CED: a conformational epitope database

Effect of lactic acid bacteria fermentation on cow milk allergenicity and antigenicity: A review

Cow milk is a major allergenic food. The potential prevention and treatment effects of lactic acid bacteria (LAB)-fermented dairy products on allergic symptoms have garnered considerable attention. Cow milk allergy (CMA) is mainly attributed to extracellular and/or cell envelope proteolytic enzymes with hydrolysis specificity. Numerous studies have demonstrated that LAB prevents the risk of allergies by modulating the development and regulation of the host immune system. Specifically, LAB and its effectors can enhance intestinal barrier function and affect immune cells by interfering with humoral and cellular immunity. Fermentation hydrolysis of allergenic epitopes is considered the main mechanism of reducing CMA. This article reviews the linear epitopes of allergens in cow milk and the effect of LAB on these allergens and provides insight into the means of predicting allergenic epitopes by conventional laboratory analysis methods combined with molecular simulation. Although LAB can reduce CMA in several ways, the mechanism of action remains partially clarified. Therefore, this review additionally attempts to summarize the main mechanism of LAB fermentation to provide guidance for establishing an effective preventive and treatment method for CMA and serve as a reference for the screening, research, and application of LAB-based intervention.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Detection of a natural antibody targeting the shed ectodomain of BP180 in mice

BACKGROUND

Pemphigoid diseases are characterized by subepidermal blister formation accompanied by autoantibodies targeting skin component molecules, such as BP180. It is suggested that an epitope-phenotype correlation exists among autoantibodies recognizing BP180. However, it is unclear which regions of BP180 are likely targets for autoantibodies.

OBJECTIVE

To elucidate the portions of BP180 where antibodies tend to react under the breakdown of immune tolerance.

METHODS

We immunized mice with full-length mouse BP180 (mBP180) to produce anti-mBP180 antibodies. Using the immunized mice, hybridoma cells were established to produce anti-mBP180 antibodies. We analyzed the characteristics of the anti-mBP180 antibodies that were produced in terms of epitopes, immunoglobulin subclasses, and somatic hypermutations.

RESULTS

Hybridoma cells derived from immunized mice with full-length mBP180 produced antibodies targeting the intracellular domain (IC) and the shed ectodomain (EC) of mBP180. Using the domain-deleted mBP180 recombinant protein, we revealed that monoclonal anti-mBP180 EC antibodies react to neoepitopes on the 13th collagenous region of cleaved mBP180, which corresponds to the epitopes of linear IgA bullous dermatosis antibodies in human BP180. Furthermore, the subclasses of these antibodies could be distinguished by epitope: The subclass of the anti-mBP180 IC monoclonal antibodies was IgG, whereas that of the anti-mBP180 EC antibodies was IgM. Of note, a clone of these IgM mBP180 EC antibodies was a germline antibody without somatic hypermutation, which is also known as a natural antibody.

CONCLUSION

These data suggest that mice potentially have natural antibodies targeting the neoepitopes of cleaved mBP180 EC.

Dynamic structural analysis-based epitope prediction of Exendin-4 in aqueous solution

The study of epitopes has a broad range of applications in drug discovery, vaccine design, and immunotherapy. In this study, an epitope prediction method was developed based on the dynamic structure of protein antigens. Solvent accessible surface area, charge, and root mean square fluctuation were introduced as the key residue property parameters. The epitope prediction algorithm was established by constructing a three-parameter complex metrics of seven-peptide groups. The method was applied to predict the epitopes of Exendin-4, an effective antidiabetic drug. The epitopes of both the natural and C-terminal amidated forms of Exendin-4 were predicted and compared in their folded and intermediate states. In the folded state, the epitopes of natural Exendin-4 (His1-Phe6 and Asp9-Val19) were found to be nearly identical to the epitopes of C-terminal aminated Exendin-4 (His1-Thr7 and Asp9-Val19). In the intermediate state, however, the epitopes of natural Exendin-4 (His1-Gly4, Phe6 and Lys12-Arg20) covered fewer amino acids than the epitopes of C-terminal aminated Exendin-4 (His1-Gly4, Phe6, Asp9-Val19 and Trp25-Lys27). The comparison with the results from other prediction tools demonstrates the reliability of our predicted epitopes of Exendin-4.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consists of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers at: http://www.biolscience.cn/DeepLBCEPred/.

B-Cell Epitope Predictions Using Computational Methods

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

In silico epitope-based vaccine design against influenza a neuraminidase protein: Computational analysis established on B- and T-cell epitope predictions

Objective

Influenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.

