WebAllergen: a web server for predicting allergenic proteins

WebAllergen is a web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs. Moreover, users are allowed to upload their own allergens as alternative training sequences on which a new set of allergenic motifs will be built. The query sequences can also be compared with these motifs. AVAILABILITY: http://weballergen.bii.a-star.edu.sg/     

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. Availability: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.     

Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning

The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via ulfh@slv.se.     

TranScout: prediction of gene expression regulatory proteins from their sequences

MOTIVATION: The advent of genomics yields thousands of reading frames in search of function. Identification of conserved functional motifs in protein sequences can be helpful for function prediction. RESULTS: A database and a classification of reported DNA-binding protein motifs has been designed. A program ('TranScout') has been developed for the detection and evaluation of conserved motifs in prokaryotic and eukaryotic sequences of proteins with a gene regulatory function. The efficiency of the program is shown in a benchmark against a database obtained from SWISS-PROT without the protein sequences used to train the program. All motifs were detected with a mean average sensitivity of 0.98 and a mean average specificity of 0.92. AVAILABILITY: The program is freely available for use on the internet at http://luz.uab.es/transcout/. The user can find additional information at this site.     

Characterization of allergenic epitopes of Ory s1 protein from Oryza sativa and its homologs

Vaccination is the most effective technique suggested now days for allergy treatment. Recombinant-based approaches are mostly focused on genetic modification of allergens to produce molecules with reduced allergenic activity and conserved antigenicity. The molecules developed for vaccination in allergy possess significantly reduced allergenicity in terms of IgE binding, and therefore will not lead to anaphylactic reactions upon injection. This approach is probably feasible with every peptide allergen with known amino acid sequence. In this study an in silico approach was used to investigate allergenic protein sequences. Motif analysis of these sequences reveals the allergenic epitopes in the amino acid sequences. Physicochemical analysis of protein sequences shows that the homolog allergens of Ory s1 are highly correlated with the aromaticity, GRAVY and cysteine content. Moreover, phylogenetic analysis of Ory s1 with other sequences reveals that Oryza sativa japonica and Zea mays are close homologs, whilst Lolium perenne and Dactylis glomerata are found to be remote homologs. The multiple sequence alignment reveals of Ory s1 with all its homologs in this study reveals the high conservation of residues in DPBB_1 domain (amino acid residue positions 86- 164) and was found distinctly in all the sequences. These findings support the proposal that allergenic epitopes encompass conserved residues. The consensus allergenic was found to be mainly composed of hydrophobic residues. The functional sites of allergenic proteins reported in this study shall be attenuated to develop hypoallergenic vaccine. The sequence comparison strategy adopted in this study would pave way effective evolutionary analysis of these allergens.     

Prediction of Bacterial and Archaeal Allergenicity with AllPred Program

Nowadays, allergic disorders have become one of the most important social problems in the world. This can be related to the advent of new allergenic agents in the environment, as well as an increasing density of human contact with known allergens, including various proteins. Thus, the development of computer programs designed for the prediction of allergenic properties of proteins becomes one of the urgent tasks of modern bioinformatics. Previously we developed a web accessible Allpred Program (http://www-bionet.sscc.ru/ psd/cgi-bin/programs/Allpred/allpred.cgi) that allows users to assess the allergenicity of proteins by taking into account the characteristics of their spatial structure. In this paper, using AllPred, we predicted the allergenicity of proteins from 462 archaea and bacteria species for which a complete genome was available. The segregation of considered proteins on archaea and bacteria has shown that allergens are predicted more often among archaea than among bacteria. The division of these proteins into groups according to their intracellular localization has revealed that the majority of allergenic proteins were among the secreted proteins. The application of methods for predicting the level of gene expression of microorganisms based on DNA sequence analysis showed a statistically significant relationship between the expression level of the proteins and their allergenicity. This analysis has revealed that potentially allergenic proteins were more common among highly expressed proteins. Sorting microorganisms into the pathogenic and nonpathogenic groups has shown that pathogens can potentially be more allergenic because of a statistically significant greater number of allergens predicted among their proteins.     

Detecting cryptically simple protein sequences using the SIMPLE algorithm

MOTIVATION: Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. RESULTS: We have developed a new tool, based on the SIMPLE algorithm, that facilitates the quantification of the amount of simple sequence in proteins and determines the type of short motifs that show clustering above a certain threshold. By modifying the sensitivity of the program simple sequence content can be studied at various levels, from highly organised tandem structures to complex combinations of repeats. We compare the relative amount of simplicity in different functional groups of yeast proteins and determine the level of clustering of the different amino acids in these proteins. AVAILABILITY: The program is available on request or online at http://www.biochem.ucl.ac.uk/bsm/SIMPLE.     

