Structural basis of immunogenicity

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.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I

Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.

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.

In Silico Validation of D7 Salivary Protein-derived B- and T-cell Epitopes of Aedes aegypti as Potential Vaccine to Prevent Transmission of Flaviviruses and Togaviruses to Humans

Mosquito (Aedes aegyptii) salivary proteins play a crucial role in facilitating viral transmission from vector-to-host due to their role in facilitating the "blood meal" of the vector. Three main proteins, D7, aegyptin and Sialokinin play a role in this process. Using in-silico programs, we identified B- and T-cell epitopes in the mosquito salivary proteins D7 long and short form. T-cell epitopes with high affinity to the most prevalent HLA MHC class-I supertypes among different population groups was chosen. It is our postulate that these epitopes could be successful in eliciting B and T cell responses, which would decrease the vector blood meal efficiency and hence protect against host infection by certain viruses. These include causative agents like Dengue viruses, Chikungunya virus, Zika and Yellow fever viruses. These viruses are of major public health importance in several countries in the Americas, Asia and Africa. Experimental evidence exists in previously published literature showing the protective effect of antibodies to certain salivary proteins in susceptible hosts. A novel approach of immunizing humans against the vector proteins to reduce transmission of viruses is now under investigation in several laboratories. We have identified the following two B cell epitopes LAALHVTAAPLWDAKDPEQF one from D7L and the other TSEYPDRQNQIEELNKLCKN from D7S. Likewise, two T cell epitopes MTSKNELDV one from D7L and the other YILCKASAF from D7S with affinity to the predominant MHC class-I supertypes were identified towards evaluation as potential vaccine.

Association of HLA-B*5801 allele and allopurinol-induced Stevens Johnson syndrome and toxic epidermal necrolysis: a systematic review and meta-analysis

Background

Despite some studies suggesting a possible association between human leukocyte antigen, HLA-B*5801 and allopurinol induced Stevens-Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), the evidence of association and its magnitude remain inconclusive. This study aims to systematically review and meta-analyze the association between HLA-B*5801 allele and allopurinol-induced SJS/TEN.

Methods

A comprehensive search was performed in databases including MEDLINE, Pre-MEDLINE, Cochrane Library, EMBASE, International Pharmaceutical Abstracts (IPA), CINAHL, PsychInfo, the WHO International, Clinical Trial Registry, and ClinicalTrial.gov from their inceptions to June 2011. Only studies investigating association between HLA-B*5801 with allopurinol-induced SJS/TEN were included. All studies were extracted by two independent authors. The primary analysis was the carrier frequency of HLA-B*5801 comparison between allopurinol-induced SJS/TEN cases and each comparative group. The pooled odds ratios were calculated using a random effect model.

Results

A total of 4 studies with 55 SJS/TEN cases and 678 matched-controls (allopurinol-tolerant control) was identified, while 5 studies with 69 SJS/TEN cases and 3378 population-controls (general population) were found. SJS/TEN cases were found to be significantly associated with HLA-B*5801 allele in both groups of studies with matched-control (OR 96.60, 95%CI 24.49-381.00, p < 0.001) and population-control (OR 79.28, 95%CI 41.51-151.35, p < 0.001). Subgroup analysis for Asian and Non-Asian population yielded similar findings.

Conclusion

We found a strong and significant association between HLA-B*5801 and allopurinol-induced SJS/TEN. Therefore, HLA-B*5801 allele screening may be considered in patients who will be treated with allopurinol.

High-throughput engineering and analysis of peptide binding to class II MHC

Class II major histocompatibility complex (MHC-II) proteins govern stimulation of adaptive immunity by presenting antigenic peptides to CD4+ T lymphocytes. Many allelic variants of MHC-II exist with implications in peptide presentation and immunity; thus, high-throughput experimental tools for rapid and quantitative analysis of peptide binding to MHC-II are needed. Here, we present an expression system wherein peptide and MHC-II are codisplayed on the surface of yeast in an intracellular association-dependent manner and assayed by flow cytometry. Accordingly, the relative binding of different peptides and/or MHC-II variants can be assayed by genetically manipulating either partner, enabling the application of directed evolution approaches for high-throughput characterization or engineering. We demonstrate the application of this tool to map the side-chain preference for peptides binding to HLA-DR1 and to evolve novel HLA-DR1 mutants with altered peptide-binding specificity.

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.

