Exploring antibody recognition of sequence space through random-sequence peptide microarrays

Predicting monoclonal antibody binding sequences from a sparse sampling of all possible sequences

Previous work has shown that binding of target proteins to a sparse, unbiased sample of all possible peptide sequences is sufficient to train a machine learning model that can then predict, with statistically high accuracy, target binding to any possible peptide sequence of similar length. Here, highly sequence-specific molecular recognition is explored by measuring binding of 8 monoclonal antibodies (mAbs) with specific linear cognate epitopes to an array containing 121,715 near-random sequences about 10 residues in length. Network models trained on resulting sequence-binding values are used to predict the binding of each mAb to its cognate sequence and to an in silico generated one million random sequences. The model always ranks the binding of the cognate sequence in the top 100 sequences, and for 6 of the 8 mAbs, the cognate sequence ranks in the top ten. Practically, this approach has potential utility in selecting highly specific mAbs for therapeutics or diagnostics. More fundamentally, this demonstrates that very sparse random sampling of a large amino acid sequence spaces is sufficient to generate comprehensive models predictive of highly specific molecular recognition.

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.

Production of high-complexity frameshift neoantigen peptide microarrays

Parallel measurement of large numbers of antigen-antibody interactions are increasingly enabled by peptide microarray technologies. Our group has developed an in situ synthesized peptide microarray of >400 000 frameshift neoantigens using mask-based photolithographic peptide synthesis, to profile patient specific neoantigen reactive antibodies in a single assay. The system produces 208 replicate mircoarrays per wafer and is capable of producing multiple wafers per synthetic lot to routinely synthesize over 300 million peptides simultaneously. In this report, we demonstrate the feasibility of the system for detecting peripheral-blood antibody binding to frameshift neoantigens across multiple synthetic lots.

Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers

Peptide arrays measure the binding intensity of a specific protein to thousands of amino acid peptides. By using peptides that cover all k-mers, a comprehensive picture of the binding spectrum is obtained. Researchers would like to measure binding to the longest k-mer possible but are constrained by the number of peptides that can fit into a single microarray. A key challenge is designing a minimum number of peptides that cover all k-mers. Here, we suggest a novel idea to reduce the length of the sequence covering all k-mers by utilizing a unique property of the peptide synthesis process. Since the synthesis can start from both ends of the peptide template, it is enough to cover each k-mer or its reverse and to use the same template twice: in forward and reverse. Then, the computational problem is to generate a minimum length sequence that for each k-mer either contains the k-mer or its reverse. In this study, we present a new algorithm, called ReverseCAKE, to generate such a sequence. ReverseCAKE runs in time linear in the output size and is guaranteed to produce a sequence that is longer by at most Θ(nlogn) characters compared with the optimum n. The obtained saving factor by ReverseCAKE approaches the theoretical lower bound as k increases. In addition, we formulated the problem as an integer linear program and empirically observed that the solutions obtained by ReverseCAKE are near-optimal. Through this work, we enable more effective design of peptide microarrays.

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

Background

Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances.

Results

Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method.

Conclusion

The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, “optimized” methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

Identification of Surface Epitopes Associated with Protection against Highly Immune-Evasive VlsE-Expressing Lyme Disease Spirochetes

The tick-borne pathogen Borrelia burgdorferi is responsible for approximately 300,000 Lyme disease (LD) cases per year in the United States. Recent increases in the number of LD cases, in addition to the spread of the tick vector and a lack of a vaccine, highlight an urgent need for designing and developing an efficacious LD vaccine. Identification of protective epitopes that could be used to develop a second-generation (subunit) vaccine is therefore imperative. Despite the antigenicity of several lipoproteins and integral outer membrane proteins (OMPs) on the B. burgdorferi surface, the spirochetes successfully evade antibodies primarily due to the VlsE-mediated antigenic variation. VlsE is thought to sterically block antibody access to protective epitopes of B. burgdorferi However, it is highly unlikely that VlsE shields the entire surface epitome. Thus, identification of subdominant epitope targets that induce protection when they are made dominant is necessary to generate an efficacious vaccine. Toward the identification, we repeatedly immunized immunocompetent mice with live-attenuated VlsE-deleted B. burgdorferi and then challenged the animals with the VlsE-expressing (host-adapted) wild type. Passive immunization and Western blotting data suggested that the protection of 50% of repeatedly immunized animals against the highly immune-evasive B. burgdorferi was antibody mediated. Comparison of serum antibody repertoires identified in protected and nonprotected animals permitted the identification of several putative epitopes significantly associated with the protection. Most linear putative epitopes were conserved between the main pathogenic Borrelia genospecies and found within known subdominant regions of OMPs. Currently, we are performing immunization studies to test whether the identified protection-associated epitopes are protective for mice.

