A minimal model of peptide binding predicts ensemble properties of serum antibodies
© Greiff et al; licensee BioMed Central Ltd. 2012
Received: 11 June 2011
Accepted: 21 February 2012
Published: 21 February 2012
The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.
Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.
Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.
The functional antibody repertoire (FABR), the set of all antibodies produced by plasma cells at any one time, determines the immune system's perception of the antigen universe. The FABR is shaped throughout the life of an individual by various stages and selection events during B cell development that take place in the fetal liver, in the bone marrow and in secondary lymphatic organs. As the FABR is subject to constant change due to continuous antigen encounter and establishment of immunological memory , it encompasses a variety of specificities and affinities for a wide range of antigens . The FABR's investigation thus provides the possibility to gather information about both past and on-going immune responses, and ultimately about the immune state of the body .
Since the FABR is highly diverse and the production of antibodies is a hallmark of many infectious and autoimmune diseases, high-throughput immunoblot and microarray technologies have been used intensively for large-scale profiling of serum antibody binding [4–9]. Antibody profiling data is widely used for serological diagnostics by exploiting the fact that sera of control and diseased individuals may differ substantially in their FABRs [7, 8, 10–12]. Currently, serum-antibody profiling is usually performed by incubating a serum sample with a peptide or protein microarray. Afterwards, the reactivity of antibodies is estimated by measuring the fluorescence from a fluorochrome-coupled secondary antibody that binds to the constant region of the subset of serum antibodies studied [13, 14].
The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology . This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice.
For prediction of antibody binding profiles, we use a multivariate regression model based exclusively on the peptide library's amino acid composition without taking into account amino acid positional information. This approach is related to methods of linear B cell epitope prediction which rely on propensity scales for epitope prediction [16–19]. Our method contrasts, however, with previously reported quantitative structure-activity relationship (QSAR) modeling which, in conjunction with physico-chemical properties, relates amino acid positions and amino acid compositions of peptides and monoclonal antibodies to various response variables [20–22]. We propose to examine, in vitro and in silico, the extent to which the validity of our approach depends on the composition of antibody mixtures.
The regression model led to the definition of amino acid-associated weights (AAWS) as predictors of antibody-peptide reactivity. We found that the position-independent peptide amino acid composition accounts for up to 40-50% in variation of antibody-peptide binding for healthy mice.
We demonstrate with a mathematical model the ensemble properties of highly diverse, random antibody mixtures in which no antibody dominates. We call these mixtures "unbiased" and show that the properties of unbiased mixtures are the foundation to a high predictive performance of AAWS. We hypothesize that serum antibodies of healthy individuals resemble an unbiased mixture, while during an acute immune response, specific antibodies dominate antibody-peptide binding thus lowering predictive performance. Based on in silico and in vitro evidence, our work thus suggests that the faithfulness of antibody-peptide binding prediction with propensity scales [16–19] decreases with increasing antibody dominance in a mixture.
In order to investigate the binding of antibody mixtures to large random-sequence peptide libraries, we asked two main questions: i) what is the impact of the peptides' amino acid composition on the binding to serum antibodies, ii) and how does the serum-antibody composition influence binding prediction?
To study the impact of amino acid composition of random-sequence peptide libraries on measured signal intensity, serum samples from 15 BALB/c mice bred under specific pathogen-free (SPF) conditions were collected. These mice were infected with HB (Additional file 1, Figure S1). Further serum samples were collected at 10 dpi (days post infection; 15 samples), at 14 dpi (13 samples) and at 18 dpi (15 samples) totaling 58 serum samples. Microarrays of nPep = 255 random-sequence peptide probes (hereafter referred to as standard library) were incubated with the serum samples. The peptide arrays used have been shown to be suitable for serological diagnostics by Bongartz et al. . Each probe consisted of l = 14 out of 20 proteinogenic amino acids. IgM and IgG antibody binding was simultaneously detected by means of isotype specific fluorochrome-labeled polyclonal secondary antibodies. In addition to serum samples, the peptide library was incubated separately with 13 different human monoclonal IgG antibodies.
The fluorescence signal intensities were read, log-transformed and corrected for the signal from the polyclonal secondary antibody binding directly to the peptide probes. Subsequently, the signal intensities were mean-centered and scaled to unit variance, which resulted in a normalized vector for each IgM and IgG serum sample and for each of the 13 monoclonal antibodies. We use the terms signal intensity or antibody binding profile interchangeably to denote . Each signal intensity vector has as many components as there are peptides in the standard random peptide libary. For brevity, our analysis focuses on the IgM data. The IgG data can be found in the Supporting Information (Additional file 2, Figure S2, Additional file 3, Figure S3, and Additional file 4, Figure S4). More details on the experimental setup and normalization procedures can be found in Methods.
