Bioinformatics resources and tools for phage display

Computational Methods to Predict Conformational B-Cell Epitopes

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Protective antigenic epitopes revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine

Background

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected millions of people around the world. Vaccination is a pillar in the strategy to control transmission of the SARS-CoV-2 spread. Immune responses to vaccination require elucidation.

Methods

The immune responses to vaccination with three doses of inactivated SARS-CoV-2 vaccine were followed in a cohort of 37 healthy adults (18–59 years old). Blood samples were collected at multiple time points and submitted to peptide array, machine learning modeling, and sequence alignment analyses, the results of which were used to generate vaccine-induced antibody-binding region (VIABR) immunosignatures (Registration number: ChiCTR2200058571).

Results

Antibody spectrum signals showed vaccination stimulated antibody production. Sequence alignment analyses revealed that a third vaccine dose generated a new highly represented VIABR near the A570D mutation, and the whole process of inoculation enhanced the VIABR near the N501Y mutation. In addition, the antigen conformational epitopes varied between short- and long-term samples. The amino acids with the highest scores in the short-term samples were distributed primarily in the receptor binding domain (RBD) and N-terminal domain regions of spike (S) protein, while in the long-term samples (12 weeks after the 2^nd dose), some new conformational epitopes (CEs) were localized to crevices within the head of the S protein trimer.

Conclusion

Protective antigenic epitopes were revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine inoculation. A third dose results in a new top-10 VIABR near the A570D mutation site of S protein, and the whole process of inoculation enhanced the VIABR near the N501Y mutation, thus potentially providing protection from strains that have gained invasion and immune escape abilities through these mutation.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

Phage-Displayed Mimotopes of SARS-CoV-2 Spike Protein Targeted to Authentic and Alternative Cellular Receptors

The evolution of the SARS-CoV-2 virus during the COVID-19 pandemic was accompanied by the emergence of new heavily mutated viral variants with increased infectivity and/or resistance to detection by the human immune system. To respond to the urgent need for advanced methods and materials to empower a better understanding of the mechanisms of virus’s adaptation to human host cells and to the immuno-resistant human population, we suggested using recombinant filamentous bacteriophages, displaying on their surface foreign peptides termed “mimotopes”, which mimic the structure of viral receptor-binding sites on the viral spike protein and can serve as molecular probes in the evaluation of molecular mechanisms of virus infectivity. In opposition to spike-binding antibodies that are commonly used in studying the interaction of the ACE2 receptor with SARS-CoV-2 variants in vitro, phage spike mimotopes targeted to other cellular receptors would allow discovery of their role in viral infection in vivo using cell culture, tissue, organs, or the whole organism. Phage mimotopes of the SARS-CoV-2 Spike S1 protein have been developed using a combination of phage display and molecular mimicry concepts, termed here “phage mimicry”, supported by bioinformatics methods. The key elements of the phage mimicry concept include: (1) preparation of a collection of p8-type (landscape) phages, which interact with authentic active receptors of live human cells, presumably mimicking the binding interactions of human coronaviruses such as SARS-CoV-2 and its variants; (2) discovery of closely related amino acid clusters with similar 3D structural motifs on the surface of natural ligands (FGF1 and NRP1), of the model receptor of interest FGFR and the S1 spike protein; and (3) an ELISA analysis of the interaction between candidate phage mimotopes with FGFR3 (a potential alternative receptor) in comparison with ACE2 (the authentic receptor).

Application of Next Generation Sequencing (NGS) in Phage Displayed Peptide Selection to Support the Identification of Arsenic-Binding Motifs

Next generation sequencing (NGS) in combination with phage surface display (PSD) are powerful tools in the newly equipped molecular biology toolbox for the identification of specific target binding biomolecules. Application of PSD led to the discovery of manifold ligands in clinical and material research. However, limitations of traditional phage display hinder the identification process. Growth-based library biases and target-unrelated peptides often result in the dominance of parasitic sequences and the collapse of library diversity. This study describes the effective enrichment of specific peptide motifs potentially binding to arsenic as proof-of-concept using the combination of PSD and NGS. Arsenic is an environmental toxin, which is applied in various semiconductors as gallium arsenide and selective recovery of this element is crucial for recycling and remediation. The development of biomolecules as specific arsenic-binding sorbents is a new approach for its recovery. Usage of NGS for all biopanning fractions allowed for evaluation of motif enrichment, in-depth insight into the selection process and the discrimination of biopanning artefacts, e.g., the amplification-induced library-wide reduction in hydrophobic amino acid proportion. Application of bioinformatics tools led to the identification of an SxHS and a carboxy-terminal QxQ motif, which are potentially involved in the binding of arsenic. To the best of our knowledge, this is the first report of PSD combined with NGS of all relevant biopanning fractions.