Identifying property based sequence motifs in protein families and superfamilies: application to DNase-1 related endonucleases

MOTIVATION: Identification of short conserved sequence motifs common to a protein family or superfamily can be more useful than overall sequence similarity in suggesting the function of novel gene products. Locating motifs still requires expert knowledge, as automated methods using stringent criteria may not differentiate subtle similarities from statistical noise. RESULTS: We have developed a novel automatic method, based on patterns of conservation of 237 physical-chemical properties of amino acids in aligned protein sequences, to find related motifs in proteins with little or no overall sequence similarity. As an application, our web-server MASIA identified 12 property-based motifs in the apurinic/apyrimidinic endonuclease (APE) family of DNA-repair enzymes of the DNase-I superfamily. Searching with these motifs located distantly related representatives of the DNase-I superfamily, such as Inositol 5'-polyphosphate phosphatases in the ASTRAL40 database, using a Bayesian scoring function. Other proteins containing APE motifs had no overall sequence or structural similarity. However, all were phosphatases and/or had a metal ion binding active site. Thus our automated method can identify discrete elements in distantly related proteins that define local structure and aspects of function. We anticipate that our method will complement existing ones to functionally annotate novel protein sequences from genomic projects. AVAILABILITY: MASIA WEB site: http://www.scsb.utmb.edu/masia/masia.html SUPPLEMENTARY INFORMATION: The dendrogram of 42 APE sequences used to derive motifs is available on http://www.scsb.utmb.edu/comp_biol.html/DNA_repair/publication.html     

Automatic identification of large collections of protein-coding or rRNA sequences

The number of available genomic sequences is growing very fast, due to the development of massive sequencing techniques. Sequence identification is needed and contributes to the assessment of gene and species evolutionary relationships. Automated bioinformatics tools are thus necessary to carry out these identification operations in an accurate and fast way. We developed HoSeqI (Homologous Sequence Identification), a software environment allowing this kind of automated sequence identification using homologous gene family databases. HoSeqI is accessible through a Web interface (http://pbil.univ-lyon1.fr/software/HoSeqI/) allowing to identify one or several sequences and to visualize resulting alignments and phylogenetic trees. We also implemented another application, MultiHoSeqI, to quickly add a large set of sequences to a family database in order to identify them, to update the database, or to help automatic genome annotation. Lately, we developed an application, ChiSeqI (Chimeric Sequence Identification), to automate the processes of identification of bacterial 16S ribosomal RNA sequences and of detection of chimeric sequences.     

Immunological identification and characterization of individual food allergens

Food allergies of type-I-allergy are immunoglobulin E (IgE) mediated and caused by certain proteins or glycoproteins, which are called food allergens. An analytical marker of allergens is the IgE-reactivity to these substances. Normally food allergens are minor components in allergenic source material, which consist of a huge number of chemical different substances. Thus allergen extraction, separation and immunological detection methods are described which identify and characterize individual food allergens by a minimum of manipulation. Favoured separation methods of allergenic extracts are electrophoretic ones allowing the combination of highly resolved protein separations with immunological detection methods subsumed by the term immunoblotting. These techniques are a useful basis to characterize allergens by chemical methods. Once the primary protein structure of a food allergen is established, the way is cleared for the identification of epitopes. Epitopes are immunological detectable parts of a protein or glycoprotein generating the interface between chemical structure and immune-system. The nature of epitopes may differ, for instance, can be conformational, continuous, or built up by glycoconjugates, which determine the stability of food allergens, especially in the case of food processing. Progress in identification and characterization of food allergens will improve diagnostics and therapy of food allergy.     

Two-dimensional electrophoresis replica blotting: a valuable technique for the immunological and biochemical characterization of single components of complex extracts

The combination of the high resolution electrophoresis (2-DE) with subsequent transfer onto a protein-binding membrane (blotting), immunological detection, and/or N-terminal sequencing is a powerful tool to identify and characterize single components of complex protein mixtures. Direct comparison of protein staining, immunological detection, and biochemical characterization of single protein spots was achieved by the replica blotting technique. The proteins were transferred from one two-dimensional gel onto several blotting membranes one after another. A canon of methods has been employed to identify and characterize allergens from different allergen sources. We have studied single major allergens as well as related major allergens from different grass species ("allergen groups") using patients' sera and allergen-specific monoclonal antibodies. The biochemical structure of the allergenic components has been analyzed by N-terminal and internal protein sequencing, precise mass determinations by matrix-assisted laser desorption/ionization mass spectrometry and investigations on post-translational modifications such as glycosylation. Here, we give a general survey of methods, and we describe an array of techniques suitable for characterization and identification of components of complex extracts, even if there is little or no previous information available.     