High-Density Redox Amplified Coulostatic Discharge-Based Biosensor Array

High-density biosensor arrays are essential for many cutting-edge biomedical applications including point-of-care vaccination screening to detect multiple highly-contagious diseases. Typical electrochemical biosensing techniques are based on the measurement of sub-pA currents for micron-sized sensors requiring highly-sensitive readout circuits. Such circuits are often too complex to scale down for high-density arrays. In this paper, a high-density 4,096-pixel electrochemical biosensor array in 180 nm CMOS is presented. It uses a coulostatic discharge sensing technique and interdigitated electrode geometry to reduce both the complexity and size of the readout circuitry. Each biopixel contains an interdigitated microelectrode with a 13 aA low-leakage readout circuit directly underneath. Compared to standard planar electrodes, the implemented interdigitated electrodes achieve a maximum amplification factor of 10.5× from redox cycling. The array's sensor density is comparable to state-of-the-art arrays, all without augmenting the sensors with complex post-processing. The detection of anti-Rubella and anti-Mumps antibodies in human serum is demonstrated.

Entropy is a Simple Measure of the Antibody Profile and is an Indicator of Health Status: A Proof of Concept

We have previously shown that the diversity of antibodies in an individual can be displayed on chips on which 130,000 peptides chosen from random sequence space have been synthesized. This immunosignature technology is unbiased in displaying antibody diversity relative to natural sequence space, and has been shown to have diagnostic and prognostic potential for a wide variety of diseases and vaccines. Here we show that a global measure such as Shannon's entropy can be calculated for each immunosignature. The immune entropy was measured across a diverse set of 800 people and in 5 individuals over 3 months. The immune entropy is affected by some population characteristics and varies widely across individuals. We find that people with infections or breast cancer, generally have higher entropy values than non-diseased individuals. We propose that the immune entropy as measured from immunosignatures may be a simple method to monitor health in individuals and populations.

Antibody repertoire profiling with mimotope arrays

Large-scale profiling and monitoring of antibody repertoires is possible through next generation sequencing (NGS), phage display libraries and microarrays. These methods can be combined in a pipeline, which ultimately maps the antibody reactivities onto defined arrays of structures - peptides or carbohydrates. The arrays can help analyze the individual specificities or can be used as complex patterns. In any case, the targets recognized should formally be considered mimotopes unless they are proven to be epitopes driving the antibody synthesis. Here, the advantages and disadvantages of the major profiling techniques as well as their current and future application in disease prediction and vaccination are discussed.

A General Method to Discover Epitopes from Sera

Antigen-antibody complexes are central players in an effective immune response. However, finding those interactions relevant to a particular disease state can be arduous. Nonetheless many paths to discovery have been explored since deciphering these interactions can greatly facilitate the development of new diagnostics, therapeutics, and vaccines. In silico B cell epitope mapping approaches have been widely pursued, though success has not been consistent. Antibody mixtures in immune sera have been used as handles for biologically relevant antigens, but these and other experimental approaches have proven resource intensive and time consuming. In addition, these methods are often tailored to individual diseases or a specific proteome, rather than providing a universal platform. Most of these methods are not able to identify the specific antibody’s epitopes from unknown antigens, such as un-annotated neo antigens in cancer. Alternatively, a peptide library comprised of sequences unrestricted by naturally-found protein space provides for a universal search for mimotopes of an antibody’s epitope. Here we present the utility of such a non-natural random sequence library of 10,000 peptides physically addressed on a microarray for mimotope discovery without sequence information of the specific antigen. The peptide arrays were probed with serum from an antigen-immunized rabbit, or alternatively probed with serum pre-absorbed with the same immunizing antigen. With this positive and negative screening scheme, we identified the library-peptides as the mimotopes of the antigen. The unique library peptides were successfully used to isolate antigen-specific antibodies from complete immune serum. Sequence analysis of these peptides revealed the epitopes in the immunized antigen. We present this method as an inexpensive, efficient method for identifying mimotopes of any antibody’s targets. These mimotopes should be useful in defining both components of the antigen-antibody complex.