Methods

The present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.

Results

Computational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.

Conclusions

We propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.

Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.

Plastic Antibodies Mimicking the ACE2 Receptor for Selective Binding of SARS-CoV-2 Spike

Molecular imprinting has proven to be a versatile and simple strategy to obtain selective materials also termed "plastic antibodies" for a wide variety of species, i.e., from ions to macromolecules and viruses. However, to the best of the authors' knowledge, the development of epitope-imprinted polymers for selective binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not reported to date. An epitope from the SARS-CoV-2 spike protein comprising 17 amino acids is used as a template during the imprinting process. The interactions between the epitope template and organosilane monomers used for the polymer synthesis are predicted via molecular docking simulations. The molecularly imprinted polymer presents a 1.8-fold higher selectivity against the target epitope compared to non-imprinted control polymers. Rebinding studies with pseudoviruses containing SARS-CoV-2 spike protein demonstrate the superior selectivity of the molecularly imprinted matrices, which mimic the interactions of angiotensin-converting enzyme 2 receptors from human cells. The obtained results highlight the potential of SARS-CoV-2 molecularly imprinted polymers for a variety of applications including chem/biosensing and antiviral delivery.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

VirVACPRED: A Web Server for Prediction of Protective Viral Antigens

Viral antigens are key in the development of vaccines that prevent or eradicate infections caused by these pathogens. Bioinformatics tools are modern alternatives that facilitate the discovery of viral antigens, reducing the costs of experimental assays. We developed a bioinformatics tool called VirVACPRED, which is highly efficient in predicting viral antigens. In this study, we obtained a model based on the gradient boosting classifier, which showed high performance during the training, leave-one-out cross-validation (accuracy = 0.7402, sensitivity = 0.7319, precision = 0.7503, F1 = 0.7251, kappa = 0.4774, Matthews correlation coefficient = 0.4981) and testing (accuracy = 0.8889, sensitivity = 1.0, precision = 0.8276, F1 = 0.9057, kappa = 0.7734, Matthews correlation coefficient = 0.7941). VirVACPRED is a robust tool that can be of great help in the search and proposal of new viral antigens, which can be considered in the development of future vaccines against infections caused by viruses.

virusMED: an atlas of hotspots of viral proteins

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Epitope-Based Immunoinformatic Approach on Heat Shock 70 kDa Protein Complex of Cryptococcus neoformans var. grubii

Introduction

Cryptococcosis is a ubiquitous opportunistic fungal disease caused by Cryptococcus neoformans var. grubii. It has high global morbidity and mortality among HIV patients and non-HIV carriers with 99% and 95%, respectively. Furthermore, the increasing prevalence of undesired toxicity profile of antifungal, multidrug-resistant organisms and the scarcity of FDA-authorized vaccines were the hallmark in the present days. This study was undertaken to design a reliable epitope-based peptide vaccine through targeting highly conserved immunodominant heat shock 70 kDa protein of Cryptococcus neoformans var. grubii that covers a considerable digit of the world population through implementing a computational vaccinology approach.

Materials and Methods

A total of 38 sequences of Cryptococcus neoformans var. grubii's heat shock 70 kDa protein were retrieved from the NCBI protein database. Different prediction tools were used to analyze the aforementioned protein at the Immune Epitope Database (IEDB) to discriminate the most promising T-cell and B-cell epitopes. The proposed T-cell epitopes were subjected to the population coverage analysis tool to compute the global population's coverage. Finally, the T-cell projected epitopes were ranked based on their binding scores and modes using AutoDock Vina software. Results and Discussion. The epitopes (ANYVQASEK, QSEKPKNVNPVI, SEKPKNVNPVI, and EKPKNVNPVI) had shown very strong binding affinity and immunogenic properties to B-cell. (FTQLVAAYL, YVYDTRGKL) and (FFGGKVLNF, FINAQLVDV, and FDYALVQHF) exhibited a very strong binding affinity to MHC-I and MHC-II, respectively, with high population coverage for each, while FYRQGAFEL has shown promising results in terms of its binding profile to MHC-II and MHC-I alleles and good strength of binding when docked with HLA-C∗12:03. In addition, there is massive global population coverage in the three coverage modes. Accordingly, our in silico vaccine is expected to be the future epitope-based peptide vaccine against Cryptococcus neoformans var. grubii that covers a significant figure of the entire world citizens.