Protein multiple alignment incorporating primary and secondary structure information.

Identifying common local segments, also called motifs, in multiple protein sequences plays an important role for establishing homology between proteins. Homology is easy to establish when sequences are similar (sharing an identity > 25%). However, for distant proteins, it is much more difficult to align motifs that are not similar in sequences but still share common structures or functions. This paper is a first attempt to align multiple protein sequences using both primary and secondary structure information. A new sequence model is proposed so that the model assigns high probabilities not only to motifs that contain conserved amino acids but also to motifs that present common secondary structures. The proposed method is tested in a structural alignment database BAliBASE. We show that information brought by the predicted secondary structures greatly improves motif identification. A website of this program is available at www.stat.purdue.edu/~junxie/2ndmodel/sov.html.     

SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.     

Automatic annotation of protein motif function with Gene Ontology terms

Background  

Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base.     

Predicting allergenic proteins using wavelet transform

MOTIVATION: With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described. RESULTS: The new prediction system uses a clustering algorithm that groups the known allergenic proteins into clusters. Proteins within each cluster are assumed to carry one or more common motifs. After a multiple sequence alignment, proteins in each cluster go through a wavelet analysis program whereby conserved motifs will be identified. A hidden Markov model (HMM) profile will then be prepared for each identified motif. The allergens that do not appear to carry detectable allergen motifs will be saved in a small database. The allergenicity of an unknown protein may be predicted by comparing it against the HMM profiles, and, if no matching profiles are found, against the small allergen database by BLASTP. Over 70% of recall and over 90% of precision were observed using cross-validation experiments. Using the entire Swiss-Prot as the query, we predicted about 2000 potential allergens. AVAILABILITY: The software is available upon request from the authors.     

Prediction of food protein allergenicity: a bioinformatic learning systems approach

Food hypersensitivity is constantly increasing in Western societies with a prevalence of about 1-2% in Europe and in the USA. Among children, the incidence is even higher. Because of the introduction of foods derived from genetically modified crops on the marketplace, the scientific community, regulatory bodies and international associations have intensified discussions on risk assessment procedures to identify potential food allergenicity of the newly introduced proteins. In this work, we present a novel biocomputational methodology for the classification of amino acid sequences with regard to food allergenicity and non-allergenicity. This method relies on a computerised learning system trained using selected excerpts of amino acid sequences. One example of such a successful learning system is presented which consists of feature extraction from sequence alignments performed with the FASTA3 algorithm (employing the BLOSUM50 substitution matrix) combined with the k-Nearest-Neighbour (kNN) classification algorithm. Briefly, the two features extracted are the alignment score and the alignment length and the kNN algorithm assigns the pair of extracted features from an unknown sequence to the prevalent class among its k nearest neighbours in the training (prototype) set available. 91 food allergens from several specialised public repositories of food allergy and the SWALL database were identified, pre-processed, and stored, yielding one of the most extensively characterised repositories of allergenic sequences known today. All allergenic sequences were classified using a standard one-leave-out cross validation procedure yielding about 81% correctly classified allergens and the classification of 367 non-allergens in an independent test set resulted in about 98% correct classifications. The biocomputational approach presented should be regarded as a significant extension and refinement of earlier attempts suggested for in silico food safety assessment. Our results show that the framework described here is powerful enough to become useful as part of a multiple-procedure test scheme that also depicts other evaluation approaches such as solid phase immunoassay and tests for stability to digestions.     

双孢蘑菇菇脚氨基酸含量的测定及营养评价

测定了双孢蘑菇菇脚中蛋白质和氨基酸的含量,应用模糊识别法和氨基酸比值系数法,以鸡蛋蛋白为标准蛋白,以WHO/FAO的必需氨基酸参考模式为评价标准,对双孢蘑菇菇脚的蛋白质营养价值进行了全面评价,并与双孢蘑菇子实体的蛋白质进行比较.结果表明:双孢蘑菇菇脚蛋白质中总氨基酸含量为73.16％,氨基酸种类齐全,必需氨基酸含量占氨...