Innovative IgG Biomarkers Based on Phage Display Microbial Amyloid Mimotope for State and Stage Diagnosis in Alzheimer's Disease

An innovative approach to identify new conformational antigens of Aβ_1–42 recognized by IgG autoantibodies as biomarkers of state and stage in Alzheimer’s disease (AD) patients is described. In particular, through the use of bioinformatics modeling, conformational similarities between several Aβ_1–42 forms and other amyloid-like proteins with F1 capsular antigen (Caf1) of Yersinia pestis were first found. pVIII M13 phage display libraries were then screened against YPF19, anti-Caf1 monoclonal antibody, and IgGs of AD patients, in alternate biopanning cycles of a so-called “double binding” selection. From the selected phage clones, one, termed 12III1, was found to be able to prevent in vitro Aβ_1–42-induced cytotoxicity in SH-SY5Y cells, as well as to promote disaggregation of preformed fibrils, to a greater extent with respect to wild-type phage (pC89). IgG levels detected by 12III1 provided a significant level of discrimination between diseased and nondemented subjects, as well as a good correlation with the state progression of the disease. These results give significant impact in AD state and stage diagnosis, paving the way for the development not only for an innovative blood diagnostic assay for AD precise diagnosis, progressive clinical assessment, and screening but also for new effective treatments.

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.

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.

Rational Identification of a Colorectal Cancer Targeting Peptide through Phage Display

Colorectal cancer is frequently diagnosed at an advanced stage due to the absence of early clinical indicators. Hence, the identification of new targeting molecules is crucial for an early detection and development of targeted therapies. This study aimed to identify and characterize novel peptides specific for the colorectal cancer cell line RKO using a phage-displayed peptide library. After four rounds of selection plus a negative step with normal colorectal cells, CCD-841-CoN, there was an obvious phage enrichment that specifically bound to RKO cells. Cell-based enzyme-linked immunosorbent assay (ELISA) was performed to assess the most specific peptides leading to the selection of the peptide sequence CPKSNNGVC. Through fluorescence microscopy and cytometry, the synthetic peptide RKOpep was shown to specifically bind to RKO cells, as well as to other human colorectal cancer cells including Caco-2, HCT 116 and HCT-15, but not to the normal non-cancer cells. Moreover, it was shown that RKOpep specifically targeted human colorectal cancer cell tissues. A bioinformatics analysis suggested that the RKOpep targets the monocarboxylate transporter 1, which has been implicated in colorectal cancer progression and prognosis, proven through gene knockdown approaches and shown by immunocytochemistry co-localization studies. The peptide herein identified can be a potential candidate for targeted therapies for colorectal cancer.

PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides

Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.

A novel peptide specifically binding to VEGF receptor suppresses angiogenesis in vitro and in vivo

Vascular endothelial growth factor (VEGF), one of the most important angiogenic factors, plays an essential role in both physiological and pathological angiogenesis through binding to VEGF receptors (VEGFRs). Here we report a novel peptide designated HRHTKQRHTALH (peptide HRH), which was isolated from the Ph.D. -12 phage display library using VEGFR-Fc fusion protein as the bait. This peptide was found to dose-dependently inhibit the proliferation of human umbilical vein endothelial cells stimulated by VEGF. The anti-angiogenesis effect of the HRH peptide was further confirmed in vivo using the chick chorioallantoic membrane assay, which was also dose-dependent. Besides, peptide HRH was proved to inhibit corneal neovascularization in an alkali-burnt rat corneal model and a suture-induced rat corneal model. Taken together, these findings suggest that the HRH peptide can inhibit angiogenesis both in vitro and in vivo. Consequently, the HRHTKQRHTALH peptide might be a promising lead peptide for the development of potential angiogenic inhibitors.

An Overview of Bioinformatics Tools and Resources in Allergy

The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

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.