Mechanisms of Immunotoxicity: Stressors and Evaluators

The immune system defends the body against certain tumor cells and against foreign agents such as fungi, parasites, bacteria, and viruses. One of its main roles is to distinguish endogenous components from non-self-components. An unproperly functioning immune system is prone to primary immune deficiencies caused by either primary immune deficiencies such as genetic defects or secondary immune deficiencies such as physical, chemical, and in some instances, psychological stressors. In the manuscript, we will provide a brief overview of the immune system and immunotoxicology. We will also describe the biochemical mechanisms of immunotoxicants and how to evaluate immunotoxicity.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Molecular recognition of structurally disordered Pro/Ala-rich sequences (PAS) by antibodies involves an Ala residue at the hot spot of the epitope

Pro/Ala-rich sequences (PAS) are polypeptides that were developed as a biological alternative to poly-ethylene glycol (PEG) to generate biopharmaceuticals with extended plasma half-life. Like PEG, PAS polypeptides are conformationally disordered and show high solubility in water. Devoid of any charged or prominent hydrophobic side chains, these biosynthetic polymers represent an extreme case of intrinsically disordered proteins. Despite lack of immunogenicity of PAS tags in numerous animal studies we now succeeded in generating monoclonal antibodies (MAbs) against three different PAS versions. To this end, mice were immunized with a PAS#1, P/A#1 or APSA 40mer peptide conjugated to keyhole limpet hemocyanin as highly immunogenic carrier protein. In each case, one MAb with high binding activity and specificity towards a particular PAS motif was obtained. The apparent affinity was strongly dependent on the avidity effect and most pronounced for the bivalent MAb when interacting with a long PAS repeat. X-ray structural analysis of four representative anti-PAS Fab fragments in complex with their cognate PAS epitope peptides revealed interactions dominated by hydrogen bond networks involving the peptide backbone as well as multiple Van der Waals contacts arising from intimate shape complementarity. Surprisingly, Ala, the L-amino acid with the smallest side chain, emerged as a crucial feature for epitope recognition, contributing specific contacts at the center of the paratope in several anti-PAS complexes. Apart from these insights into how antibodies can recognize feature-less peptides without secondary structure, the MAbs characterized in this study offer valuable reagents for the preclinical and clinical development of PASylated biologics.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational Immune Proteomics Approach to Target COVID-19

Progress of the omics platforms widens their application to diverse fields, including immunology. This enables a deeper level of knowledge and the provision of a huge amount of data for which management and fruitful integration with the past evidence requires a steadily growing computational effort. In light of this, immunoinformatics emerges as a new discipline placed in between the traditional lab-based investigations and the computational analysis of the biological data. Immunoinformatics make use of tailored bioinformatics tools and data repositories to facilitate the analysis of data from a plurality of disciplines and help drive novel research hypotheses and in silico screening investigations in a fast, reliable, and cost-effective manner. Such computational immunoproteomics studies may as well prepare and guide lab-based investigations, representing valuable technology for the investigation of novel pathogens, to tentatively evaluate specificity of diagnostic products, to forecast on potential adverse effects of vaccines and to reduce the use of animal models. The present manuscript provides an overview of the COVID-19 pandemic and reviews the state of the art of the omics technologies employed in fighting SARS-CoV-2 infections. A comprehensive description of the immunoinformatics approaches and its potential role in contrasting COVID-19 pandemics is provided.

The Complex Molecules Detector (CMOLD): A Fluidic-Based Instrument Suite to Search for (Bio)chemical Complexity on Mars and Icy Moons

Organic chemistry is ubiquitous in the Solar System, and both Mars and a number of icy satellites of the outer Solar System show substantial promise for having hosted or hosting life. Here, we propose a novel astrobiologically focused instrument suite that could be included as scientific payload in future missions to Mars or the icy moons: the Complex Molecules Detector, or CMOLD. CMOLD is devoted to determining different levels of prebiotic/biotic chemical and structural targets following a chemically general approach (i.e., valid for both terrestrial and nonterrestrial life), as well as their compatibility with terrestrial life. CMOLD is based on a microfluidic block that distributes a liquid suspension sample to three instruments by using complementary technologies: (1) novel microscopic techniques for identifying ultrastructures and cell-like morphologies, (2) Raman spectroscopy for detecting universal intramolecular complexity that leads to biochemical functionality, and (3) bioaffinity-based systems (including antibodies and aptamers as capture probes) for finding life-related and nonlife-related molecular structures. We highlight our current developments to make this type of instruments flight-ready for upcoming Mars missions: the Raman spectrometer included in the science payload of the ESAs Rosalind Franklin rover (Raman Laser Spectrometer instrument) to be launched in 2022, and the biomarker detector that was included as payload in the NASA Icebreaker lander mission proposal (SOLID instrument). CMOLD is a robust solution that builds on the combination of three complementary, existing techniques to cover a wide spectrum of targets in the search for (bio)chemical complexity in the Solar System.