Identification of non-random sequence properties in groups of signature peptides obtained in random sequence peptide microarray experiments

Immunosignaturing is an emerging experimental technique that uses random sequence peptide microarrays to detect antibodies produced by the immune system in response to a particular disease. Two important questions regarding immunosignaturing are "Do microarray peptides that exhibit a strong affinity to a given type of antibodies share common sequence properties?" and "If so, what are those properties?" In this work, three statistical tests designed to detect non-random patterns in the amino acid makeup of a group of microarray peptides are presented. One test detects patterns of significantly biased amino acid usage, whereas the other two detect patterns of significant bias in the biochemical properties. These tests do not require a large number of peptides per group. The tests were applied to analyze 19 groups of peptides identified in immunosignaturing experiments as being specific for antibodies produced in response to various types of cancer and other diseases. The positional distribution of the biochemical properties of the amino acids in these 19 peptide groups was also studied. Remarkably, despite the random nature of the sequence libraries used to design the microarrays, a unique group-specific non-random pattern was identified in the majority of the peptide groups studied. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 318-329, 2016.

General Assessment of Humoral Activity in Healthy Humans

The humoral immune system is network of biological molecules designed to maintain a healthy homeostatic equilibrium. Because antibodies are an abundant and highly specific effector of immunological action, they are also an important reservoir of previous host exposures. Antibodies may play a major role in early detection of host challenge. Unfortunately, few practical methods exist for interpreting the information stored in antibody variable regions. Immunosignatures use a microarray of thousands of random sequence peptides to interrogate antibodies in a broad and unbiased fashion. The pattern of binding between antibody and peptide is reproducible. Once the system has been trained on a disease cohort, blinded samples can be reliably predicted. Although immunosignatures of both chronic and infectious disease have been extensively tested, less has been done to demonstrate how healthy immunosignatures change over time or between individuals. Here, we report the results of a study of immunosignatures of healthy persons over brief (12 h sampled once per hour), intermediate (32 days sampled once per day), and long (5 years sampled once every year) time spans. Using this information, we were also able to detect intentional and unintentional immunological perturbations in the form of a vaccine and an infection, respectively. Our findings suggest that, even with the variability inherent in healthy immunosignatures, a single person's immunosignature will remain constant over time. Over this healthy signature, vaccines and infections create subsignatures that are common across multiple people, even subsuming healthy fluctuations. These findings have implications for disease monitoring and early diagnosis.

Multiplex localization of sequential peptide epitopes by use of a planar microbead chip

Epitope mapping is crucial for the characterization of protein-specific antibodies. Commonly, small overlapping peptides are chemically synthesized and immobilized to determine the specific peptide sequence. In this study, we report the use of a fast and inexpensive planar microbead chip for epitope mapping. We developed a generic strategy for expressing recombinant peptide libraries instead of using expensive synthetic peptide libraries. A biotin moiety was introduced in vivo at a defined peptide position using biotin ligase. Peptides in crude Escherichia coli lysate were coupled onto streptavidin-coated microbeads by incubation, thereby avoiding tedious purification procedures. For read-out we used a multiplex planar microbead chip with size- and fluorescence-encoded microbead populations. For epitope mapping, up to 18 populations of peptide-loaded microbeads (at least 20 microbeads per peptide) displaying the primary sequence of a protein were analyzed simultaneously. If an epitope was recognized by an antibody, a secondary fluorescence-labeled antibody generated a signal that was quantified, and the mean value of all microbeads in the population was calculated. We mapped the epitopes for rabbit anti-PA28γ (proteasome activator 28γ) polyclonal serum, for a murine monoclonal antibody against PA28γ, and for a murine monoclonal antibody against the hamster polyoma virus major capsid protein VP1 as models. In each case, the identification of one distinct peptide sequence out of up to 18 sequences was possible. Using this approach, an epitope can be mapped multiparametrically within three weeks.

