Explore the vast range of titles available.

Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.

An effective vaccine against syphilis could aid current control measures to reduce the incidence of infection. Protective immunity from the syphilis agent, Treponema pallidum subsp. pallidum (T. pallidum), is associated with pathogen clearance by phagocytosis, supporting that immunization with an effective vaccine candidate should elicit opsonic antibodies to key epitopes at the host-pathogen interface. The T. pallidumrepeat (Tpr) proteins are putative β-barrel outer membrane porins with ten predicted extracellular loops. Here, we immunized three groups of eight rabbits with either a combination of three recombinant variants of the full-length TprC antigen, the TprD2 protein, or the conserved NH2-terminal region of TprK, with the latter antigen already known to induce incomplete protection in immunized rabbits. Compared to unimmunized controls, rabbits immunized with the three TprC variants or the TprK fragment exhibited attenuated primary chancres, reduced treponemal burden at the challenge sites, and limited pathogen dissemination to lymph nodes. Immunization with TprD2, alone did not produce comparable results. Strong humoral and cellular responses against TprC and TprK were elicited by immunization, and functional analyses supported the induction of opsonizing antibodies. Epitope mapping performed using TprC- and TprK-specific synthetic peptides and phage immunoprecipitation-sequencing identified a subset of highly reactive sequences and demonstrated immunity to predicted surface-exposed epitopes across multiple Tpr paralogs, which explained the significant, albeit incomplete protection measured post-challenge. These data advance TprC and TprK as syphilis vaccine candidates and highlight several correlates of their protection that deserve further examination.

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and diagnostics. In vaccine design, the intent is to identify the minimal epitope of an antigen that can elicit an immune response and avoid off-target effects. For prognostics and diagnostics, the epitope-antibody interaction is exploited to measure antigens associated with disease outcomes. Experimental methods such as X-ray crystallography, cryo-electron microscopy, and peptide arrays are used widely to map epitopes but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations of current methods and the potential for machine learning to deepen interpretation and increase feasibility of these methods. We also propose how machine learning can be employed to refine epitope prediction to address the apparent promiscuity of polyreactive antibodies and the challenge of defining conformational epitopes. We highlight the impact of machine learning on our current understanding of epitopes and its potential to guide the design of therapeutic interventions with more predictable outcomes.

An important issue for the performance and specificity of an antibody is the nature of the binding to its protein target, including if the recognition involves linear or conformational epitopes. Here, we dissect polyclonal sera by creating epitope-specific antibody fractions using a combination of epitope mapping and an affinity capture approach involving both synthesized peptides and recombinant protein fragments. This allowed us to study the relative amounts of antibodies to linear and conformational epitopes in the polyclonal sera as well as the ability of each antibody-fraction to detect its target protein in Western blot assays. The majority of the analyzed polyclonal sera were found to have most of the target-specific antibodies directed towards linear epitopes and these were in many cases giving Western blot bands of correct molecular weight. In contrast, many of the antibodies towards conformational epitopes did not bind their target proteins in the Western blot assays. The results from this work have given us insights regarding the nature of the antibody response generated by immunization with recombinant protein fragments and has demonstrated the advantage of using antibodies recognizing linear epitopes for immunoassay involving wholly or partially denatured protein targets.

Fibrinolytic drugs, particularly streptokinase (SK), are crucial for the management of blood clotting disorders. However, SK’s bacterial origin triggers immune responses that generate neutralizing antibodies, diminishing its effectiveness. This study aimed to identify and eliminate B-cell epitopes in SK to reduce its immunogenicity through targeted point mutations. By utilizing advanced in silico tools, we predicted the SK structure and identified both linear and conformational B-cell epitopes. Hot spot residues within these epitopes were identified using a combination of epitope prediction algorithms, molecular dynamics and docking simulations, conservancy analysis, and propensity scales. Our innovative approach suggested key antigenic residues E53, D174, and S258 that were strategically mutated to minimize immunogenicity. Immuno-informatics tools indicated that the modified SK could exhibit a significantly reduced immunogenic profile. Molecular dynamics simulations supported the structural integrity of the modified SK, and docking studies suggested its preserved interaction potential with plasminogen. Our results propose that the mutein E53M-D174M-S258W could reduce the immunogenic response, thus improving its therapeutic potential.