Phage Display Technology in Biomaterials Engineering: Progress and Opportunities for Applications in Regenerative Medicine

The field of regenerative medicine has been gaining momentum steadily over the past few years. The emphasis in regenerative medicine is to use various in vitro and in vivo approaches that leverage the intrinsic healing mechanisms of the body to treat patients with disabling injuries and chronic diseases such as diabetes, osteoarthritis, and degenerative disorders of the cardiovascular and central nervous system. Phage display has been successfully employed to identify peptide ligands for a wide variety of targets, ranging from relatively small molecules (enzymes, cell receptors) to inorganic, organic, and biological (tissues) materials. Over the past two decades, phage display technology has advanced tremendously and has become a powerful tool in the most varied fields of research, including biotechnology, materials science, cell biology, pharmacology, and diagnostics. The growing interest in and success of phage display libraries is largely due to its incredible versatility and practical use. This review discusses the potential of phage display technology in biomaterials engineering for applications in regenerative medicine.

Discovery of a Biological Mechanism of Active Transport through the Tympanic Membrane to the Middle Ear

Otitis media (OM) is a common pediatric disease for which systemic antibiotics are often prescribed. While local treatment would avoid the systemic treatment side-effects, the tympanic membrane (TM) represents an impenetrable barrier unless surgically breached. We hypothesized that the TM might harbor innate biological mechanisms that could mediate trans-TM transport. We used two M13-bacteriophage display biopanning strategies to search for mediators of trans-TM transport. First, aliquots of linear phage library displaying 10^10th 12mer peptides were applied on the TM of rats with active bacterial OM. The middle ear (ME) contents were then harvested, amplified and the preparation re-applied for additional rounds. Second, the same naïve library was sequentially screened for phage exhibiting TM binding, internalization and then transit. Results revealed a novel set of peptides that transit across the TM to the ME in a time and temperature dependent manner. The peptides with highest transport capacities shared sequence similarities. Historically, the TM was viewed as an impermeable barrier. However, our studies reveal that it is possible to translocate peptide-linked small particles across the TM. This is the first comprehensive biopanning for the isolation of TM transiting peptidic ligands. The identified mechanism offers a new drug delivery platform into the ME.

BDB: biopanning data bank

The BDB database (http://immunet.cn/bdb) is an update of the MimoDB database, which was previously described in the 2012 Nucleic Acids Research Database issue. The rebranded name BDB is short for Biopanning Data Bank, which aims to be a portal for biopanning results of the combinatorial peptide library. Last updated in July 2015, BDB contains 2904 sets of biopanning data collected from 1322 peer-reviewed papers. It contains 25,786 peptide sequences, 1704 targets, 492 known templates, 447 peptide libraries and 310 crystal structures of target-template or target-peptide complexes. All data stored in BDB were revisited, and information on peptide affinity, measurement method and procedures was added for 2298 peptides from 411 sets of biopanning data from 246 published papers. In addition, a more professional and user-friendly web interface was implemented, a more detailed help system was designed, and a new on-the-fly data visualization tool and a series of tools for data analysis were integrated. With these new data and tools made available, we expect that the BDB database would become a major resource for scholars using phage display, with improved utility for biopanning and related scientific communities.

Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets

Motivation: Proteins often recognize their interaction partners on the basis of short linear motifs located in disordered regions on proteins’ surface. Experimental techniques that study such motifs use short peptides to mimic the structural properties of interacting proteins. Continued development of these methods allows for large-scale screening, resulting in vast amounts of peptide sequences, potentially containing information on multiple protein-protein interactions. Processing of such datasets is a complex but essential task for large-scale studies investigating protein-protein interactions.

Results: The software tool presented in this article is able to rapidly identify multiple clusters of sequences carrying shared specificity motifs in massive datasets from various sources and generate multiple sequence alignments of identified clusters. The method was applied on a previously published smaller dataset containing distinct classes of ligands for SH3 domains, as well as on a new, an order of magnitude larger dataset containing epitopes for several monoclonal antibodies. The software successfully identified clusters of sequences mimicking epitopes of antibody targets, as well as secondary clusters revealing that the antibodies accept some deviations from original epitope sequences. Another test indicates that processing of even much larger datasets is computationally feasible.

Availability and implementation: Hammock is published under GNU GPL v. 3 license and is freely available as a standalone program (from http://www.recamo.cz/en/software/hammock-cluster-peptides/) or as a tool for the Galaxy toolbox (from https://toolshed.g2.bx.psu.edu/view/hammock/hammock). The source code can be downloaded from https://github.com/hammock-dev/hammock/releases.