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.

Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures

One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody–peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors in a clinically useful way.

Antibody profiling of bipolar disorder using Escherichia coli proteome microarrays

To profile plasma antibodies of patients with bipolar disorder (BD), an E. coli proteome microarray comprising ca. 4200 proteins was used to analyze antibody differences between BD patients and mentally healthy controls (HCs). The plasmas of HCs and patients aged 18-45 years with bipolar I disorder (DSM-IV) in acute mania (BD-A) along with remission (BD-R) were collected. The initial samples consisting of 19 BD-A, 20 BD-R, and 20 HCs were probed with the microarrays. After selecting protein hits that recognized the antibody differences between BD and HC, the proteins were purified to construct BD focus arrays for training diagnosis committees and validation. Additional six BD-A, six BD-R, six HCs, and nine schizophrenic disorder (SZ, as another psychiatric control) samples were individually probed with the BD focus arrays. The trained diagnosis committee in BD-A versus HC combined top six proteins, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. However, the optimized committees in BD-R versus HC and BD-A versus BD-R were of low accuracy (< 0.6). In the single blind test using another four BD-A, four HC, and four SZ samples, the committee of BD-A versus HC was able to classify BD-A versus HC and SZ with 75% sensitivity and 80% specificity that both HC and SZ were regarded as negative controls. The consensus motif of the six proteins, which form the committee of BD-A versus HC, is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] by Gapped Local Alignment of Motifs. We demonstrated that the E. coli proteome microarray is capable of screening BD plasma antibody differences and the selected proteins committee was successfully used for BD diagnosis with 79% accuracy.

Hydrogen/deuterium exchange mass spectrometry applied to IL-23 interaction characteristics: potential impact for therapeutics

IL-23 is an important therapeutic target for the treatment of inflammatory diseases. Adnectins are targeted protein therapeutics that are derived from domain III of human fibronectin and have a similar protein scaffold to antibodies. Adnectin 2 was found to bind to IL-23 and compete with the IL-23/IL-23R interaction, posing a potential protein therapeutic. Hydrogen/deuterium exchange mass spectrometry and computational methods were applied to probe the binding interactions between IL-23 and Adnectin 2 and to determine the correlation between the two orthogonal methods. This review summarizes the current structural knowledge about IL-23 and focuses on the applicability of hydrogen/deuterium exchange mass spectrometry to investigate the higher order structure of proteins, which plays an important role in the discovery of new and improved biotherapeutics.

Epitope identification from fixed-complexity random-sequence peptide microarrays

Antibodies play an important role in modern science and medicine. They are essential in many biological assays and have emerged as an important class of therapeutics. Unfortunately, current methods for mapping antibody epitopes require costly synthesis or enrichment steps, and no low-cost universal platform exists. In order to address this, we tested a random-sequence peptide microarray consisting of over 330,000 unique peptide sequences sampling 83% of all possible tetramers and 27% of pentamers. It is a single, unbiased platform that can be used in many different types of tests, it does not rely on informatic selection of peptides for a particular proteome, and it does not require iterative rounds of selection. In order to optimize the platform, we developed an algorithm that considers the significance of k-length peptide subsequences (k-mers) within selected peptides that come from the microarray. We tested eight monoclonal antibodies and seven infectious disease cohorts. The method correctly identified five of the eight monoclonal epitopes and identified both reported and unreported epitope candidates in the infectious disease cohorts. This algorithm could greatly enhance the utility of random-sequence peptide microarrays by enabling rapid epitope mapping and antigen identification.