Nanobodies are single-domain antibodies of camelid origin. We generated nanobodies against the vertebrate nuclear pore complex (NPC) and used them in STORM imaging to locate individual NPC proteins with <2 nm epitope-label displacement. For this, we introduced cysteines at specific positions in the nanobody sequence and labeled the resulting proteins with fluorophore-maleimides. As nanobodies are normally stabilized by disulfide-bonded cysteines, this appears counterintuitive. Yet, our analysis showed that this caused no folding problems. Compared to traditional NHS ester-labeling of lysines, the cysteine-maleimide strategy resulted in far less background in fluorescence imaging, it better preserved epitope recognition and it is site-specific. We also devised a rapid epitope-mapping strategy, which relies on crosslinking mass spectrometry and the introduced ectopic cysteines. Finally, we used different anti-nucleoporin nanobodies to purify the major NPC building blocks – each in a single step, with native elution and, as demonstrated, in excellent quality for structural analysis by electron microscopy. The presented strategies are applicable to any nanobody and nanobody-target. Antibodies not only protect humans and other animals against disease-causing bacteria and viruses. They can also be used as tools for medical diagnostics and basic research. Conventional antibodies consist of light and heavy protein chains, and both are required to bind to target molecules (or antigens). Alpacas, llamas and camels, however, possess simpler antibodies that lack light chains and bind to antigens via a single protein domain. Such domains can be produced in \"re-programmed\" bacteria and are then called nanobodies. Compared to normal antibodies, nanobodies are 10-fold smaller, which is of great advantage in virtually all practical applications. Pleiner et al. made nanobodies against the nuclear pore complex (or NPC for short) – a nanoscopic machine for transporting large biological molecules in and out of the cell’s nucleus. These nanobodies can be linked to dyes called fluorophores and then used to stain NPCs so that they can be observed under a microscope. When fluorophores were attached, in the traditional way, via the amino acid lysine, all tested nanobodies performed poorly in fluorescence microscopy - pointing to a systematic problem. Pleiner et al. therefore explored an alternative, namely to label nanobodies via engineered cysteines. This was counterintuitive, because nanobodies contain already two other cysteines that must not be modified and that normally form a stabilizing “disulfide” bond. Pleiner et al. found, however, that the labeling reaction is absolutely specific for the engineered surface cysteines when it is performed at low temperature. This strategy consistently yielded imaging reagents that could effectively deliver fluorophores as close as 1-2 nanometers to their antigens. Nanobodies labeled in this way are therefore ideal to exploit the full potential of super-resolution microscopy. The engineered surface cysteines proved also useful as \"position sensors\" to report which region of an antigen is actually contacted by a given nanobody. Nanobodies are also used to purify protein complexes from crude cell extracts by a method called affinity chromatography. Previously, nanobodies were chemically attached to an insoluble matrix, and bound protein complexes were released under conditions that destroy interactions between proteins. Pleiner et al. now replaced the destructive step with a step that uses an enzyme to cut a bond and gently detach the nanobody (along with any bound protein complex) from the matrix. Bound protein complexes thus stay intact and can be studied further. In the future, this strategy can be applied to nanobodies that recognize tags commonly added to proteins (i.e. GFP) to isolate virtually any protein complex for functional assays or structural analyses.

The monoclonal antibody (mAb) IP5B11, which is used worldwide for the diagnosis of viral haemorrhagic septicaemia (VHS) in fish, reacts with all genotypes of VHS virus (VHSV). The mAb exceptionally also reacts with the carpione rhabdovirus (CarRV). Following next generation genome sequencing of CarRV and N protein sequence alignment including five kinds of fish novirhabdoviruses, the epitope recognized by mAb IP5B11 was identified. Dot blot analysis confirmed the epitope of mAb IP5B11 to be associated with the region N219 to N233 of the N protein of VHSV. Phylogenetic analysis identified CarRV as a new member of the fish novirhabdoviruses.

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world’s population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.