (α3 domain) were very close to the lipid bilayer in the complete HLA-G1 molecule, which might limit accessibility. On the other hand, these residues can be completely exposed in the soluble HLA-G1 dimer, due to the free rotation of the disulfide bridge (Cys42/Cys42). In fact, we speculate that this free rotation of each protomer (i.e., the chains composing the dimer) could enable alternative binding modes for ILT2/ILT4 receptors, which in turn could be associated with greater affinity of the soluble HLA-G1 dimer. Structural analysis of the HLA-G5 isoform demonstrated higher stability for the complex containing the peptide and coupled ß2-microglobulin, while structures lacking such domains were significantly unstable. This study reports for the first time structural conformations for the HLA-G5 isoform and the dynamic behavior of HLA-G1 molecules under simulated biological conditions. All modeled structures were made available through GitHub (https://github.com/KavrakiLab/), enabling their use as templates for modeling other alleles and isoforms, as well as for other computational analyses to investigate key molecular interactions.

Novel frameshift variant of the CFTR gene: S511Lfs*2 from phenotype to molecular predictions.

Cystic fibrosis (CF) is a genetic disease caused by variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. There are over 2,000 different pathogenic and non-pathogenic variants described in association with a broad clinical heterogeneity. In this work, we identified a novel variant S511Lfs*2 in CFTR gene that has not been reported in patients with CF. The patient was a female genotyped with c.1000C>T (legacy name: R334W) variant (pathogenic, CF-causing) and the novel variant (S511Lfs*2). We verified the amino acid sequence, the protein structure, and predicted the pathogenicity employing computational analysis. Our findings showed that S511Lfs*2 is a frameshift variant and suggest that it is associated with severe CF phenotype, as it leads to a lack of CFTR protein synthesis, and consequently the loss of its functional activity.

Host expression system modulates recombinant Hsp70 activity through post-translational modifications.

The use of model organisms for recombinant protein production results in the addition of model-specific post-translational modifications (PTMs) that can affect the structure, charge, and function of the protein. The 70-kDa heat shock proteins (Hsp70) were originally described as intracellular chaperones, with ATPase and foldase activity. More recently, new extracellular activities of Hsp70 proteins (e.g. as immunomodulators) have been identified. While some studies indicate an inflammatory potential for extracellular Hsp70 proteins, others suggest an immunosuppressive activity. We hypothesized that the production of recombinant Hsp70 in different expression systems would result in the addition of different PTMs, perhaps explaining at least some of these opposing immunological outcomes. We produced and purified Mycobacterium tuberculosis DnaK from two different systems, Escherichia coli and Pichia pastoris, and analyzed by mass spectrometry the protein preparations, investigating the impact of PTMs in an in silico and in vitro perspective. The comparisons of DnaK structures in silico highlighted that electrostatic and topographical differences exist that are dependent upon the expression system. Production of DnaK in the eukaryotic system dramatically affected its ATPase activity, and significantly altered its ability to downregulate MHC II and CD86 expression on murine dendritic cells (DCs). Phosphatase treatment of DnaK indicated that some of these differences related specifically to phosphorylation. Altogether, our data indicate that PTMs are an important characteristic of the expression system, with differences that impact interactions of Hsps with their ligands and subsequent functional activities.

Respiratory syncytial virus reduces STAT3 phosphorylation in human memory CD8 T cells stimulated with IL-21.

Respiratory syncytial virus (RSV) is a common cause of childhood lower respiratory tract infections. The recent failure of a vaccine candidate based on recombinant F protein underlines the urgent need to better understand the protective human memory immune response against RSV. Signal transducer and activator of transcription 3 (STAT3) protein is a transcription factor that promotes the maturation of the memory CD8 T cell response in cooperation with IL-10 and IL-21. However, the role of STAT3 in the memory CD8 T cell response during RSV infection remains to be elucidated. We found that in infants with bronchiolitis infected with RSV, the expression of STAT3 detected in nasal washes is reduced when compared to that in infants infected by other viruses. In vitro, RSV impairs STAT3 phosphorylation induced by IL-21 in purified human memory CD8 T cells. In addition, RSV decreases granzyme B production by memory CD8 T cells, reducing its cytotoxic activity against RSV-infected epithelial pulmonary cell lines. Together, these data indicate that RSV modulates the IL-21/STAT3 pathway in human memory CD8 T cells, and this could be a mechanism to be further explored to improve the memory response against the infection.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.