Identity tests: determination of cell line cross-contamination

Cell line cross-contamination is a phenomenon that arises as a result of the continuous cell line culture. It has been estimated that around 20% of the cell lines are misidentified, therefore it is necessary to carry out quality control tests for the detection of this issue. Since cell line cross-contamination discovery, different methods have been applied, such as isoenzyme analysis for inter-species cross-contamination; HLA typing, and DNA fingerprinting using short tandem repeat and a variable number of tandem repeat for intra-species cross-contamination. The cell banks in this sense represent the organizations responsible for guaranteeing the authenticity of cell lines for future research and clinical uses.

The immunogenicity of a conformationally restricted peptide mimetic of meningococcal lipooligosaccharide

Life-threatening meningitis and septicaemia caused by Neisseria meningitidis are a public health priority, and their prevention by vaccination is a major objective. Meningococcal capsular polysaccharide-based vaccines are effective against the major invasive serogroups, except for serogroup B, the capsule of which mimics human polysaccharides and is poorly immunogenic. An alternative vaccine candidate that has the potential to offer cross-protection against antigenically diverse meningococci is the lipooligosaccharide (LOS). The structurally constrained peptide mimetic, C22, of a bactericidal antibody epitope within LOS was previously shown to elicit cross-reactive antibodies to meningococcal LOS when complexed to NeutrAvidintrade mark as a carrier protein. The immunogenicity of this antigen in H-2(d) (BALB/c) and H-2(k) (C3H/HeN) haplotype mice was further investigated. Anti-LOS immunoglobulin G (IgG) antibody titres increased with the vaccine dose and correlated with the anti-C22 peptide antibody titres in both haplotypes. Antigen-stimulated Th1/Th2 cytokine secretion by splenocytes and antibody isotypes indicated a Th2-type immune response with IgG1 antibodies and a low titre of IgG2b. There was no serum bactericidal activity observed against the meningococcus.

MHC class I alleles and their exploration of the antigen-processing machinery

At the cell surface, major histocompatibility complex (MHC) class I molecules present fragments of intracellular antigens to the immune system. This is the end result of a cascade of events initiated by multiple steps of proteolysis. Only a small part of the fragments escapes degradation by interacting with the peptide transporter associated with antigen presentation and is translocated into the endoplasmic reticulum lumen for binding to MHC class I molecules. Subsequently, these newly formed complexes can be transported to the plasma membrane for presentation. Every step in this process confers specificity and determines the ultimate result: presentation of only few fragments from a given antigen. Here, we introduce the players in the antigen processing and presentation cascade and describe their specificity and allelic variation. We highlight MHC class I alleles, which are not only different in sequence but also use different aspects of the antigen presentation pathway to their advantage: peptide acquaintance.

The design and implementation of the immune epitope database and analysis resource

Epitopes are defined as parts of antigens interacting with receptors of the immune system. Knowledge about their intrinsic structure and how they affect the immune response is required to continue development of techniques that detect, monitor, and fight diseases. Their scientific importance is reflected in the vast amount of epitope-related information gathered, ranging from interactions between epitopes and major histocompatibility complex molecules determined by X-ray crystallography to clinical studies analyzing correlates of protection for epitope based vaccines. Our goal is to provide a central resource capable of capturing this information, allowing users to access and connect realms of knowledge that are currently separated and difficult to access. Here, we portray a new initiative, "The Immune Epitope Database and Analysis Resource." We describe how we plan to capture, structure, and store this information, what query interfaces we will make available to the public, and what additional predictive and analytical tools we will provide.

Replacement of the membrane proximal region of I-Ad MHC class II molecule with I-E-derived sequences promotes production of an active and stable soluble heterodimer without altering peptide-binding specificity

The MHC class II molecule I-A is the murine homologue of HLA-DQ in humans. The I-A and DQ heterodimers display considerable heterodimer instability compared with their I-E and HLA-DR counterparts. This isotype-specific behavior makes the production of soluble I-A and DQ molecules very difficult. We have developed a strategy for production of soluble I-A(d) molecules involving expression of I-A(d) as a glycosil phosphatidyl inositol (PI) anchored chimera in Chinese Hamster Ovary (CHO) cells. The regions comprising the membrane proximal segments of I-A(d) alpha and beta chains were substituted for the corresponding regions of I-E, and the derived constructs were expressed in CHO cells. Procedures for purification of the soluble class II molecules were optimized and the WT and chimeric molecule were compared for structure, biochemical stability and functionality. Our analysis revealed that the substitutions in the membrane proximal domains improved cell surface expression and thermal stability of I-A(d) without altering the peptide binding specificity of the class II molecule. The results suggest that similar strategies could be used to increase the stability of other unstable class II molecules for in vitro studies.