Contact:muller@mou.cz

Supplementaryinformation:Supplementary data are available at Bioinformatics online.

Epitope Fingerprinting for Recognition of the Polyclonal Serum Autoantibodies of Alzheimer's Disease

Autoantibodies (aAb) associated with Alzheimer's disease (AD) have not been sufficiently characterized and their exact involvement is undefined. The use of information technology and computerized analysis with phage display technology was used, in the present research, to map the epitope of putative self-antigens in AD patients. A 12-mer random peptide library, displayed on M13 phages, was screened using IgG from AD patients with two repetitions. Seventy-one peptides were isolated; however, only 10 were positive using the Elisa assay technique (Elisa Index > 1). The results showed that the epitope regions of the immunoreactive peptides, identified by phage display analysis, were on the exposed surfaces of the proteins. The putative antigens MAST1, Enah, MAO-A, X11/MINT1, HGF, SNX14, ARHGAP 11A, APC, and CENTG3, which have been associated with AD or have functions in neural tissue, may indicate possible therapeutic targets.

Identification of a novel linear epitope on the NS1 protein of avian influenza virus

BACKGROUND

The NS1 protein of avian influenza virus (AIV) is an important virulent factor of AIV. It has been shown to counteract host type I interferon response, to mediate host cell apoptosis, and to regulate the process of protein synthesis. The identification of AIV epitopes on NS1 protein is important for understanding influenza virus pathogenesis.

RESULTS

In this paper, we describe the generation, identification, and epitope mapping of a NS1 protein-specific monoclonal antibody (MAb) D9. First, to induce the production of MAbs, BALB/c mice were immunized with a purified recombinant NS1 expressed in E. coli. The spleen cells from the immunized mice were fused with myeloma cells SP2/0, and through screening via indirect ELISAs, a MAb, named D9, was identified. Western blot assay results showed that MAb D9 reacted strongly with the recombinant NS1. Confocal laser scanning microscopy showed that this MAb also reacts with NS1 expressed in 293T cells that had been transfected with eukaryotic recombinant plasmids. Results from screening a phage display random 7-mer peptide library with MAb D9 demonstrated that it recognizes phages displaying peptides with the consensus peptide WNLNTV--VS, which closely matches the (182)WNDNTVRVS(190) of AIV NS1. Further identification of the displayed epitope was performed with a set of truncated polypeptides expressed as glutathione S-transferase fusion proteins, and the motif (182)WNDNT(186) was defined as the minimal unit of the linear B cell epitope recognized by MAb D9 in western blot assays. Moreover, homology analysis showed that this epitope is a conserved motif among AIV.

CONCLUSIONS

We identified a conserved linear epitope, WNDNT, on the AIV NS1 protein that is recognized by MAb D9. This MAb and its epitope may facilitate future studies on NS1 function and aid the development of new diagnostic methods for AIV detection.

Improved serological detection of rheumatoid arthritis: a highly antigenic mimotope of carbonic anhydrase III selected in a murine model by phage display

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease that affects around 1% of the human population worldwide. RA diagnosis can be difficult as there is no definitive test for its detection. Therefore, the aim of this study was to identify biomarkers that could be used for RA diagnosis.

METHODS

Sera from a collagen-induced arthritis mouse model were used to select potential biomarkers for RA diagnosis by phage display technology. In silico and in vitro analyses were performed to characterize and validate the selected peptides. Samples were classified into three groups: RA; two other immune-mediated rheumatic diseases (systemic lupus erythematosus (SLE) and ankylosing spondylitis (AS)); and healthy controls (HC). Enzyme-linked immunosorbent assay (ELISA) was carried out to determine antibody levels, and diagnostic parameters were determined by constructing receiver operating characteristic curves. Mass spectrometry and Western blot were performed to identify the putative autoantigen that was mimicked by a highly reactive mimotope.

RESULTS

After three rounds of selection, 14 clones were obtained and tested for immunoreactivity analysis against sera from RA and HC groups. The phage-fused peptide with the highest immunoreactivity (M12) was synthesized, and was able to efficiently discriminate RA patients from SLE, AS and HCs (p < 0.0001) by ELISA. The specificity and sensitivity of anti-M12 antibodies for RA diagnosis were 91 % and 84.3 %, respectively. The M12 peptide was identified as one that mimics a predicted antigenic site of the carbonic anhydrase III (CAIII) protein, a ubiquitous biomarker that has been identified in patients with other diseases.