Immunosignature system for diagnosis of cancer

Although the search for disease biomarkers continues, the clinical return has thus far been disappointing. The complexity of the body's response to disease makes it difficult to represent this response with only a few biomarkers, particularly when many are present at low levels. An alternative to the typical reductionist biomarker paradigm is an assay we call an "immunosignature." This approach leverages the response of antibodies to disease-related changes, as well as the inherent signal amplification associated with antigen-stimulated B-cell proliferation. To perform an immunosignature assay, the antibodies in diluted blood are incubated with a microarray of thousands of random sequence peptides. The pattern of binding to these peptides is the immunosignature. Because the peptide sequences are completely random, the assay is effectively disease-agnostic, potentially providing a comprehensive diagnostic on multiple diseases simultaneously. To explore the ability of an immunosignature to detect and identify multiple diseases simultaneously, 20 samples from each of five cancer cohorts collected from multiple sites and 20 noncancer samples (120 total) were used as a training set to develop a reference immunosignature. A blinded evaluation of 120 blinded samples covering the same diseases gave 95% classification accuracy. To investigate the breadth of the approach and test sensitivity to biological diversity further, immunosignatures of >1,500 historical samples comprising 14 different diseases were examined by training with 75% of the samples and testing the remaining 25%. The average accuracy was >98%. These results demonstrate the potential power of the immunosignature approach in the accurate, simultaneous classification of disease.

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays

Background

An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans.

Results

In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets.

Conclusions

These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

Could immunosignatures technology enable the development of a preventative cancer vaccine?

The exciting prospect of developing a universal prophylactic cancer vaccine now seems more possible due to advances in technology and basic knowledge. However, the problem of testing the efficacy of such a vaccine in a clinical trial seems daunting. The low incidence and long lead-time to diagnosis of cancer would make a standard clinical trial long and expensive. Recently, we demonstrated that the immunosignatures diagnostic technology could be useful in evaluating vaccines. The technology is based on profiling the antibody diversity in an individual on a peptide chip platform. Here we propose that this technology may also enable a clinical trial of a preventative vaccine. Preliminary evidence supports the prospect of immunosignatures detecting cancer at very early stages, well before conventional diagnosis. Because the technology is simple and inexpensive, it could be used to monitor the occurrence of cancer in participants and shorten the clinical trial.

A liquid array platform for the multiplexed analysis of synthetic molecule-protein interactions

Synthetic molecule microarrays, consisting of many different compounds spotted onto a planar surface such as modified glass or cellulose, have proven to be useful tools for the multiplexed analysis of small molecule- and peptide-protein interactions. However, these arrays are technically difficult to manufacture and use with high reproducibility and require specialized equipment. Here we report a more convenient alternative composed of color-encoded beads that display a small molecule protein ligand on the surface. Quantitative, multiplexed assay of protein binding to up to 24 different ligands can be achieved using a common flow cytometer for the readout. This technology should be useful for evaluating hits from library screening efforts, the determination of structure activity relationships, and certain types of serological analyses.

Cross-reactivity in antibody microarrays and multiplexed sandwich assays: shedding light on the dark side of multiplexing

Immunoassays are indispensable for research and clinical analysis, and following the emergence of the omics paradigm, multiplexing of immunoassays is more needed than ever. Cross-reactivity (CR) in multiplexed immunoassays has been unexpectedly difficult to mitigate, preventing scaling up of multiplexing, limiting assay performance, and resulting in inaccurate and even false results, and wrong conclusions. Here, we review CR and its consequences in single and dual antibody single-plex and multiplex assays. We establish a distinction between sample-driven and reagent-driven CR, and describe how it affects the performance of antibody microarrays. Next, we review and evaluate various platforms aimed at mitigating CR, including SOMAmers and protein fractionation-bead assays, as well as dual Ab methods including (i) conventional multiplex assays, (ii) proximity ligation assays, (iii) immuno-mass spectrometry, (iv) sequential multiplex analyte capture, (v) antibody colocalization microarrays and (vi) force discrimination assays.