A regression model based exclusively on peptide amino acid composition predicts antibody binding profiles
where is the signal intensity vector and X the amino acid composition matrix (AACM) of the peptide library. The X matrix is formed by counting the occurrences of each of the 20 amino acids in each peptide which results in a matrix with 20 columns and 255 rows. Importantly, X does not contain information about the position of an amino acid in a given peptide sequence.
The AAWS vector indicates the contribution of every amino acid to the measured signal intensity. Furthermore, the residual of the regression model, , captures the part of which cannot be explained by X alone. AAWS and residuals were estimated by partial least squares regression (PLS) (see Methods for details on the data analysis).
A minimal model of antibody-peptide binding
We hypothesize that the high predictive performance of our regression model is due to properties of an antibody ensemble. We test this hypothesis with the help of a model that simulates binding between peptides and antibodies. In this model, the binding affinity of simulated monoclonal antibodies depends non-linearly on amino acid positions in the peptide sequences (Equations 2 and 4). The model we propose is similar to bit string models [23–26] in that it uses vectors as simple representations of peptides and antibodies. The peptide string is represented by unique real numbers taken from a vector of assigned AAWS, denoted , the twenty components of which were drawn from a uniform distribution on the closed interval 0. A peptide of l amino acids is thus represented by a vector of l numbers drawn from .
An antibody binding site is represented by a vector of length l. The binding strength of each position is given by a number between -1 and 1 that is drawn randomly from a uniform distribution and is scaled such that . The binding association between peptide and antibody is computed as the dot product of the two vectors, . Thus, the binding association y i, k depends explicitly on an amino acid's position in a given peptide sequence.
where [Ab] k is the concentration of antibody k with . The thermodynamic equilibrium association constant for antibody k binding peptide i is defined as with . Logarithmizing the results of Equation 2, and centering them to zero and unit variance, we obtained a vector of normalized simulated signal intensities . A more detailed description of the mathematical model can be found in Methods.
Simulations show that the prediction of antibody binding profiles based exclusively on peptide amino acid composition improves with increasing antibody diversity
Predictive performance differs for monoclonal and serum-antibody binding profiles
In order to test our in silico-based prediction that predictive performance depends heavily on antibody diversity when only taking into account the peptide library's amino acid composition, we compared the predictive performance of the 58 BALB/c mouse serum samples (antibody diversity nAb >> 1) with that of the 13 human monoclonal IgG antibodies (antibody diversity nAb = 1). We found both a significantly higher predictive performance (Figure 2A, p < 0.001) and significantly higher pairwise correlations between AAWS for serum antibodies (Figure 2B, p < 0.001) than for monoclonal antibodies, which confirms the predictions of our mathematical model (Figure 4).
Predictive performance decreases in the course of an HB-infection
Stages of murine immune response differ in their amino acid-associated weights
Average amino acid-associated weights of healthy mice correlate with amino acid physico-chemical properties but not with widely used amino acid scales for epitope prediction
AAWS represent a priority scale for peptide-antibody binding assigning to every amino acid the importance of contribution to the measured (or simulated) signal intensity. In addition, analogously to QSAR modeling, AAWS can a posteriori be conceived of as a vector representing correlates of the respective amino acids' physico-chemical properties. We therefore correlated the average AAWS (Figure 8) with the z-scale developed by Sandberg and colleagues . The z-scale aggregates in matrix form 26 physico-chemical amino acid properties for every one of the 20 examined amino acids (Additional file 6, Figure S6). The average AAWS yield an absolute correlation coefficient higher than 0.3 with the following physico-chemical properties: side chain van der Waals volume, alpha-polarizability, absolute electronegativity, number of hydrogen bond donors, total accessible molecular surface area, and indicator of negative charge in side chain.
In order to compare the average AAWS with other published amino acid-scales for epitope prediction, we correlated them with propensity scales published by Parker and colleagues  (hydrophilicity), Kolaskar and Tongaonkar  (antigenicity), Chou and Fasman  (secondary structure) and by Emini and colleagues  (accessibility) and found the resemblance with them to be poor (absolute values of correlation coefficients smaller than 0.22). Notably, the compared propensity scales also do not highly correlate (range of correlation coefficients: -0.61 to 0.67).