CONCLUSION

M12 is the first peptide associated with the CAIII protein that may be used as an antigen for antibody detection to aid in RA diagnosis with high sensitivity and specificity.

Novel p16 binding peptide development for p16-overexpressing cancer cell detection using phage display

Protein p(16INK4a) (p16) is a well-known biomarker for diagnosis of human papillomavirus (HPV) related cancers. In this work, we identify novel p16 binding peptides by using phage display selection method. A random heptamer phage display library was screened on purified recombinant p16 protein-coated plates to elute only the bound phages from p16 surfaces. Binding affinity of the bound phages was compared with each other by enzyme-linked immunosorbent assay (ELISA), fluorescence imaging technique, and bioinformatic computations. Binding specificity and binding selectivity of the best candidate phage-displayed p16 binding peptide were evaluated by peptide blocking experiment in competition with p16 monoclonal antibody and fluorescence imaging technique, respectively. Five candidate phage-displayed peptides were isolated from the phage display selection method. All candidate p16 binding phages show better binding affinity than wild-type phage in ELISA test, but only three of them can discriminate p16-overexpressing cancer cell, CaSki, from normal uterine fibroblast cell, HUF, with relative fluorescence intensities from 2.6 to 4.2-fold greater than those of wild-type phage. Bioinformatic results indicate that peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' binds to p16 molecule with the best binding score and does not interfere with the common protein functions of p16. Peptide blocking experiment shows that the phage-displayed peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' can conceal p16 from monoclonal antibody interaction. This phage clone also selectively interacts with the p16 positive cell lines, and thus, it can be applied for p16-overexpressing cell detection.

Conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis

Identification of epitopes which invokes strong humoral responses is an essential issue in the field of immunology. Various computational methods that have been developed based on the antigen structures and the mimotopes these years narrow the search for experimental validation. These methods can be divided into two categories: antigen structure-based methods and mimotope-based methods. Though new methods of the two kinds have been proposed in these years, they cannot maintain a high degree of satisfaction in various circumstances. In this paper, we proposed a new conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis. The method classifies the antigen surface residues into “epitopes” and “nonepitopes” by six epitope propensity scales, removing the “nonepitopes” and using the preprocessed antigen for epitope prediction based on mimotope sequences. The proposed method gives out the mean F score of 0.42 on the testing dataset. When compared with other publicly available servers by using the testing dataset, the new method yields better performance. The results demonstrate the proposed method is competent for the conformational B-cell epitope prediction.

PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library

Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.

Databases for B-cell epitopes

Identification and characterization of B-cell epitopes in target antigens is one of the key steps in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. For localizing epitopes by experimental methods is time consuming and cost expensive, researchers have been developing in silico or computational models for the prediction of B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. A sufficient number of available B-cell epitopes are indispensable for establishing the prediction models. To our knowledge, some popular databases associated with the B-cell epitopes are proposed and widely used in the immunoinformatics. In this chapter, we present an overview of the important databases and introduce how to compile datasets for the development of B-cell epitope prediction tools.

Bioinformatics resources and tools for conformational B-cell epitope prediction

Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.

Epitope mapping of metuximab on CD147 using phage display and molecular docking

Metuximab is the generic name of Licartin, a new drug for radioimmunotherapy of hepatocellular carcinoma. Although it is known to be a mouse monoclonal antibody against CD147, the complete epitope mediating the binding of metuximab to CD147 remains unknown. We panned the Ph.D.-12 phage display peptide library against metuximab and got six mimotopes. The following bioinformatics analysis based on mimotopes suggested that metuximab recognizes a conformational epitope composed of more than 20 residues. The residues of its epitope may include T28, V30, K36, L38, K57, F74, D77, S78, D79, D80, Q81, G83, S86, N98, Q100, L101, H102, G103, P104, V131, P132, and K191. The homology modeling of metuximab and the docking of CD147 to metuximab were also performed. Based on the top one docking model, the epitope was predicted to contain 28 residues: AGTVFTTV (23–30), I37, D45, E84, V88, EPMGTANIQLH (92–102), VPP (131–133), Q164, and K191. Almost half of the residues predicted on the basis of mimotope analysis also appear in the docking result, indicating that both results are reliable. As the predicted epitopes of metuximab largely overlap with interfaces of CD147-CD147 interactions, a structural mechanism of metuximab is proposed as blocking the formation of CD147 dimer.

