Prediction Of Allergenicity Research Articles

Protein expression of Myt272-3 recombinant clone and in silico prediction of a possible vaccine candidate against <i>Mycobacterium tuberculosis</i>

Purpose : To investigate the expression of Myt272-3 recombinant protein and also to predict a possible protein vaccine candidate against Mycobacterium tuberculosis. Methods : Myt272-3 protein was expressed in pET30a+-Myt272-3 clone. The purity of the protein was determined using Dynabeads® His-Tag Isolation & Pulldown. Protein sequence was analysed in silico using bioinformatics software for the prediction of allergenicity, antigenicity, MHC-I and MHC-II binding, and B-cell epitope binding. Results : The candidate protein was a non-allergen with 15.19 % positive predictive value. It was also predicted to be antigenic, with binding affinity to MHC-I and MHC-II, as well as B-cell epitope binding. Conclusion : The predicted results obtained in this study provide a guide for practical design of a new tuberculosis vaccine. Keywords : Vaccine, Mycobacterium tuberculosis, Epitope prediction, Histocompatibility complexes

Origin and Functional Prediction of Pollen Allergens in Plants.

Pollen allergies have long been a major pandemic health problem for human. However, the evolutionary events and biological function of pollen allergens in plants remain largely unknown. Here, we report the genome-wide prediction of pollen allergens and their biological function in the dicotyledonous model plant Arabidopsis (Arabidopsis thaliana) and the monocotyledonous model plant rice (Oryza sativa). In total, 145 and 107 pollen allergens were predicted from rice and Arabidopsis, respectively. These pollen allergens are putatively involved in stress responses and metabolic processes such as cell wall metabolism during pollen development. Interestingly, these putative pollen allergen genes were derived from large gene families and became diversified during evolution. Sequence analysis across 25 plant species from green alga to angiosperms suggest that about 40% of putative pollen allergenic proteins existed in both lower and higher plants, while other allergens emerged during evolution. Although a high proportion of gene duplication has been observed among allergen-coding genes, our data show that these genes might have undergone purifying selection during evolution. We also observed that epitopes of an allergen might have a biological function, as revealed by comprehensive analysis of two known allergens, expansin and profilin. This implies a crucial role of conserved amino acid residues in both in planta biological function and allergenicity. Finally, a model explaining how pollen allergens were generated and maintained in plants is proposed. Prediction and systematic analysis of pollen allergens in model plants suggest that pollen allergens were evolved by gene duplication and then functional specification. This study provides insight into the phylogenetic and evolutionary scenario of pollen allergens that will be helpful to future characterization and epitope screening of pollen allergens.

Bioactive hydrolysates from bovine blood globulins: Generation, characterisation, and in silico prediction of toxicity and allergenicity

Two protein fractions rich in γ-globulins (FI) and α- and β-globulins (FII) were generated from bovine blood and hydrolysed with the enzyme papain. The generated hydrolysates showed in vitro angiotensin-I-converting enzyme (ACE-I), renin, and dipeptidyl peptidase-IV (DPP-IV) inhibitory activities. A total of 626 and 2246 peptides were identified by LC-MS/MS from the 1 kDa fractions of FI and FII, and the potential toxicity and allergenicity of these peptides were assessed in silico using three independent predictive approaches. All of the peptides identified from the bioactive blood protein fractions FI and FII were predicted to be non-toxic. However, 72 peptides from FI and 492 peptides from FII were identified as potential allergens with at least two predictive approaches. Results suggested that papain hydrolysates of FI and FII contain potential allergenic peptides, and those products containing such hydrolysates should be labelled correctly in line with Food and Consumer legislation – Regulation (EU) No.1169/2011.