Amino acid-associated weights are a compact, information-preserving representation of serum-antibody binding profiles
A minimal linear regression model defines AAWS as predictors that are based solely on the amino acid composition of a given peptide. For serum antibodies of BALB/c mice, AAWS account for up to 50% of variation in antibody binding profiles, whereas monoclonal antibodies generally show poor predictive performance values. The regression model performs best for healthy mice (median Q2 = 0.43, Figure 5). Furthermore, we find AAWS to be comparable across healthy BALB/c mouse serum samples (Figure 5B). During the immune response against HB, however, predictive performance decreases steadily. Accordingly, pairwise correlations of AAWS are highest for healthy mice and decrease during the immune response (Figure 5). Therefore, we hypothesize that the average AAWS for healthy mice, shown in Figure 8, are a signature of health. AAWS of infected mice, in turn, are systematically different from AAWS of healthy mice and can be separated by principal component analysis.
Simulated unbiased antibody mixtures show ensemble properties
In order to interpret the reported experimental results, we built a mathematical model based on the law of mass action. We defined a property vector that characterizes each peptide's amino acid binding strength. In this model, the binding signals for a given simulated monoclonal antibody depend on the amino acid's position in a given peptide.
For a single simulated antibody, AAWS calculated by the amino acid composition-based linear regression model generally yield neither good predictive performance nor a high correlation with assigned AAWS . However, highly diverse antibody mixtures with random--in the sense of an independent identically distributed--repertoire, and no dominant antibodies, show both a close to perfect predictive performance and recovery of assigned AAWS (Figure 4 and Additional file 5, Figure S5). Our mathematical model thus predicts that high predictive performance and high correlation of estimated AAWS and are ensemble properties of such antibody mixtures: the average affinity of these mixtures does not depend on the epitope's amino acid position anymore. In contrast to that, the monoclonal antibody-epitope affinities do. We call random and highly diverse antibody mixtures that are not biased by dominant antibodies "unbiased". In fact, introducing, in simulations, a dominant antibody by increasing the concentration of a single antibody decreases predictive performance (Figure 6A). In addition, we showed that noise reduces predictive performance (Figure 6A).
Serum samples of healthy BALB/c mice show signs of unbiased antibody mixtures
As shown in our mathematical model, unbiased antibody mixtures are characterized by high predictive performance values. In view of the relatively high predictive performance of antibody binding profiles of serum samples from healthy BALB/c mice, we postulate that these sera exhibit properties of unbiased antibody mixtures.
The first prerequisite for an unbiased mixture is high diversity. This requirement seems to be met. The potential antibody diversity is very high , and the functional diversity is estimated to be of the order of 104 . However, fulfillment of the second requirement, the independent identical distribution of antibody binding sites, is harder to claim. On the one hand, the antibody repertoire is composed of preexisting gene segments and shaped by clonal selection, but on the other hand, V(D)J recombination and--in later stages of an immune response--somatic hypermutation arrange and mutate these segments in a largely random fashion . Our results suggest that randomness in fact prevails. This is consistent with the hypothesis that antibody repertoires can potentially recognize the entire antigenic universe [33, 34].
The predictive performance of healthy BALB/c mice is not perfect but amounts to a median of 0.43. This can be due to both noise and the fact that serum violates the assumptions of randomness to a certain degree. Noise may be caused by varying peptide spot quality on microarrays and by the experimental procedure itself. It is known that during a primary acute immune response, antibodies of a certain specificity for the antigen are produced in high abundance [35, 36]. Therefore, it can be expected that sera of infected mice deviate from the properties of an unbiased mixture and would have reduced predictive performance values. Indeed, this is corroborated by experimental results (Figures 5 and 7).
Unbiased mixtures represent a special case for which the use of propensity scales for epitope prediction is justified
The prediction of linear B-cell epitopes was first done by using propensity scales [19, 37, 38]. These scales assign a propensity value to every amino acid based on a priori studies of their physico-chemical properties. We found that our average AAWS, a posteriori termed signature of health (Figure 8), are not significantly correlated to widely used propensity scales.
Blythe and Flower tested 484 amino acid propensity scales on a set of 50 epitope-mapped proteins. They found that even the best set of scales perform only marginally better than random . We show that unbiased mixtures represent a special case for which the converse holds true: antibody binding profiles of unbiased mixtures can be predicted based on AAWS. We show that the use of amino acid scales becomes increasingly less justified with increasing dominance of antibodies in a serum. In fact, each of Blythe and Flower's experiments used polyclonal antibodies raised against the whole protein . We conjecture that the used polyclonal antibody mixtures were biased, that is, they contained dominant antibodies. In this regard, our study provides a possible explanation to Blythe and Flower's survey. More generally, our work suggests that results obtained with polyclonal antibody mixtures tend to be skewed by the inherent ensemble properties, which obscure the affinities of epitope-specific antibodies.
Technological features may bias amino acid-associated weights
We have shown that antibody mixtures exhibit ensemble properties. Resulting AAWS were shown to be consistent across healthy mice and qualitatively different from AAWS of infected mice (Figure 7). We have also provided a possible explanation for the difference between AAWS of healthy and infected mice: dominant antibodies in the course of the immune response.