An antibody-like peptide that recognizes malignancy among thyroid nodules

There is an urgent need for biomarkers to identify malignant thyroid nodules from indeterminate follicular lesions. We have used a subtractive proteomic strategy to identify novel biomarkers by selecting ligands to goiter tissue from a 12-mer random peptide phage-displayed library using the BRASIL method (Biopanning and Rapid Analysis of Selective Interactive Ligands). After three rounds of selection, two highly reactive clones to the papillary thyroid tumor cell line NPA were further evaluated, and their specific binding to tumor proteins was confirmed using phage-ELISA. The antibody-like peptide CaT12 was tumor-specific, which was further tested by immunohistochemistry against TMAs (tissue microarrays) comprised of 775 human benign and malignant tissues, including 232 thyroid nodular lesions: 15 normal thyroid tissues, 53 nodular goiters (NG), 54 follicular adenomas (FA); 69 papillary thyroid carcinomas (PTC); and 41 follicular carcinomas (FC). CaT12 was able to identify PTC among thyroid nodular lesions with 91.2% sensitivity and 85.1% specificity, despite its non-specificity for thyroid tissues. Additionally, the CaT12 peptide helped characterize follicular lesions distinguishing the follicular variant of PTC (FVPTC) from FA with 91.9% accuracy; FVPTC from NG with 83.1% accuracy; FVPTC from the classic PTC with 57.7% accuracy; and FVPTC from FC with 88.7% accuracy. In conclusion, our strategy to select differentially expressed ligands to thyroid tissue was highly effective and resulted in a useful antibody-like biomarker that recognizes malignancy among thyroid nodules and may help distinguish follicular patterned lesions.

Phage display selection of peptides that target calcium-binding proteins

Phage display allows to rapidly identify peptide sequences with binding affinity towards target proteins, for example, calcium-binding proteins (CBPs). Phage technology allows screening of 10(9) or more independent peptide sequences and can identify CBP binding peptides within 2 weeks. Adjusting of screening conditions allows selecting CBPs binding peptides that are either calcium-dependent or independent. Obtained peptide sequences can be used to identify CBP target proteins based on sequence homology or to quickly obtain peptide-based CBP inhibitors to modulate CBP-target interactions. The protocol described here uses a commercially available phage display library, in which random 12-mer peptides are displayed on filamentous M13 phages. The library was screened against the calcium-binding protein S100B.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Phage display as a method for discovering cellular targets of small molecules

Phage display can be used for the discovery of cellular targets of small molecules in order to unravel their mechanism of action, which is important in the drug discovery field to assess biological effects of drugs at the molecular level and to investigate pharmacokinetic characteristics of drugs in clinical use. The potential of phage display in the drug discovery field is shown by a lot of successful cellular target identifications of drug-like small molecules in the last decade. More recently, phage display was also introduced in environmental science to predict risks of small molecules, like nickel, 17β estradiol and bisphenol A on both environmental and human health, wherefore knowledge about the mechanism of action and cellular targets is essential. This paper discusses some important aspects of the phage display approach for the discovery of cellular targets of small molecules. The different phage display libraries and immobilization strategies used for the discovery of cellular target of small molecules are described. In general, the phage display approach is very useful in drug discovery and environmental science as a fast and cost-effective in vitro tool to determine cellular targets of small molecules, which increases our understanding of the mechanisms of action of small molecules.

PepMapper: a collaborative web tool for mapping epitopes from affinity-selected peptides

Epitope mapping from affinity-selected peptides has become popular in epitope prediction, and correspondingly many Web-based tools have been developed in recent years. However, the performance of these tools varies in different circumstances. To address this problem, we employed an ensemble approach to incorporate two popular Web tools, MimoPro and Pep-3D-Search, together for taking advantages offered by both methods so as to give users more options for their specific purposes of epitope-peptide mapping. The combined operation of Union finds as many associated peptides as possible from both methods, which increases sensitivity in finding potential epitopic regions on a given antigen surface. The combined operation of Intersection achieves to some extent the mutual verification by the two methods and hence increases the likelihood of locating the genuine epitopic region on a given antigen in relation to the interacting peptides. The Consistency between Intersection and Union is an indirect sufficient condition to assess the likelihood of successful peptide-epitope mapping. On average from 27 tests, the combined operations of PepMapper outperformed either MimoPro or Pep-3D-Search alone. Therefore, PepMapper is another multipurpose mapping tool for epitope prediction from affinity-selected peptides. The Web server can be freely accessed at: http://informatics.nenu.edu.cn/PepMapper/