Development of multiepitope subunit protein vaccines against Toxoplasma gondii using an immunoinformatics approach

Approximately one-third of the world’s human population is estimated to have been exposed to the parasite Toxoplasma gondii. Its prevalence is reportedly high in Ethiopia (74.80%) and Zimbabwe (68.58%), and is 40.40% in Nigeria. The adverse effect of this parasite includes a serious congenital disease in the developing fetus of pregnant women. After several efforts to eliminate the disease, only one licensed vaccine ‘Toxovax’ has been used to avoid congenital infections in sheep. The vaccine has been adjudged expensive coupled with adverse effects and short shelf life. The potential of vaccine to likely revert to virulent strain is a major reason why it has not been found suitable for human use, hence the need for a vaccine that will induce T and B memory cells capable of eliciting longtime immunity against the infection. This study presents immunoinformatics approaches to design a T. gondii-oriented multiepitope subunit vaccine with focus on micronemal proteins for the vaccine construct. The designed vaccine was subjected to antigenicity, immunogenicity, allergenicity and physicochemical parameter analyses. A 657-amino acid multiepitope vaccine was designed with the antigenicity probability of 0.803. The vaccine construct was classified as stable, non-allergenic, and highly immunogenic, thereby indicating the safety of the vaccine construct for human use.

Molecular shape as a key source of prebiotic information

One of the most striking features of a living system is the self-sustaining functional inner organization, which is only possible when a source of internal references is available from which the system is able to self-organize components and processes. Internal references are intrinsically related to biological information, which is typically understood as genetic information. However, the organization in living systems supports a diversity of intricate processes that enable life to endure, adapt and reproduce because of this organization. In a biological context, information refers to a complex relationship between internal architecture and system functionality. Nongenetic processes, such as conformational recognition, are not considered biological information, although they exert important control over cell processes. In this contribution, we discuss the informational nature in the recognition of molecular shape in living systems. Thus, we highlight supramolecular matching as having a theoretical key role in the origin of life. Based on recent data, we demonstrate that the transfer of molecular conformation is a very likely dynamic of prebiotic information, which is closely related to the origin of biological homochirality and biogenic systems. In light of the current hypothesis, we also revisit the central dogma of molecular biology to assess the consistency of the proposal presented here. We conclude that both spatial (molecular shape) and sequential (genetic) information must be represented in this biological paradigm.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

Immunoinformatics and Epitope Prediction

With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity.This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli (ETEC). Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design. The predicted peptides were analyzed for conservation and population coverage. HLA distribution analysis for predicted epitopes identified efficient MHC binders. Epitopes were further tested using computational docking studies to bind in MHC-I molecule cleft. The predicted epitopes were conserved and covered more than 80% of the world population.

EPCES and EPSVR: Prediction of B-Cell Antigenic Epitopes on Protein Surfaces with Conformational Information

Accurate prediction of discontinuous antigenic epitopes is important for immunologic research and medical applications, but it is not an easy problem. Currently, there are only a few prediction servers available, though discontinuous epitopes constitute the majority of all B-cell antigenic epitopes. In this chapter, we describe two online servers, EPCES and EPSVR, for discontinuous epitope prediction. All methods were benchmarked by a curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The servers and all datasets are available at http://sysbio.unl.edu/EPCES/ and http://sysbio.unl.edu/EPSVR/ .

Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning

In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known reference epitope sequence under specific experimental conditions. The peptide sequences were transformed into molecular descriptors of sequence recurrence networks and were mixed under experimental conditions. The new models were generated using 709,100 instances of pair descriptors for query and reference peptide sequences. Using perturbations of the initial descriptors under sequence or assay conditions, 10 transformed features were used as inputs for seven Machine Learning methods. The best model was obtained with random forest classifiers with an Area Under the Receiver Operating Characteristics (AUROC) of 0.981 ± 0.0005 for the external validation series (five-fold cross-validation). The database included information about 83,683 peptides sequences, 1448 epitope organisms, 323 host organisms, 15 types of in vivo processes, 28 experimental techniques, and 505 adjuvant additives. The current model could improve the in silico predictions of epitopes for vaccine design. The script and results are available as a free repository.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

Molecular Design of Peptide-Fc Fusion Drugs

Background:

Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.