However, the actual signature of health values shown in Figure 8 should be interpreted with caution. In addition to being indicative of both amino acid antibody binding preferences and physico-chemical properties (Figure 8 and Additional file 6, Figure S6), signal intensity may also be influenced by two other factors: (i) the accessibility of peptides and (ii) a possible interaction of aromatic amino acids and aromatic labeling dyes.
Accessibility may bias the resulting signal intensities systematically. For example, we find that cysteine contributes negatively to the signal intensity. This could partly be due to its ability to form disulfide bonds, which causes increased aggregation of cystein-containing peptides, and diminishes their surface exposure. This would lead to reduced antibody-peptide binding and accordingly to reduced signal intensity. Furthermore, it cannot be ruled out that aromatic amino acids interact via π-stacking with the aromatic labeling dyes Alexa Fluor 546 and 647 which are coupled to the secondary antibodies. Indeed, it has recently been found that TAMRA, another aromatic dye, cross-reacts with individual amino acids in a peptide sequence . In order to minimize this effect, we performed secondary antibody correction on the log-transformed signal intensities.
We show that due to ensemble properties of unbiased mixtures, the position of amino acids in a linear epitope is no longer determinative for binding prediction. We found that prediction of peptide-binding as well as consistence of AAWS was best in sera of healthy BALB/c mice. Therefore, we defined a signature of health characterizing the binding behavior of serum of healthy individuals. This finding has far-reaching significance for the field of serological diagnostics.
Furthermore, our findings have also deep implications for the field of B cell epitope mapping as we have discovered an important special case which enables amino acid scale prediction of peptide binding. We show that amino acid scale prediction of peptide binding is justified only for unbiased mixtures. For other cases, alternative methods have to be sought. We thus showed that a knowledge of the composition of the used polyclonal mixture is essential for both the choice of the prediction method as well as the interpretation of results.
In the future, it will be of great interest to investigate the effects of a more detailed representation of binding in the mathematical model, and to study the effect of non-uniform antibody concentration distributions on predictive performance. Indeed, it has recently been shown for healthy zebrafish that the B cell clone repertoire follows a power-law distribution . Thanks to our minimal assumptions approach, the conclusions of our model are independent of species, genetical background and individual exposure history. Future studies have to verify these predictions.
Animals were housed and handled following national guidelines and as approved by our animal ethics committee.
BALB/c mice were bred and maintained under specific pathogen-free (SPF) conditions by the Department of Molecular Parasitology, Humboldt University Berlin, Berlin, Germany. Infection of mice with HB was carried out by oral gavage with 200 L3 stage larvae in distilled water.
Mice were narcotized and bled either by cardiac or retro-orbital puncture at the age of 8 weeks. Blood samples were collected from healthy SPF-BALB/c mice (n = 15), which were then infected with HB. Blood was collected at three time points post infection (dpi): at 10 dpi (n = 15), 14 dpi (n = 13) and 18 dpi (n = 15). The blood was allowed to clot at room temperature and centrifuged. The supernatant was stored at -20°C.
The 13 human monoclonal antibodies were kindly provided by the group of Hedda Wardemann (Max Planck Institute for Infection Biology, Berlin, Germany). Ten different Ig gene sequences of IgG+ memory B cells from 2 healthy human donors, PN and VB, (PN115, PN138, PN16, PN89, VB1, VB142, VB161, VB176, VB18, VB4)  and three further ones from 3 other human donors ED38 , eiJB40 and mGO53  were expressed as detailed in .
Random peptide library
The peptide library consists of 255 different 14-mer peptides. Their sequence was designed with a random generator. Repetitions of three or more consecutive amino acids were not allowed.
Peptide synthesis and microarray design
The peptide library was displayed in five identical sub-arrays on each slide purchased from JPT Peptide Technologies GmbH, Berlin, Germany. Furthermore, TAMRA-derived peptides, as internal fluorescence control, and mouse-IgM, mouse-IgG, human-IgM and human-IgG as secondary antibody controls, were included on each sub-array. Peptide microarrays were stored at 4°C.