Recent advances in microarray technologies for proteomics

Proteins are fundamental components of all living systems and critical drivers of biological functions. The large-scale study of proteins, their structures and functions, is defined as proteomics. This systems-wide analysis leads to a more comprehensive view of the intricate signaling transduction pathways that proteins engage in and improves the overall understanding of the complex processes supporting the living systems. Over the last two decades, the development of high-throughput analytical tools, such as microarray technologies, capable of rapidly analyzing thousands of protein-functioning and protein-interacting events, has fueled the growth of this important field. Herein, we review the most recent advancements in microarray technologies, with a special focus on peptide microarray, small molecule microarray, and protein microarray. These technologies have become prominent players in proteomics and have made significant changes to the landscape of life science and biomedical research. We will elaborate on their performance, advantages, challenges, and future directions.

Immunosignatures can predict vaccine efficacy

The development of new vaccines would be greatly facilitated by having effective methods to predict vaccine performance. Such methods could also be helpful in monitoring individual vaccine responses to existing vaccines. We have developed "immunosignaturing" as a simple, comprehensive, chip-based method to display the antibody diversity in an individual on peptide arrays. Here we examined whether this technology could be used to develop correlates for predicting vaccine effectiveness. By using a mouse influenza infection, we show that the immunosignaturing of a natural infection can be used to discriminate a protective from nonprotective vaccine. Further, we demonstrate that an immunosignature can determine which mice receiving the same vaccine will survive. Finally, we show that the peptides comprising the correlate signatures of protection can be used to identify possible epitopes in the influenza virus proteome that are correlates of protection.

Advances in blood-based protein biomarkers for Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for the majority of dementia cases. While research over the past decades has made advances into understanding disease pathology, definite AD diagnosis currently relies on confirmation by autopsy. The anticipated dramatic rise in affected individuals over the next decades necessitates the development of diagnostic tests applicable to living individuals, which depends on identification of disease biomarkers. Diagnostics based on blood protein biomarkers are particularly desired since these would allow for economical, rapid and non-invasive analysis of individual biomarker profiles. Research is actively ongoing in this field and has led to the identification of autoantibodies and various proteins in the blood that may represent a disease-specific blood signature of AD. This review provides an overview on the progress in the field of identification of AD-specific blood protein biomarkers.

Discovery of peptoid ligands for anti-aquaporin 4 antibodies

Neuromyelitis optica (NMO) is an autoimmune inflammatory disorder of the central nervous system. In most NMO patients, autoantibodies to the water channel protein Aquaporin 4 (AQP4) are present at high levels and are thought to drive pathology by mediating complement-dependent destruction of astrocytes. Here, we apply recently developed chemical library screening technology to identify a synthetic peptoid that binds anti-AQP4 antibodies in the serum of NMO patients. This finding validates, in a well-defined human disease, that synthetic, unnatural ligands for the antigen-binding site of a disease-linked antibody can be isolated by high-throughput screening.

A technology for developing synbodies with antibacterial activity

The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and in vitro tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding Staphylococcus aureus to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block S. aureus induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.

Characterization of epitopes recognized by monoclonal antibodies: experimental approaches supported by freely accessible bioinformatic tools

Monoclonal antibodies (mAbs) have been used successfully both in research and for clinical purposes. The possible use of protective mAbs directed against different microbial pathogens is currently being considered. The fine definition of the epitope recognized by a protective mAb is an important aspect to be considered for possible development in epitope-based vaccinology. The most accurate approach to this is the X-ray resolution of mAb/antigen crystal complex. Unfortunately, this approach is not always feasible. Under this perspective, several surrogate epitope mapping strategies based on the use of bioinformatics have been developed. In this article, we review the most common, freely accessible, bioinformatic tools used for epitope characterization and provide some basic examples of molecular visualization, editing and computational analysis.

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach

MOTIVATION

Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides.

RESULTS

The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule.

AVAILABILITY

The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.