Methods:

We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.

Results:

The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.

Conclusion:

Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.

Immunogenicity of Protein Pharmaceuticals

Protein therapeutics have drastically changed the landscape of treatment for many diseases by providing a regimen that is highly specific and lacks many off-target toxicities. The clinical utility of many therapeutic proteins has been undermined by the potential development of unwanted immune responses against the protein, limiting their efficacy and negatively impacting its safety profile. This review attempts to provide an overview of immunogenicity of therapeutic proteins, including immune mechanisms and factors influencing immunogenicity, impact of immunogenicity, preclinical screening methods, and strategies to mitigate immunogenicity.

Canine CD117-Specific Antibodies with Diverse Binding Properties Isolated from a Phage Display Library Using Cell-Based Biopanning

CD117 (c-Kit) is a tyrosine kinase receptor that is overexpressed in multiple dog tumors. There is 100% homology between the juxtamembrane domain of human and canine CD117, and many cancer-causing mutations occur in this region in both species. Thus, CD117 is an important target for cancer treatment in dogs and for comparative oncology studies. Currently, there is no monoclonal antibody (mAb) specifically designed to target the exposed region of canine CD117, although there exist some with species cross-reactivity. We panned a naïve phage display library to isolate antibodies against recombinant CD117 on whole cells. Several mAbs were isolated and were shown to bind recombinant canine CD117 at low- to sub-nanomolar affinity. Additionally, binding to native canine CD117 was confirmed by immunohistochemistry and by flow cytometry. Competitive binding assays also identified mAbs that competed with the CD117 receptor-specific ligand, the stem cell factor (SCF). These results show the ability of our cell-based biopanning strategy to isolate a panel of antibodies that have varied characteristics when used in different binding assays. These in vitro/ex vivo assessments suggest that some of the isolated mAbs might be promising candidates for targeting overexpressed CD117 in canine cancers for different useful applications.

H1N1 influenza virus epitopes classified by monoclonal antibodies

Epitopes serve an important role in influenza infection. It may be useful to screen universal influenza virus vaccines, analyzing the epitopes of multiple subtypes of the hemagglutinin (HA) protein. A total of 40 monoclonal antibodies (mAbs) previously obtained from flu virus HA antigens (development and characterization of 40 mAbs generated using H1N1 influenza virus split vaccines were previously published) were used to detect and classify mAbs into distinct flu virus sub-categories using the ELISA method. Following this, the common continuous amino acid sequences were identified by multiple sequence alignment analysis with the GenBank database and DNAMAN software, for use in predicting the epitopes of the HA protein. Synthesized peptides of these common sequences were prepared, and used to verify and determine the predicted linear epitopes through localization and distribution analyses. With these methods, nine HA linear epitopes distributed among different strains of influenza virus were identified, which included three from influenza A, four from 2009 H1N1 and seasonal influenza, and two from H1. The present study showed that considering a combination of the antigen-antibody reaction specificity, variation in the influenza virus HA protein and linear epitopes may present a useful approach for designing effective multi-epitope vaccines. Furthermore, the study aimed to clarify the cause and pathogenic mechanism of influenza virus HA-induced flu, and presents a novel idea for identifying the epitopes of other pathogenic microorganisms.

Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines .

Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments.

Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade.

Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.

In silico methods for design of biological therapeutics

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.

Calpain-5 Expression in the Retina Localizes to Photoreceptor Synapses

Purpose

We characterize calpain-5 (CAPN5) expression in retinal and neuronal subcellular compartments.

Methods

CAPN5 gene variants were classified using the exome variant server, and RNA-sequencing was used to compare expression of CAPN5 mRNA in the mouse and human retina and in retinoblastoma cells. Expression of CAPN5 protein was ascertained in humans and mice in silico, in mouse retina by immunohistochemistry, and in neuronal cancer cell lines and fractionated central nervous system tissue extracts by Western analysis with eight antibodies targeting different CAPN5 regions.

Results

Most CAPN5 genetic variation occurs outside its protease core; and searches of cancer and epilepsy/autism genetic databases found no variants similar to hyperactivating retinal disease alleles. The mouse retina expressed one transcript for CAPN5 plus those of nine other calpains, similar to the human retina. In Y79 retinoblastoma cells, the level of CAPN5 transcript was very low. Immunohistochemistry detected CAPN5 expression in the inner and outer nuclear layers and at synapses in the outer plexiform layer. Western analysis of fractionated retinal extracts confirmed CAPN5 synapse localization. Western blots of fractionated brain neuronal extracts revealed distinct subcellular patterns and the potential presence of autoproteolytic CAPN5 domains.