Antibody binding assays
The microarrays were briefly immersed in 100% v/v ethanol, washed three times with T-PBS (phosphate buffered saline containing 0.05% w/v Tween20), three times with deionized water and dried by centrifugation. Since the microarray surfaces had been pre-treated to minimize unspecific binding of the target antibodies, no blocking step was required prior to incubation. All incubations were performed using a five-well adhesive incubation chamber (Multiwell GeneFrameTM, ABgene Germany, Hamburg, Germany) with a total assay volume of 45μ L per well. Serum was diluted 1:10 in T-PBS and monoclonal antibodies were applied in a concentration of 10μ g/mL. We showed in a technological case study that approximately 10μ g/ml of antibody are best for reliable signal intensity measurements . The concentration of IgM in in the serum of healthy SPF BALB/c mice was found to be around 0.50 mg/ml , which yields 50μ g/ml for a 1:10 dilution. The diluted sera are thus within the optimal binding range. After incubation for four hours at room temperature, the microarrays were washed three times with T-PBS and three times with deionized water. Serum-antibody binding was detected with polyclonal goat anti-mouse IgM-Alexa Fluor 546 and polyclonal goat anti-mouse IgG-Alexa Fluor 647 (Invitrogen Ltd, Paisley, UK), simultaneously.
Monoclonal antibody binding was detected with polyclonal goat anti-human IgG Alexa Fluor 647 (Invitrogen Ltd, Paisley, UK). Secondary antibodies were diluted in T-PBS (20μ g/mL, 300μ L) and incubated for one hour at room temperature. The microarrays were washed three times with T-PBS, three times with deionized water, rinsed with running deionized water and dried by centrifugation. Water, ethanol and PBS were filtered.
By PLS-based computation of the intercepts, β0 and β1, we replaced log(I) with the resulting PLS-computed, mean-centered and scaled-to-unit variance residuals ε for further analysis. The results reported in the main text of this paper are based exclusively on the calculated normalized residuals.
The two-sided, non-paired Wilcoxon rank sum test was used to compute all p-values. P-values were regarded as significant when p < 0.05. Association between variables was assessed by Pearson correlation (r) unless otherwise stated.
Generation of simulated signal intensities with a mathematical model
Peptides and antibody binding sites were modeled as strings. Binding strengths between antibodies and the various amino acid residues of a peptide, referred to as assigned AAWS , were sampled from the uniform distribution on the closed interval 0. A binding site on an antibody was simulated in a similar fashion with a random number from the closed interval [-1, 1] for every sequential position and scaled such that . The binding association between peptide and antibody was calculated by .
Similar to a bit string model approach in , our approach to calculating K i, k assumes additivity in free energy of binding, an assumption that is supported by experimental results [48, 49]. The signal intensity that we measure on the array is assumed to be proportional to the ratio of bound-to-total surface of the peptide spot, S i . An expression for this quantity, based on the law of mass action, can be obtained from classical Langmuir adsorption theory  resulting in Equation 2 with R = 8.314472, T = 273.15 + 25, β0 = 0 and β1 = RT.
At last, signal intensities were log-transformed, mean-centered, and scaled to unit variance. If Gaussian noise ((μ = 0, σ = 0.01)) was introduced into simulated signal intensities, the noise term was introduced before logarithmic transformation of the data. We showed that, for monoclonal antibodies, visibly fluorescent spots have at least a K-value of 107M-1 .
Partial least squares regression
The vector is the left-out test data set, the signal intensity of which is predicted () from the remaining training data set. The left-out test data represented randomly chosen 10% of the total data set.
Principal component analysis
Principal component analysis was performed using the pcaMethods R-package .
List of Abbreviations
Amino acid composition matrix
Amino acid-associated weights
Functional antibody repertoire
Partial least squares regression.
We thank René Riedel (German Rheumatism Research Center, Berlin, Germany), Carsten C. Mahrenholz (Charité - University Medicine Berlin, Berlin, Germany), Johannes Eckstein and Nicole Wittenbrink (Systems Immunology Lab, Dept. of Biology, Humboldt University Berlin, Berlin, Germany) for critical reading of the manuscript. We thank H. Wardemann (Max Planck Institute for Infection Biology, Berlin, Germany) and her lab for providing the monoclonal antibodies. The authors were funded by BMBF Grant 315005B and the ProFit Grant IBB/EFRES (10142548).
