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.

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.

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.

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.

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.

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.

Vaccination with RSV M209-223 peptide promotes a protective immune response associated with reduced pulmonary inflammation.

Respiratory syncytial virus (RSV) is the most common etiologic agent in severe infections of the lower respiratory tract in children with a high mortality rate. However, there are still no licensed vaccines for RSV. In this study, we investigated a putative vaccine based on M209-223 peptide. Mice vaccinated with M209-223 peptide expanded M209-223-specific effector CD4+ T cells upon infection. Vaccination resulted in increased numbers of regulatory T cells (Treg) and Th1 cells, and decreased numbers of Th2 cells. In addition, vaccination with M209-223 peptide, protected mice from infection and prevented lung inflammation, leading to increase in IL-10 and IFN-γ production by lung CD4+ T cells. Treg depletion with anti-CTLA4 antibodies abrogated protection induced by peptide vaccination. Our results support vaccination with M209-223 peptide as an important strategy to generate protection, both systemic and local, by memory RSV-specific CD4+ T cells in mice. Contrarily to inactivated RSV particles, M209-223 peptide vaccination is capable of not only promoting viral clearance, but also reducing inflammatory processes in lungs upon infection.

DockTope: a Web-based tool for automated pMHC-I modelling.

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8(+) T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

Evolutionary pattern in the OXT-OXTR system in primates: coevolution and positive selection footprints.

Oxytocin is a nonapeptide involved in a wide range of physiologic and behavioral functions. Until recently, it was believed that an unmodified oxytocin sequence was present in all placental mammals. This study analyzed oxytocin (OXT) in 29 primate species and the oxytocin receptor (OXTR) in 21 of these species. We report here three novel OXT forms in the New World monkeys, as well as a more extensive distribution of a previously described variant (Leu8Pro). In structural terms, these OXTs share the same three low-energy conformations in solution during molecular dynamic simulations, with subtle differences in their side chains. A consistent signal of positive selection was detected in the Cebidae family, and OXT position 8 showed a statistically significant (P = 0.013) correlation with litter size. Several OXTR changes were identified, some of them promoting gain or loss of putative phosphorylation sites, with possible consequences for receptor internalization and desensitization. OXTR amino acid sites are under positive selection, and intramolecular and intermolecular coevolutionary processes with OXT were also detected. We suggest that some New World monkey OXT-OXTR forms can be correlated to male parental care through the increase of cross-reactivity with its correlated vasopressin system.

CrossTope: a curate repository of 3D structures of immunogenic peptide: MHC complexes.

The CrossTope is a highly curate repository of three-dimensional structures of peptide:major histocompatibility complex (MHC) class I complexes (pMHC-I). The complexes hosted by this databank were obtained in protein databases and by large-scale in silico construction of pMHC-I structures, using a new approach developed by our group. At this moment, the database contains 182 'non-redundant' pMHC-I complexes from two human and two murine alleles. A web server provides interface for database query. The user can download (i) structure coordinate files and (ii) topological and charges distribution maps images from the T-cell receptor-interacting surface of pMHC-I complexes. The retrieved structures and maps can be used to cluster similar epitopes in cross-reactivity approaches, to analyse viral escape mutations in a structural level or even to improve the immunogenicity of tumour antigens. Database URL: http://www.crosstope.com.br.

Immunogenic epitopes of Hantaviruses' N protein are restricted to conserved regions.

The Bunyaviridae virus family is composed by five genera, of which the Hantavirus genus is one of the most important representatives. Occasionally, these viruses can be transmitted to humans, giving rise to severe diseases that present high mortality rates. We analyzed the amino acid sequences of the nucleocapsid (N) proteins of 34 different hantaviruses to investigate the potential mechanisms involved in immunogenicity against hantaviruses. Immunogenic epitopes described in the literature through experimental analyses for Sin Nombre (SNV), Puumala (PUUV), and Hantaan (HTNV) viruses' species were retrieved. We identified and characterized the regions believed to be responsible for the induction of immune response in hosts. We found that N protein epitopes described in the literature for PUUV, SNV and HTNV viruses are all located in highly conserved regions of the protein. The high conservation of these regions suggests that a cross-reactive immune response among different hantaviruses can be induced.