Conclusions

CAPN5 is moderately expressed in the retina and, despite higher expression in other tissues, hyperactive disease mutants of CAPN5 only manifest as eye disease. At the cellular level, CAPN5 is expressed in several different functional compartments. CAPN5 localization at the photoreceptor synapse and with mitochondria explains the neural circuitry phenotype in human CAPN5 disease alleles.

Mapping the Energetic Epitope of an Antibody/Interleukin-23 Interaction with Hydrogen/Deuterium Exchange, Fast Photochemical Oxidation of Proteins Mass Spectrometry, and Alanine Shave Mutagenesis

Epitope mapping the specific residues of an antibody/antigen interaction can be used to support mechanistic interpretation, antibody optimization, and epitope novelty assessment. Thus, there is a strong need for mapping methods, particularly integrative ones. Here, we report the identification of an energetic epitope by determining the interfacial hot-spot that dominates the binding affinity for an anti-interleukin-23 (anti-IL-23) antibody by using the complementary approaches of hydrogen/deuterium exchange mass spectrometry (HDX-MS), fast photochemical oxidation of proteins (FPOP), alanine shave mutagenesis, and binding analytics. Five peptide regions on IL-23 with reduced backbone amide solvent accessibility upon antibody binding were identified by HDX-MS, and five different peptides over the same three regions were identified by FPOP. In addition, FPOP analysis at the residue level reveals potentially key interacting residues. Mutants with 3-5 residues changed to alanine have no measurable differences from wild-type IL-23 except for binding of and signaling blockade by the 7B7 anti-IL-23 antibody. The M5 IL-23 mutant differs from wild-type by five alanine substitutions and represents the dominant energetic epitope of 7B7. M5 shows a dramatic decrease in binding to BMS-986010 (which contains the 7B7 Fab, where Fab is fragment antigen-binding region of an antibody), yet it maintains functional activity, binding to p40 and p19 specific reagents, and maintains biophysical properties similar to wild-type IL-23 (monomeric state, thermal stability, and secondary structural features).

SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence

BACKGROUND

The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.

RESULTS

We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa's average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set.

CONCLUSIONS

SEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO^® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Computer-Aided Antibody Design: An Overview

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

Computational Tools for Aiding Rational Antibody Design

Antibodies are a group of proteins responsible for mediating immune reactions in vertebrates. They are able to bind a variety of structural motifs on noxious molecules tagging them for elimination from the organism. As a result of their versatile binding properties, antibodies are currently one of the most important classes of biopharmaceuticals. In this chapter, we discuss how knowledge-based computational methods can aid experimentalists in the development of potent antibodies. When using common experimental methods for antibody development, we often know the sequence of an antibody that binds to our molecule, antigen, of interest. We may also have a structure or model of the antigen. In these cases, computational methods can help by both modeling the antibody and identifying the antibody-antigen contact residues. This information can then play a key role in the rational design of more potent antibodies.

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

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.

In silico structure analysis and epitope prediction of E3 CR1-beta protein of Human Adenovirus E for vaccine design

BACKGROUND

Human Adenoviruses are divided into 7 species of Human Adenovirus A to G based on DNA genome homology. The Human Adenovirus E (HAdVs-E) genome is a linear, double-stranded DNA containing 38 protein-coding genes. Wild-type adenoviruses type E, are linked to a number of slight illnesses. The most important part of HAdVs-E is E3 CR1-beta protein which controls the host immune response and viral attachment.

METHOD

We use numerous bio-informatics and immuno-informatics implements comprising sequence and construction tools for construction of 3D model and epitope prediction for HAdVs-E.

RESULTS

The 3D structure of E3 CR1-beta protein was generated and total of ten antigenic B cell epitopes, 6 MHC class I and 11 MHC class II binding peptides were predicted.

CONCLUSION

The study was carried out to predict antigenic determinants/epitopes of the E3 CR1-beta protein of Human Adenovirus E along with the 3D protein modeling. The study revealed potential T-cell and B-cell epitopes that can raise the desired immune response against E3 CR1-beta protein and useful in developing effective vaccines against HAdVs-E.