- Abbas AK, Lichtman A: Cellular and Molecular Immunology. 2005, Saunders, 5Google Scholar
- Nobrega A, Grandien A, Haury M, Hecker L, Malanchère E, Coutinho A: Functional diversity and clonal frequencies of reactivity in the available antibody repertoire. European Journal of Immunology. 1998, 28 (4): 1204-1215. 10.1002/(SICI)1521-4141(199804)28:04<1204::AID-IMMU1204>3.0.CO;2-G.View ArticlePubMedGoogle Scholar
- Quintana FJJ, Merbl Y, Sahar E, Domany E, Cohen IR: Antigen-chip technology for accessing global information about the state of the body. Lupus. 2006, 15 (7): 428-430. 10.1191/0961203306lu2328oa.View ArticlePubMedGoogle Scholar
- Nobrega A, Haury M, Grandien A, Malanchère E, Sundblad A, Coutinho A: Global analysis of antibody repertoires. II. Evidence for specificity, self-selection and the immunological "homunculus" of antibodies in normal serum. European Journal of Immunology. 1993, 23 (11): 2851-2859. 10.1002/eji.1830231119.View ArticlePubMedGoogle Scholar
- Haury M, Grandien A, Sundblad A, Coutinho A, Nobrega A: Global analysis of antibody repertoires. 1. An immunoblot method for the quantitative screening of a large number of reactivities. Scandinavian Journal of Immunology. 1994, 39: 79-87. 10.1111/j.1365-3083.1994.tb03343.x.View ArticlePubMedGoogle Scholar
- Robinson WH, DiGennaro C, Hueber W, Haab BB, Kamachi M, Dean EJ, Fournel S, Fong D, Genovese MC, de Vegvar HEN, Skriner K, Hirschberg DL, Morris RI, Muller S, Pruijn GJ, van Venrooij WJ, Smolen JS, Brown PO, Steinman L, Utz PJ: Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat Med. 2002, 8 (3): 295-301. 10.1038/nm0302-295.View ArticlePubMedGoogle Scholar
- Quintana FJ: Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes. Proceedings of the National Academy of Sciences. 2004, 101 (suppl_2): 14615-14621.View ArticleGoogle Scholar
- Merbl Y, Itzchak R, Vider-Shalit T, Louzoun Y, Quintana FJ, Vadai E, Eisenbach L, Cohen IR: A systems immunology approach to the host-tumor interaction: large-scale patterns of natural autoantibodies distinguish healthy and tumor-bearing mice. PloS One. 2009, 4 (6): e6053-10.1371/journal.pone.0006053.PubMed CentralView ArticlePubMedGoogle Scholar
- Cekaite L, Haug O, Myklebost O, Aldrin M, Østenstad B, Holden M, Frigessi A, Hovig E, Sioud M: Analysis of the humoral immune response to immunoselected phage-displayed peptides by a microarray-based method. PROTEOMICS. 2004, 4 (9): 2572-2582. 10.1002/pmic.200300768.View ArticlePubMedGoogle Scholar
- Bongartz J, Bruni N, Or-Guil M: Epitope mapping using randomly generated peptide libraries. Methods in Molecular Biology (Clifton, N.J.). 2009, 524: 237-246. 10.1007/978-1-59745-450-6_17.View ArticleGoogle Scholar
- Legutki JB, Magee DM, Stafford P, Johnston SA: A general method for characterization of humoral immunity induced by a vaccine or infection. Vaccine. 2010, 28 (28): 4529-4537. 10.1016/j.vaccine.2010.04.061.View ArticlePubMedGoogle Scholar
- Reddy MM, Wilson R, Wilson J, Connell S, Gocke A, Hynan L, German D, Kodadek T: Identification of Candidate IgG Biomarkers for Alzheimer's Disease via Combinatorial Library Screening. Cell. 2011, 144: 132-142. 10.1016/j.cell.2010.11.054.PubMed CentralView ArticlePubMedGoogle Scholar
- Weiser AA, Or-Guil M, Tapia V, Leichsenring A, Schuchhardt J, Frömmel C, Volkmer-Engert R: SPOT synthesis: Reliability of array-based measurement of peptide binding affinity. Analytical Biochemistry. 2005, 342 (2): 300-311. 10.1016/j.ab.2005.04.033.View ArticlePubMedGoogle Scholar
- Tapia V, Bongartz J, Schutkowski M, Bruni N, Weiser A, Ay B, Volkmer R, Or-Guil M: Affinity profiling using the peptide microarray technology: A case study. Analytical Biochemistry. 2007, 363: 108-118. 10.1016/j.ab.2006.12.043.View ArticlePubMedGoogle Scholar
- Brown JR, Stafford P, Johnston SA, Dinu V: Statistical methods for analyzing immunosignatures. BMC Bioinformatics. 2011, 12: 349-10.1186/1471-2105-12-349.PubMed CentralView ArticlePubMedGoogle Scholar
- Chou PY, Fasman GD: Prediction of the secondary structure of proteins from their amino acid sequence. Advances in Enzymology and Related Areas of Molecular Biology. 1978, 47: 45-148.PubMedGoogle Scholar