High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays

Antibodies empower numerous important scientific, clinical, diagnostic, and industrial applications. Ideally, the epitope(s) targeted by an antibody should be identified and characterized, thereby establishing antibody reactivity, highlighting possible cross-reactivities, and perhaps even warning against unwanted (e.g. autoimmune) reactivities. Antibodies target proteins as either conformational or linear epitopes. The latter are typically probed with peptides, but the cost of peptide screening programs tends to prohibit comprehensive specificity analysis. To perform high-throughput, high-resolution mapping of linear antibody epitopes, we have used ultrahigh-density peptide microarrays generating several hundred thousand different peptides per array. Using exhaustive length and substitution analysis, we have successfully examined the specificity of a panel of polyclonal antibodies raised against linear epitopes of the human proteome and obtained very detailed descriptions of the involved specificities. The epitopes identified ranged from 4 to 12 amino acids in size. In general, the antibodies were of exquisite specificity, frequently disallowing even single conservative substitutions. In several cases, multiple distinct epitopes could be identified for the same target protein, suggesting an efficient approach to the generation of paired antibodies. Two alternative epitope mapping approaches identified similar, although not necessarily identical, epitopes. These results show that ultrahigh-density peptide microarrays can be used for linear epitope mapping. With an upper theoretical limit of 2,000,000 individual peptides per array, these peptide microarrays may even be used for a systematic validation of antibodies at the proteomic level.

Immunosignaturing can detect products from molecular markers in brain cancer

Immunosignaturing shows promise as a general approach to diagnosis. It has been shown to detect immunological signs of infection early during the course of disease and to distinguish Alzheimer’s disease from healthy controls. Here we test whether immunosignatures correspond to clinical classifications of disease using samples from people with brain tumors. Blood samples from patients undergoing craniotomies for therapeutically naïve brain tumors with diagnoses of astrocytoma (23 samples), Glioblastoma multiforme (22 samples), mixed oligodendroglioma/astrocytoma (16 samples), oligodendroglioma (18 samples), and 34 otherwise healthy controls were tested by immunosignature. Because samples were taken prior to adjuvant therapy, they are unlikely to be perturbed by non-cancer related affects. The immunosignaturing platform distinguished not only brain cancer from controls, but also pathologically important features about the tumor including type, grade, and the presence or absence of O⁶-methyl-guanine-DNA methyltransferase methylation promoter (MGMT), an important biomarker that predicts response to temozolomide in Glioblastoma multiformae patients.

Comparative study of classification algorithms for immunosignaturing data

BACKGROUND

High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

RESULTS

We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

CONCLUSIONS

'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Deciphering enzyme function using peptide arrays

Enzymes are key molecules in signal-transduction pathways. However, only a small fraction of more than 500 human kinases, 300 human proteases and 200 human phosphatases is characterised so far. Peptide microarray based technologies for extremely efficient profiling of enzyme substrate specificity emerged in the last years. This technology reduces set-up time for HTS assays and allows the identification of downstream targets. Moreover, peptide microarrays enable optimisation of enzyme substrates. Focus of this review is on assay principles for measuring activities of kinases, phosphatases or proteases and on substrate identification/optimisation for kinases. Additionally, several examples for reliable identification of substrates for lysine methyl-transferases, histone deacetylases and SUMO-transferases are given. Finally, use of high-density peptide microarrays for the simultaneous profiling of kinase activities in complex biological samples like cell lysates or lysates of complete organisms is described. All published examples of peptide arrays used for enzyme profiling are summarised comprehensively.

Hepatitis C virus antigenic convergence

Vaccine development against hepatitis C virus (HCV) is hindered by poor understanding of factors defining cross-immunoreactivity among heterogeneous epitopes. Using synthetic peptides and mouse immunization as a model, we conducted a quantitative analysis of cross-immunoreactivity among variants of the HCV hypervariable region 1 (HVR1). Analysis of 26,883 immunological reactions among pairs of peptides showed that the distribution of cross-immunoreactivity among HVR1 variants was skewed, with antibodies against a few variants reacting with all tested peptides. The HVR1 cross-immunoreactivity was accurately modeled based on amino acid sequence alone. The tested peptides were mapped in the HVR1 sequence space, which was visualized as a network of 11,319 sequences. The HVR1 variants with a greater network centrality showed a broader cross-immunoreactivity. The entire sequence space is explored by each HCV genotype and subtype. These findings indicate that HVR1 antigenic diversity is extensively convergent and effectively limited, suggesting significant implications for vaccine development.