Cross-Neutralising Nanobodies Bind to a Conserved Pocket in the Hemagglutinin Stem Region Identified Using Yeast Display and Deep Mutational Scanning

Cross-neutralising monoclonal antibodies against influenza hemagglutinin (HA) are of considerable interest as both therapeutics and diagnostic tools. We have recently described five different single domain antibodies (nanobodies) which share this cross-neutralising activity and suggest their small size, high stability, and cleft binding properties may present distinct advantages over equivalent conventional antibodies. We have used yeast display in combination with deep mutational scanning to give residue level resolution of positions in the antibody-HA interface which are crucial for binding. In addition, we have mapped positions within HA predicted to have minimal effect on antibody binding when mutated. Our cross-neutralising nanobodies were shown to bind to a highly conserved pocket in the HA2 domain of A(H1N1)pdm09 influenza virus overlapping with the fusion peptide suggesting their mechanism of action is through the inhibition of viral membrane fusion. We also note that the epitope overlaps with that of CR6261 and F10 which are human monoclonal antibodies in clinical development as immunotherapeutics. Although all five nanobodies mapped to the same highly conserved binding pocket we observed differences in the size of the epitope footprint which has implications in comparing the relative genetic barrier each nanobody presents to a rapidly evolving influenza virus. To further refine our epitope map, we have re-created naturally occurring mutations within this HA stem epitope and tested their effect on binding using yeast display. We have shown that a D46N mutation in the HA2 stem domain uniquely interferes with binding of R2b-E8. Further testing of this substitution in the context of full length purified HA from 1918 H1N1 pandemic (Spanish flu), 2009 H1N1 pandemic (swine flu) and highly pathogenic avian influenza H5N1 demonstrated binding which correlated with D46 whereas binding to seasonal H1N1 strains carrying N46 was absent. In addition, our deep sequence analysis predicted that binding to the emerging H1N1 strain (A/Christchurch/16/2010) carrying the HA2-E47K mutation would not affect binding was confirmed experimentally. This demonstrates yeast display, in combination with deep sequencing, may be able to predict antibody reactivity to emerging influenza strains so assisting in the preparation for future influenza pandemics.

High Resolution Mapping of Bactericidal Monoclonal Antibody Binding Epitopes on Staphylococcus aureus Antigen MntC

The Staphylococcus aureus manganese transporter protein MntC is under investigation as a component of a prophylactic S.aureus vaccine. Passive immunization with monoclonal antibodies mAB 305-78-7 and mAB 305-101-8 produced using MntC was shown to significantly reduce S. aureus burden in an infant rat model of infection. Earlier interference mapping suggested that a total of 23 monoclonal antibodies generated against MntC could be subdivided into three interference groups, representing three independent immunogenic regions. In the current work binding epitopes for selected representatives of each of these interference groups (mAB 305-72-5 – group 1, mAB 305-78-7 – group 2, and mAB 305-101-8 – group 3) were mapped using Hydrogen-Deuterium Exchange Mass Spectrometry (DXMS). All of the identified epitopes are discontinuous, with binding surface formed by structural elements that are separated within the primary sequence of the protein but adjacent in the context of the three-dimensional structure. The approach was validated by co-crystallizing the Fab fragment of one of the antibodies (mAB 305-78-7) with MntC and solving the three-dimensional structure of the complex. X-ray results themselves and localization of the mAB 305-78-7 epitope were further validated using antibody binding experiments with MntC variants containing substitutions of key amino acid residues. These results provided insight into the antigenic properties of MntC and how these properties may play a role in protecting the hostagainst S. aureus infection by preventing the capture and transport of Mn²⁺, a key element that the pathogen uses to evade host immunity.

Staphylococcus aureus protein MntC is a metal-binding protein of the ABC-type transporter involved in the acquisition of an essential nutrient, Mn²⁺, by the pathogen. An earlier study demonstrated that use of MntC as an antigen in experimental vaccine can provide protection against staphylococcal infections in animals and identified three groups of protective monoclonal antibodies induced by the protein. In the current work we employed Deuterium-Hydrogen Exchange Mass Spectrometry (DXMS) to determine binding sites of selected representatives from each of those three groups. DXMS results were further validated using X-ray crystallography, site-directed mutagenesis and functional studies. Locations of the binding sites and results of the functional studies were used to draw conclusion on molecular mechanisms of protection afforded by MntC: antibodies belonging to two of the groups are predicted to interfere with Mn²⁺ transfer from the protein to the transmembrane channel pore, while the third group of the antibodies is expected to interfere with Mn²⁺ binding to MntC itself. The net result in both cases is impaired Mn²⁺ transport across the bacterial membrane and increased susceptibility of the bacterium to the oxidative stress, likely due to the reduced activity of superoxide dismutase which requires Mn²⁺ as an essential co-factor for activity.