- Parker JM, Guo D, Hodges RS: New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry. 1986, 25 (19): 5425-5432. 10.1021/bi00367a013.View ArticlePubMedGoogle Scholar
- Emini EA, Hughes JV, Perlow DS, Boger J: Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of Virology. 1985, 55 (3): 836-839.PubMed CentralPubMedGoogle Scholar
- Hopp TP, Woods KR: Prediction of protein antigenic determinants from amino acid sequences. Proceedings of the National Academy of Sciences of the USA. 1981, 78 (6): 3824-3828. 10.1073/pnas.78.6.3824.PubMed CentralView ArticlePubMedGoogle Scholar
- Mandrika I, Prusis P, Yahorava S, Tars K, Wikberg JE: QSAR of multiple mutated antibodies. Journal of Molecular Recognition. 2007, 20 (2): 97-102. 10.1002/jmr.817.View ArticlePubMedGoogle Scholar
- Mandrika I, Prusis P, Yahorava S, Shikhagaie M, Wikberg JE: Proteochemometric modelling of antibody-antigen interactions using SPOT synthesised peptide arrays. Protein Engineering, Design and Selection. 2007, gzm022-Google Scholar
- Mandrika I, Prusis P, Bergström J, Yahorava S, Wikberg JES: Improving the affinity of antigens for mutated antibodies by use of statistical molecular design. Journal of Peptide Science: An Official Publication of the European Peptide Society. 2008, 14 (7): 786-796.View ArticleGoogle Scholar
- De Boer RJ, Perelson AS: T Cell Repertoires and Competitive Exclusion. Journal of Theoretical Biology. 1994, 169 (4): 375-390. 10.1006/jtbi.1994.1160.View ArticlePubMedGoogle Scholar
- Perelson AS: Immune Network Theory. Immunological Reviews. 1989, 110: 5-36. 10.1111/j.1600-065X.1989.tb00025.x.View ArticlePubMedGoogle Scholar
- Sulzer B, Perelson AS: Equilibrium binding of multivalent ligands to cells: Effects of cell and receptor density. Mathematical Biosciences. 1996, 135 (2): 147-185. 10.1016/0025-5564(96)00022-3.View ArticlePubMedGoogle Scholar
- Perelson AS, Weisbuch G: Immunology for physicists. Reviews of Modern Physics. 1997, 69 (4): 1219-10.1103/RevModPhys.69.1219.View ArticleGoogle Scholar
- Alkhamis KA, Wurster DE: Prediction of adsorption from multicomponent solutions by activated carbon using single-solute parameters. Part II-Proposed equation. AAPS PharmSciTech. 2002, 3 (3): E23-10.1208/pt030323.View ArticlePubMedGoogle Scholar
- Rausch S, Huehn J, Kirchhoff D, Rzepecka J, Schnoeller C, Pillai S, Loddenkemper C, Scheffold A, Hamann A, Lucius R, Hartmann S: Functional Analysis of Effector and Regulatory T Cells in a Parasitic Nematode Infection. Infect Immun. 2008, 76 (5): 1908-1919. 10.1128/IAI.01233-07.PubMed CentralView ArticlePubMedGoogle Scholar
- Sandberg M, Eriksson L, Jonsson J, Sjöström M, Wold S: New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. Journal of Medicinal Chemistry. 1998, 41 (14): 2481-2491. 10.1021/jm9700575.View ArticlePubMedGoogle Scholar
- Kolaskar A, Tongaonkar PC: A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Letters. 1990, 276 (1-2): 172-174. 10.1016/0014-5793(90)80535-Q.View ArticlePubMedGoogle Scholar
- Berek C, Griffiths GM, Milstein C: Molecular events during maturation of the immune response to oxazolone. Nature. 1985, 316 (6027): 412-418. 10.1038/316412a0.View ArticlePubMedGoogle Scholar
- Brissac C, Nobrega A, Carneiro J, Stewart J: Functional diversity of natural IgM. Int Immunol. 1999, 11 (9): 1501-1507. 10.1093/intimm/11.9.1501.View ArticlePubMedGoogle Scholar
- Jerne NK: The natural-selection theory of antibody formation. Proceedings of the National Academy of Sciences of the USA. 1955, 41 (11): 849-10.1073/pnas.41.11.849.PubMed CentralView ArticlePubMedGoogle Scholar
- Perelson AS, Oster GF: Theoretical studies of clonal selection: Minimal antibody repertoire size and reliability of self-non-self discrimination. Journal of Theoretical Biology. 1979, 81 (4): 645-670. 10.1016/0022-5193(79)90275-3.View ArticlePubMedGoogle Scholar
- Janeway C, Shlomchik MJ, Walport : Immunobiology. 2004, Garland Science, 6Google Scholar
- McCoy KD, Stoel M, Stettler R, Merky P, Fink K, Senn BM, Schaer C, Massacand J, Odermatt B, Oettgen HC, Zinkernagel RM, Bos NA, Hengartner H, Macpherson AJ, Harris NL: Polyclonal and Specific Antibodies Mediate Protective Immunity against Enteric Helminth Infection. Cell Host & Microbe. 2008, 4 (4): 362-373. 10.1016/j.chom.2008.08.014.View ArticleGoogle Scholar