Functional epitope core motif of the Anaplasma marginale major surface protein 1a and its incorporation onto bioelectrodes for antibody detection

Anaplasmosis, a persistent intraerythrocytic infection of cattle by Anaplasma marginale, causes severe anemia and a higher rate of abortion, resulting in significant loss to both dairy and beef industries. Clinical diagnosis is based on symptoms and confirmatory laboratory tests are required. Currently, all the diagnostic assays have been developed with whole antigens with indirect ELISA based on multiple epitopes. In a pioneer investigation we demonstrated the use of critical motifs of an epitope as biomarkers for immunosensor applications. Mimotopes of the MSP1a protein functional epitope were obtained through Phage Display after three cycles of selection of a 12-mer random peptide library against the neutralizing monoclonal antibody 15D2. Thirty-nine clones were randomly selected, sequenced, translated and aligned with the native sequence. The consensus sequence SxSSQSEASTSSQLGA was obtained, which is located in C-terminal end of the 28-aa repetitive motif of the MSP1a protein, but the alignment and sequences' variation among mimotopes allowed us to map the critical motif STSSxL within the consensus sequence. Based on these results, two peptides were chemically synthesized: one based on the critical motif (STSSQL, Am1) and the other based on the consensus sequence aligned with the native epitope (SEASTSSQLGA, Am2). Sera from 24 infected and 52 healthy animals were tested by ELISA for reactivity against Am1 and Am2, which presented sensitivities of 96% and 100%, respectively. The Am1 peptide was incorporated onto a biolectrode (graphite modified with poly-3-hydroxyphenylacetic acid) and direct serum detection was demonstrated by impedance, differential pulse voltammetry, and atomic force microscopy. The electrochemical sensor system proved to be highly effective in discriminating sera from positive and negative animals. These immunosensors were highly sensitive and selective for positive IgG, contaminants did not affect measurements, and were based on a simple, fast and reproducible electrochemical system.

MimoDB 2.0: a mimotope database and beyond

Mimotopes are peptides with affinities to given targets. They are readily obtained through biopanning against combinatorial peptide libraries constructed by phage display and other display technologies such as mRNA display, ribosome display, bacterial display and yeast display. Mimotopes have been used to infer the protein interaction sites and networks; they are also ideal candidates for developing new diagnostics, therapeutics and vaccines. However, such valuable peptides are not collected in the central data resources such as UniProt and NCBI GenPept due to their ‘unnatural’ short sequences. The MimoDB database is an information portal to biopanning results of random libraries. In version 2.0, it has 15 633 peptides collected from 849 papers and grouped into 1818 sets. Besides the core data on panning experiments and their results, broad background information on target, template, library and structure is included. An accompanied benchmark has also been compiled for bioinformaticians to develop and evaluate their new models, algorithms and programs. In addition, the MimoDB database provides tools for simple and advanced searches, structure visualization, BLAST and alignment view on the fly. The experimental biologists can easily use the database as a virtual control to exclude possible target-unrelated peptides. The MimoDB database is freely available at http://immunet.cn/mimodb.

Diversity of phage-displayed libraries of peptides during panning and amplification

The amplification of phage-displayed libraries is an essential step in the selection of ligands from these libraries. The amplification of libraries, however, decreases their diversity and limits the number of binding clones that a screen can identify. While this decrease might not be a problem for screens against targets with a single binding site (e.g., proteins), it can severely hinder the identification of useful ligands for targets with multiple binding sites (e.g., cells). This review aims to characterize the loss in the diversity of libraries during amplification. Analysis of the peptide sequences obtained in several hundred screens of peptide libraries shows explicitly that there is a significant decrease in library diversity that occurs during the amplification of phage in bacteria. This loss during amplification is not unique to specific libraries: it is observed in many of the phage display systems we have surveyed. The loss in library diversity originates from competition among phage clones in a common pool of bacteria. Based on growth data from the literature and models of phage growth, we show that this competition originates from growth rate differences of only a few percent for different phage clones. We summarize the findings using a simple two-dimensional "phage phase diagram", which describes how the collapse of libraries, due to panning and amplification, leads to the identification of only a subset of the available ligands. This review also highlights techniques that allow elimination of amplification-induced losses of diversity, and how these techniques can be used to improve phage-display selection and enable the identification of novel ligands.