Prediction and Classification of Allergenicity within Protein Families

Prediction and Classification of Allergenicity within Protein Families

In silico allergenicity prediction of several lipid transfer proteins

Non-specific lipid transfer proteins (nsLTPs) are common allergens and they are particularly widespread within the plant kingdom. They have a highly conserved three-dimensional structure that generate a strong cross-reactivity among the members of this family. In the last years several web tools for the prediction of allergenicity of new molecules based on their homology with known allergens have been released, and guidelines to assess potential allergenicity of proteins through bioinformatics have been established. Even if such tools are only partially reliable yet, they can provide important indications when other kinds of molecular characterization are lacking. The potential allergenicity of 28 amino acid sequences of LTPs homologs, either retrieved from the UniProt database or in silico deduced from the corresponding EST coding sequence, was predicted using 7 publicly available web tools. Moreover, their similarity degree to their closest known LTP allergens was calculated, in order to evaluate their potential cross-reactivity. Finally, all sequences were studied for their identity degree with the peach allergen Pru p 3, considering the regions involved in the formation of its known conformational IgE-binding epitope. Most of the analyzed sequences displayed a high probability to be allergenic according to all the software employed. The analyzed LTPs from bell pepper, cassava, mango, mungbean and soybean showed high homology (>70%) with some known allergenic LTPs, suggesting a potential risk of cross-reactivity for sensitized individuals. Other LTPs, like for example those from canola, cassava, mango, mungbean, papaya or persimmon, displayed a high degree of identity with Pru p 3 within the consensus sequence responsible for the formation, at three-dimensional level, of its major conformational epitope. Since recent studies highlighted how in patients mono-sensitized to peach LTP the levels of IgE seem directly proportional to the chance of developing cross-reactivity to LTPs from non-Rosaceae foods, and these chances increase the more similar the protein is to Pru p 3, these proteins should be taken into special account for future studies aimed at evaluating the risk of cross-allergenicity in highly sensitized individuals.

Common Amino Acid Subsequences in a Universal Proteome--Relevance for Food Science.

A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept.

Seed Pro-Nutra Care: A tool for characterization of seed storage proteins and database of bioactive peptides having potential health benefits.

Seed storage proteins, the major food proteins, possess unique physicochemical characteristics which determine their nutritional importance and influence their utilization by humans. Here, we describe a database driven tool named Seed Pro-Nutra Care which comprises a systematic compendium of seed storage proteins and their bioactive peptides influencing several vital organ systems for maintenance of health. Seed Pro-Nutra Careis an integrated resource on seed storage protein. This resource help in the (I) Characterization of proteins whether they belong to seed storage protein group or not. (II) Identification the bioactive peptides with their sequences using peptide name (III) Determination of physico chemical properties of seed storage proteins. (IV) Epitope identification and mapping (V) Allergenicity prediction and characterization. Seed Pro-Nutra Care is a compilation of data on bioactive peptides present in seed storage proteins from our own collections and other published and unpublished sources. The database provides an information resource of a variety of seed related biological information and its use for nutritional and biomedical application.Availabilityhttp://www.gbpuat-cbsh.ac.in/departments/bi/database/seed_pro_nutra_care/

Prediction of protein allergenicity based on signal-processing bioinformatics approach.

Current bioinformatics tools accomplish high accuracies in classifying allergenic protein sequences with high homology and generally perform poorly with low homology protein sequences. Although some homologous regions explained Immunoglobulin E (IgE) cross-reactivity in groups of allergens, no universal molecular structure could be associated with allergenicity. In addition, studies have showed that cross-reactivity is not directly linked to the homology between protein sequences. Therefore, a new homology independent method needs to be developed to determine if a protein is an allergen or not. The aim of this study is therefore to differentiate sets of allergenic and non-allergenic proteins using a signal-processing based bioinformatics approach. In this paper, a new method was proposed for characterisation and classification of allergenic protein sequences. For this method hydrophobicity amino acid index was used to encode proteins to numerical sequences and Discrete Fourier Transform to extract features for each protein. Finally, a classifier was constructed based on Support Vector Machines. In order to demonstrate the applicability of the proposed method 857 allergen and 1000 non-allergen proteins were collected from UniProt online database. The results obtained from the proposed method yielded: MCC: 0.752 ± 0.007, Specificity: 0.912 ± 0.005, Sensitivity: 0.835 ± 0.008 and Total Accuracy: 87.65% ± 0.004.

AllergenPro: an integrated database for allergenicity analysis and prediction.

The National Agricultural Biotechnology Information Center (NABIC) reconstructed an AllergenPro database for allergenic proteins analysis and allergenicity prediction. The AllergenPro is an integrated web-based system providing information about allergen in foods, microorganisms, animals and plants. The allergen database has the three main features namely, (1) allergen list with epitopes, (2) searching of allergen using keyword, and (3) methods for allergenicity prediction. This updated AllergenPro outputs the search based allergen information through a user-friendly web interface, and users can run tools for allergenicity prediction using three different methods namely, (1) FAO/WHO, (2) motif-based and (3) epitope-based methods. AvailabilityThe database is available for free at http://nabic.rda.go.kr/allergen/

AllerTOP v.2—a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance--typically proteins--resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP).