Physical characterization of the "immunosignaturing effect"

Identifying new, effective biomarkers for diseases is proving to be a challenging problem. We have proposed that antibodies may offer a solution to this problem. The physical features and abundance of antibodies make them ideal biomarkers. Additionally, antibodies are often elicited early in the ontogeny of different chronic and infectious diseases. We previously reported that antibodies from patients with infectious disease and separately those with Alzheimer's disease display a characteristic and reproducible "immunosignature" on a microarray of 10,000 random sequence peptides. Here we investigate the physical and chemical parameters underlying how immunosignaturing works. We first show that a variety of monoclonal and polyclonal antibodies raised against different classes of antigens produce distinct profiles on this microarray and the relative affinities are determined. A proposal for how antibodies bind the random sequences is tested. Sera from vaccinated mice and people suffering from a fugal infection are individually assayed to determine the complexity of signals that can be distinguished. Based on these results, we propose that this simple, general and inexpensive system could be optimized to generate a new class of antibody biomarkers for a wide variety of diseases.

Evaluation of biological sample preparation for immunosignature-based diagnostics

To address the need for a universal system to assess health status, we previously described a method termed "immunosignaturing" which splays the entire humoral antibody repertoire across a peptide microarray. Two important issues relative to the potential broad use of immunosignatures are sample preparation and stability. In the present study, we compared the immunosignatures developed from serum, plasma, saliva, and antibodies eluted from blood dried onto filter paper. We found that serum and plasma provide identical immunosignatures. Immunosignatures derived from dried blood also correlated well with those from nondried serum from the same individual. Immunosignatures derived from dried blood were capable of distinguishing naïve mice from those infected with influenza virus. Saliva was applied to the arrays, and the IgA immunosignature correlated strongly with that from dried blood. Finally, we demonstrate that dried blood retains immunosignature information even when exposed to high temperature. This work expands the potential diagnostic uses for immunosignatures. These features suggest that different forms of archival samples can be used for diagnosis development and that in prospective studies samples can be easily procured.

GuiTope: an application for mapping random-sequence peptides to protein sequences

BACKGROUND

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task.

RESULTS

GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance.

CONCLUSIONS

GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Impact of immunization technology and assay application on antibody performance--a systematic comparative evaluation

Antibodies are quintessential affinity reagents for the investigation and determination of a protein's expression patterns, localization, quantitation, modifications, purification, and functional understanding. Antibodies are typically used in techniques such as Western blot, immunohistochemistry (IHC), and enzyme-linked immunosorbent assays (ELISA), among others. The methods employed to generate antibodies can have a profound impact on their success in any of these applications. We raised antibodies against 10 serum proteins using 3 immunization methods: peptide antigens (3 per protein), DNA prime/protein fragment-boost ("DNA immunization"; 3 per protein), and full length protein. Antibodies thus generated were systematically evaluated using several different assay technologies (ELISA, IHC, and Western blot). Antibodies raised against peptides worked predominantly in applications where the target protein was denatured (57% success in Western blot, 66% success in immunohistochemistry), although 37% of the antibodies thus generated did not work in any of these applications. In contrast, antibodies produced by DNA immunization performed well against both denatured and native targets with a high level of success: 93% success in Western blots, 100% success in immunohistochemistry, and 79% success in ELISA. Importantly, success in one assay method was not predictive of success in another. Immunization with full length protein consistently yielded the best results; however, this method is not typically available for new targets, due to the difficulty of generating full length protein. We conclude that DNA immunization strategies which are not encumbered by the limitations of efficacy (peptides) or requirements for full length proteins can be quite successful, particularly when multiple constructs for each protein are used.

Segmentation and intensity estimation for microarray images with saturated pixels

Background

Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶ - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.

Results

We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.

Conclusions

The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points.

NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.