Advancement and applications of peptide phage display technology in biomedical science

Combinatorial phage library is a powerful research tool for high-throughput screening of protein interactions. Of all available molecular display techniques, phage display has proven to be the most popular approach. Screening phage-displayed random peptide libraries is an effective means of identifying peptides that can bind target molecules and regulate their function. Phage-displayed peptide libraries can be used for (i) B-cell and T-cell epitope mapping, (ii) selection of bioactive peptides bound to receptors or proteins, disease-specific antigen mimics, peptides bound to non-protein targets, cell-specific peptides, or organ-specific peptides, and (iii) development of peptide-mediated drug delivery systems and other applications. Targeting peptides identified using phage display technology may be useful for basic research and translational medicine. In this review article, we summarize the latest technological advancements in the application of phage-displayed peptide libraries to applied biomedical sciences.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses

Human influenza viruses are rapidly evolving RNA viruses that cause short-term respiratory infections with substantial morbidity and mortality in annual epidemics. Uncovering the general principles of viral coevolution with human hosts is important for pathogen surveillance and vaccine design. Protein regions are an appropriate model for the interactions between two macromolecules, but the currently used epitope definition for the major antigen of influenza viruses, namely hemagglutinin, is very broad. Here, we combined genetic, evolutionary, antigenic, and structural information to determine the most relevant regions of the hemagglutinin of human influenza A/H3N2 viruses for interaction with human immunoglobulins. We estimated the antigenic weights of amino acid changes at individual sites from hemagglutination inhibition data using antigenic tree inference followed by spatial clustering of antigenicity-altering protein sites on the protein structure. This approach determined six relevant areas (patches) for antigenic variation that had a key role in the past antigenic evolution of the viruses. Previous transitions between successive predominating antigenic types of H3N2 viruses always included amino acid changes in either the first or second antigenic patch. Interestingly, there was only partial overlap between the antigenic patches and the patches under strong positive selection. Therefore, besides alterations of antigenicity, other interactions with the host may shape the evolution of human influenza A/H3N2 viruses.

A Cohesive and Integrated Platform for Immunogenicity Prediction

In silico methods for immunogenicity prediction mine the enormous quantity of data arising from deciphered genomes and proteomes to identify immunogenic proteins. While high and productive immunogenicity is essential for vaccines, therapeutic proteins and monoclonal antibodies should be minimally immunogenic. Here, we present a cohesive platform for immunogenicity and MHC class I and/or II binding affinity prediction. The platform integrates three quasi-independent modular servers: VaxiJen, EpiJen, and EpiTOP. VaxiJen (http://www.ddg-pharmfac.net/vaxijen) predicts immunogenicity of proteins of different origin; EpiJen (http://www.ddg-pharmfac.net/epijen) predicts peptide binding to MHC class I proteins; and EpiTOP (http://www.ddg-pharmfac.net/epitop) predicts peptide binding to MHC class II proteins. The platform is freely accessible and user-friendly. The protocol for immunogenicity prediction is demonstrated by selecting immunogenic proteins from Mycobacterium tuberculosis and predicting how the peptide epitopes within them bind to MHC class I and class II proteins.

The Relationship between B-cell Epitope and Mimotope Sequences

B-cell epitope is a group of residues which is on the surface of an antigen. It invokes humoral responses. Locating B-cell epitope is important for effective vaccine design, and the development of diagnostic reagents. Mimotope-based B-cell epitope prediction method is a kind of conformational B-cell epitope prediction, and the core idea of the method is mapping the mimotope sequences which are obtained from a random phage display library. However, current mimotope-based B-cell epitope prediction methods cannot maintain a high degree of satisfaction in the circumstances of employing only mimotope sequences. In this study, we did a multi-perspective analysis on parameters for conformational B-cell epitopes and characteristics between epitope and mimotope on a benchmark datasets which contains 67 mimotope sets, corresponding to 40 unique complex structures. In these 67 cases, there are 25 antigen-antibody complexes and 42 protein-protein interactions. We analyzed the two parts separately. The results showed the mimotope sequences do have some epitope features, but there are also some epitope properties that mimotope sequences do not contain. In addition, the numbers of epitope segments with different lengths were obviously different between the antigen-antibody complexes and the protein-protein interactions. This study reflects how similar do mimotope sequence and genuine epitopes have; and evaluates existing mimotope-based B-cell epitope prediction methods from a novel viewpoint.