Allergenicity prediction by artificial neural networks

Two artificial neural network (ANN)‐based algorithms for allergenicity prediction were developed and tested. The first algorithm consists of three steps. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and β‐strand forming propensities. Second, the generated strings of different length are converted into vectors with equal length by auto‐covariance and cross‐covariance (ACC). At the third step, ANN is applied to discriminate between allergens and non‐allergens. The second algorithm consists of four steps. It has one additional step before the final ANN modeling. At this step, the ACC vectors are transformed into binary fingerprints. The algorithms were applied to a set of 2427 known allergens and 2427 non‐allergens and compared in terms of predictive ability. The three‐step algorithm performed better than the four‐step one identifying 82% versus 76% of the allergens and non‐allergens. The ANN algorithms presented here are universal. They could be applied for any classification problem in computational biology. The amino acid descriptors are able to capture the main structural and physicochemical properties of amino acids building the proteins. The ACC transformation overcomes the main problem in the alignment‐based comparative studies arising from the different length of the aligned protein sequences. The uniform‐length vectors allow similarity search and classification by different computational methods. Optionally, the ACC vectors could be converted into binary descriptor fingerprints. The comparative study on several Web tools for allergenicity prediction showed that the usage of more than one predictor is reasonable and recommendable because some of the tools recognize better the allergens, some of them—the non‐allergens, but none of them—both. Copyright © 2014 John Wiley & Sons, Ltd.

Allerdictor: fast allergen prediction using text classification techniques

Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years. Although these tools have achieved certain levels of specificity, when applied to large-scale allergen discovery (e.g. at a whole-genome scale), they still yield many false positives and thus low precision (even at low recall) due to the extreme skewness of the data (allergens are rare). Moreover, the most accurate tools are relatively slow because they use protein sequence alignment to build feature vectors for allergen classifiers. Additionally, only web server implementations of the current allergen prediction tools are publicly available and are without the capability of large batch submission. These weaknesses make large-scale allergen discovery ineffective and inefficient in the public domain. We developed Allerdictor, a fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took ∼6 min on a single core PC to scan a whole Swiss-Prot database of ∼540 000 sequences and identified <1% of them as allergens. Allerdictor is implemented in Python and available as standalone and web server versions at http://allerdictor.vbi.vt.edu CONTACT: lawrence@vbi.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

In silico prediction of allergenic proteins.

Currently, the prediction of new allergens is becoming important due to use of genetically modified (GM) foods and biopharmaceuticals. In this chapter, we describe how to use four popular allergenic prediction servers: (1) Structural Database of Allergenic Proteins (SDAP), (2) Allermatch, (3) Evaller 2, and (4) AlgPred. The first two prediction servers are based on traditional approaches, whereas Evaller 2 and AlgPred use sophisticated machine learning techniques.

PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

BackgroundAssessment of potential allergenicity of protein is necessary whenever transgenic proteins are introduced into the food chain. Bioinformatics approaches in allergen prediction have evolved appreciably in recent years to increase sophistication and performance. However, what are the critical features for protein's allergenicity have been not fully investigated yet.ResultsWe presented a more comprehensive model in 128 features space for allergenic proteins prediction by integrating various properties of proteins, such as biochemical and physicochemical properties, sequential features and subcellular locations. The overall accuracy in the cross-validation reached 93.42% to 100% with our new method. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) procedure were applied to obtain which features are essential for allergenicity. Results of the performance comparisons showed the superior of our method to the existing methods used widely. More importantly, it was observed that the features of subcellular locations and amino acid composition played major roles in determining the allergenicity of proteins, particularly extracellular/cell surface and vacuole of the subcellular locations for wheat and soybean. To facilitate the allergen prediction, we implemented our computational method in a web application, which can be available at http://gmobl.sjtu.edu.cn/PREAL/index.php.ConclusionsOur new approach could improve the accuracy of allergen prediction. And the findings may provide novel insights for the mechanism of allergies.