- Greenbaum JA, Andersen PH, Blythe M, Bui H, Cachau RE, Crowe J, Davies M, Kolaskar AS, Lund O, Morrison S, Mumey B, Ofran Y, Pellequer J, Pinilla C, Ponomarenko JV, Raghava GPS, van Regenmortel MHV, Roggen EL, Sette A, Schlessinger A, Sollner J, Zand M, Peters B: Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. Journal of Molecular Recognition. 2007, 20 (2): 75-82. 10.1002/jmr.815.View ArticlePubMedGoogle Scholar
- EL-Manzalawy Y, Honavar V: Recent advances in B-cell epitope prediction methods. Immunome Research. 2010, 6 (Suppl 2): S2-10.1186/1745-7580-6-S2-S2.PubMed CentralView ArticlePubMedGoogle Scholar
- Blythe MJ, Flower DR: Benchmarking B cell epitope prediction: Underperformance of existing methods. Protein Science: A Publication of the Protein Society. 2005, 14: 246-248.View ArticleGoogle Scholar
- Mahrenholz CC, Tapia V, Stigler RD, Volkmer R: A study to assess the cross-reactivity of cellulose membrane-bound peptides with detection systems: an analysis at the amino acid level. Journal of Peptide Science: An Official Publication of the European Peptide Society. 2010, 16 (6): 297-302.Google Scholar
- Weinstein JA, Jiang N, White RA, Fisher DS, Quake SR: High-Throughput Sequencing of the Zebrafish Antibody Repertoire. Science. 2009, 324 (5928): 807-810. 10.1126/science.1170020.PubMed CentralView ArticlePubMedGoogle Scholar
- Tiller T, Tsuiji M, Yurasov S, Velinzon K, Nussenzweig MC, Wardemann H: Autoreactivity in human IgG+ memory B cells. Immunity. 2007, 26 (2): 205-213. 10.1016/j.immuni.2007.01.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Meffre E, Schaefer A, Wardemann H, Wilson P, Davis E, Nussenzweig MC: Surrogate Light Chain Expressing Human Peripheral B Cells Produce Self-reactive Antibodies. The Journal of Experimental Medicine. 2004, 199: 145-150.PubMed CentralView ArticlePubMedGoogle Scholar
- Wardemann H, Yurasov S, Schaefer A, Young JW, Meffre E, Nussenzweig MC: Predominant autoantibody production by early human B cell precursors. Science (New York, N.Y.). 2003, 301 (5638): 1374-1377. 10.1126/science.1086907.View ArticleGoogle Scholar
- Tiller T, Meffre E, Yurasov S, Tsuiji M, Nussenzweig MC, Wardemann H: Efficient generation of monoclonal antibodies from single human B cells by single cell RT-PCR and expression vector cloning. Journal of immunological methods. 2008, 329 (1-2): 112-124. 10.1016/j.jim.2007.09.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Haury M, Sundblad A, Grandien A, Barreau C, Coutinho A, Nobrega A: The repertoire of serum IgM in normal mice is largely independent of external antigenic contact. European journal of immunology. 1997, 27 (6): 1557-1563. 10.1002/eji.1830270635.View ArticlePubMedGoogle Scholar
- Rosenwald S, Kafri R, Lancet D: Test of a Statistical Model for Molecular Recognition in Biological Repertoires. Journal of Theoretical Biology. 2002, 216 (3): 327-336. 10.1006/jtbi.2002.2538.View ArticlePubMedGoogle Scholar
- Horovitz A, Rigbi M: Protein-protein interactions: Additivity of the free energies of association of amino acid residues. Journal of Theoretical Biology. 1985, 116: 149-159. 10.1016/S0022-5193(85)80135-1.View ArticlePubMedGoogle Scholar
- Free SM, Wilson JW: A Mathematical Contribution to Structure-Activity Studies. Journal of Medicinal Chemistry. 1964, 7 (4): 395-399. 10.1021/jm00334a001.View ArticlePubMedGoogle Scholar
- Mevik B, Wehrens R: The pls Package: Principal Component and Partial Least Squares Regression in R. Journal of Statistical Software. 2007, 18 (2): 1-24.View ArticleGoogle Scholar
- Team RDC: R: A Language and Environment for Statistical Computing. 2009, Vienna, AustriaGoogle Scholar
- Stacklies W, Redestig H, Scholz M, Walther D, Selbig J: pcaMethods a bioconductor package providing PCA methods for incomplete data. Bioinformatics. 2007, 23 (9): 1164-1167. 10.1093/bioinformatics/btm069.View ArticlePubMedGoogle Scholar
- Behnke J, Harris PD: Heligmosomoides bakeri: a new name for an old worm?. Trends in Parasitology. 2010Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.