In silico analysis of chimeric recombinant immunogen against three diarrhea causing bacteria

Shigella and Escherichia belong to the Enterobacteriaceae family which are the cause for most of the diarrheal cases in the world. Shigella can cause bacterial dysenteries and shigellosis. One of the most effective proteins for pathogenesis is invasion plasmid antigen C (IpaC). Other bacteria like Enterotoxogenic (ETEC), Enterohemorrhagic (EHEC), and E.coli can also cause diarrhea and produce intestinal disorders. Colonization factor antigen I (CFA/I), a critical virulence protein for these infections, has two subunits i.e. CfaB and CfaE. EHEC Attachment of bacteria is the main step of infection with intimin playing the key role in this function. This study was designed to elicit protection against the majority of diarrheal pathogens via development of polyvalent vaccine against Shigella, ETEC and EHEC. In silico techniques are as best tools to design new vaccines. For this purpose the immunogenic epitopes of CfaB, IpaC and Intimin were identified through bioinformatic tools and were then selected as major antigens to construct a chimeric protein (CII). The humoral and cellular immunities were analyzed bioinformatically. Prediction of allergens and mapping of IgE epitopes were carried out. The bioinformatic analysis showed each domain was folded separately in fusion structure. CII had many T and B cell epitopes in both linear and three-dimensional structures. This prediction of the chimeric construct had the potential to induce CD4+ and CD8+ immune responses against these pathogens. In addition CII could be accessible to surveillance by the immune system in mouse and human. In conclusion, in silico analysis showed that this chimeric protein can be used as a vaccine against Shigella, ETEC and EHEC simultaneously.

AllergenFP: allergenicity prediction by descriptor fingerprints

Allergenicity, like antigenicity and immunogenicity, is a property encoded linearly and non-linearly, and therefore the alignment-based approaches are not able to identify this property unambiguously. A novel alignment-free descriptor-based fingerprint approach is presented here and applied to identify allergens and non-allergens. The approach was implemented into a four step algorithm. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and β-strand forming propensities. Then, the generated strings of different length are converted into vectors with equal length by auto- and cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of Tanimoto coefficient. The approach was applied to a set of 2427 known allergens and 2427 non-allergens and identified correctly 88% of them with Matthews correlation coefficient of 0.759. The descriptor fingerprint approach presented here is universal. It could be applied for any classification problem in computational biology. The set of E-descriptors is able to capture the main structural and physicochemical properties of amino acids building the proteins. The ACC transformation overcomes the main problem in the alignment-based comparative studies arising from the different length of the aligned protein sequences. The conversion of protein ACC values into binary descriptor fingerprints allows similarity search and classification. The algorithm described in the present study was implemented in a specially designed Web site, named AllergenFP (FP stands for FingerPrint). AllergenFP is written in Python, with GIU in HTML. It is freely accessible at http://ddg-pharmfac.net/Allergen FP. idoytchinova@pharmfac.net or ivanbangov@shu-bg.net.

In silico analyses of structural and allergenicity features of sapodilla (Manilkara zapota) acidic thaumatin-like protein in comparison with allergenic plant TLPs

Thaumatin-like proteins (TLPs) belong to the pathogenesis-related family (PR-5) of plant defense proteins. TLPs from only 32 plant genera have been identified as pollen or food allergens. IgE epitopes on allergens play a central role in food allergy by initiating cross-linking of specific IgE on basophils/mast cells. A comparative analysis of pollen- and food-allergenic TLPs is lacking. The main objective of this investigation was to study the structural and allergenicity features of sapodilla (Manilkara zapota) acidic TLP (TLP 1) by in silico methods. The allergenicity prediction of composite sequence of sapodilla TLP 1 (NCBI B3EWX8.1, G5DC91.1) was performed using FARRP, Allermatch and Evaller web tools. A homology model of the protein was generated using banana TLP template (1Z3Q) by HHPRED-MODELLER. B-cell linear epitope prediction was performed using BCpreds and BepiPred. Sapodilla TLP 1 matched significantly with allergenic TLPs from olive, kiwi, bell pepper and banana. IgE epitope prediction as performed using AlgPred indicated the presence of 2 epitopes (epitope 1: residues 36–48; epitope 2: residues 51–63), and a comprehensive analysis of all allergenic TLPs displayed up to 3 additional epitopes on other TLPs. It can be inferred from these analyses that plant allergenic TLPs generally carry 2–3 IgE epitopes. ClustalX alignments of allergenic TLPs indicate that IgE epitopes 1 and 2 are common in food allergenic TLPs, and IgE epitopes 2 and 3 are common in pollen allergenic TLPs; IgE epitope 2 overlaps with a portion of the thaumatin family signature. The secondary structural elements of TLPs vary markedly in regions 1 and 2 which harbor all the predicted IgE epitopes in all food and pollen TLPs in either of the region. Further, based on the number of IgE epitopes, food TLPs are grouped into rosid and non-rosid clades. The number and distribution of the predicted IgE epitopes among the allergenic TLPs may explain the specificity of food or pollen allergy as well as the varied degree of cross-reactivity among plant foods and/or pollens.

Mast cell-based electrochemical biosensor for quantification of the major shrimp allergen Pen a 1 (tropomyosin)

A novel cell-based electrochemical biosensor was developed to quantify major shrimp allergen Pen a 1 (tropomyosin) and to assess its immunoglobulin E (IgE)-mediated hypersensitivity. Rat basophilic leukemia (RBL-2H3) mast cells, encapsulated in type I collagen, were immobilized on a self-assembled l-cysteine/gold nanoparticle (AuNPsCys)-modified gold electrode to monitor IgE-mediated mast cell sensitization and activation. The exposure of dinitrophenol–bovine serum albumin (DNP-BSA), as a model antigen that stimulates mast cells, induced a robust and long-lasting electrochemical impedance signal in a dose-dependent manner which efficiently measured degranulation of anti-DNP IgE-stimulated mast cells. Then this mast cell-based biosensor was applied into quantification for the shrimp allergen with anti-shrimp tropomyosin IgE-sensitization. The electrochemical impedance spectroscopy (EIS) results showed that the impedance value (Ret) increased with the concentration of purified shrimp allergen Pen a 1 (tropomyosin) in range of 0.5–0.25μgmL−1 with the detection limit as 0.15μgmL−1, and the electrochemical result was confirmed by β-hexosaminidase assay and scanning electron microscopic morphological (SEM) analysis. Thus, a simple, label-free, and sensitive method for the determination of shrimp allergens was proposed and demonstrated here, implying a highly versatile biosensor for food allergen detection and prediction.

AllerTOP - a server for in silico prediction of allergens

BackgroundAllergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.ResultsA set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.ConclusionsAllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.

Evaluation and integration of existing methods for computational prediction of allergens

BackgroundAllergy involves a series of complex reactions and factors that contribute to the development of the disease and triggering of the symptoms, including rhinitis, asthma, atopic eczema, skin sensitivity, even acute and fatal anaphylactic shock. Prediction and evaluation of the potential allergenicity is of importance for safety evaluation of foods and other environment factors. Although several computational approaches for assessing the potential allergenicity of proteins have been developed, their performance and relative merits and shortcomings have not been compared systematically.ResultsTo evaluate and improve the existing methods for allergen prediction, we collected an up-to-date definitive dataset consisting of 989 known allergens and massive putative non-allergens. The three most widely used allergen computational prediction approaches including sequence-, motif- and SVM-based (Support Vector Machine) methods were systematically compared using the defined parameters and we found that SVM-based method outperformed the other two methods with higher accuracy and specificity. The sequence-based method with the criteria defined by FAO/WHO (FAO: Food and Agriculture Organization of the United Nations; WHO: World Health Organization) has higher sensitivity of over 98%, but having a low specificity. The advantage of motif-based method is the ability to visualize the key motif within the allergen. Notably, the performances of the sequence-based method defined by FAO/WHO and motif eliciting strategy could be improved by the optimization of parameters. To facilitate the allergen prediction, we integrated these three methods in a web-based application proAP, which provides the global search of the known allergens and a powerful tool for allergen predication. Flexible parameter setting and batch prediction were also implemented. The proAP can be accessed at http://gmobl.sjtu.edu.cn/proAP/main.html.ConclusionsThis study comprehensively evaluated sequence-, motif- and SVM-based computational prediction approaches for allergens and optimized their parameters to obtain better performance. These findings may provide helpful guidance for the researchers in allergen-prediction. Furthermore, we integrated these methods into a web application proAP, greatly facilitating users to do customizable allergen search and prediction.