Daniel Soeria-Atmadja

Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins

MOTIVATION Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens. RESULTS Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO. AVAILABILITY The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh@slv.se

Statistical evaluation of local alignment features predicting allergenicity using supervised classification algorithms.

Background: Recently, two promising alignment-based features predicting food allergenicity using the k nearest neighbor (kNN) classifier were reported. These features are the alignment score and alignment length of the best local alignment obtained in a database of known allergen sequences. Methods: In the work reported here a much more comprehensive statistical evaluation of the potential of these features was performed, this time for the prediction of allergenicity in general. The evaluation consisted of the following four key components. (1) A new high quality database consisting of 318 carefully selected, non-redundant allergens and 1,007 sequences carefully selected to be non-allergens. (2) Three different supervised algorithms: the kNN classifier, the Bayesian linear Gaussian classifier, and the Bayesian quadratic Gaussian classifier. (3) A large set of local alignment procedures defined using the FASTA3 alignment program by means of a wide range of different parameter settings. (4) Novel performance curves, alternative to conventional receiver-operating characteristic curves, to display not only average behaviors but also statistical variations due to small data sets. Results: The linear Gaussian classifier proved most useful among the tested supervised machine learning algorithms, closely followed by the quadratic Gaussian equivalent and kNN. The overall best classification results were obtained with a novel feature vector consisting of the combined alignment scores derived from local alignment procedures using different substitution matrices. Conclusions: The models reported here should be useful as a part of an integrated assessment scheme for potential protein allergenicity and for future comparisons with alternative bioinformatic approaches.

IgE sensitization to fungi mirrors fungal phylogenetic systematics

BACKGROUND Fungal allergy is an elusive disease, and little progress has been made in this field during recent years. Moreover, because of the complexity of the organisms, it is difficult to categorize fungi systematically on the basis of morphologic characterization. However, recent molecular phylogenetics studies have substantially improved fungal categorization. In parallel, new approaches to analyze large IgE antibody datasets enable identification and visualization of IgE sensitization patterns. OBJECTIVE To study whether molecular phylogenetic relationships of fungal species, commonly used in allergy diagnosis, also are reflected in IgE sensitization profiles of individuals sensitized to fungi. METHODS A dataset was compiled of recorded serum IgE antibody levels to 17 different fungal species from 668 individuals sensitized to at least 1 of the 17 species. By applying a clustering method to this dataset, the fungal species were grouped into a hierarchical organization. Finally, the resulting organization was compared with recently published fungal systematics. RESULTS The hierarchical structure of fungi, based on the presence of IgE antibodies in sensitized individuals, very well reflected phylogenetic relationships. Examples include the distinct separation of basal fungi from the subkingdom Dikarya as well as individual cluster formations of fungi belonging to the subphylum Saccharomycotina and order Pleosporales. CONCLUSION To our knowledge, this is the first in-depth study that demonstrates a close relationship between molecular fungal systematics and IgE sensitization to fungal species. Because close evolutionary organisms typically have a higher degree of protein similarity, IgE cross-reactivity is likely the main reason for obtained organization.

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.

Genetically modified plants for non-food or non-feed purposes: straightforward screening for their appearance in food and feed.

Genetically modified (GM) plants aimed at producing food/feed are part of regular agriculture in many areas of the World. Commodity plants have also found application as bioreactors, designated non-food/non-feed GM (NFGM) plants, thereby making raw material for further refinement to industrial, diagnostic or pharmaceutical preparations. Many among them may pose health challenge to consumers or livestock animals, if occurring in food/feed. NFGM plants are typically released into the environment, but are grown under special oversight and any among several containment practices, none of which provide full protection against accidental dispersal. Adventitious admixture with food or feed can occur either through distributional mismanagement or as a consequence of gene flow to plant relatives. To facilitate NFGM surveillance we propose a new mandatory tagging of essentially all such plants, prior to cultivation or marketing in the European Union. The suggested tag--Plant-Made Industrial or Pharmaceutical Products Tag (PMIP-T)--is envisaged to occur as a transgenic silent DNA identifier in host plants and designed to enable technically simple identification and characterisation of any NFGM. Implementation of PMIP-T would permit inexpensive, reliable and high-throughput screening for NFGM specifically. The paper outlines key NFGM prospects and challenges as well as the PMIP-T concept.

External cross‐validation for unbiased evaluation of protein family detectors: Application to allergens

Key issues in protein science and computational biology are design and evaluation of algorithms aimed at detection of proteins that belong to a specific family, as defined by structural, evolutionary, or functional criteria. In this context, several validation techniques are often used to compare different parameter settings of the detector, and to subsequently select the setting that yields the smallest error rate estimate. A frequently overlooked problem associated with this approach is that this smallest error rate estimate may have a large optimistic bias. Based on computer simulations, we show that a detectors error rate estimate can be overly optimistic and propose a method to obtain unbiased performance estimates of a detector design procedure. The method is founded on an external 10‐fold cross‐validation (CV) loop that embeds an internal validation procedure used for parameter selection in detector design. The designed detector generated in each of the 10 iterations are evaluated on held‐out examples exclusively available in the external CV iterations. Notably, the average of these 10 performance estimates is not associated with a final detector, but rather with the average performance of the design procedure used. We apply the external CV loop to the particular problem of detecting potentially allergenic proteins, using a previously reported design procedure. Unbiased performance estimates of the allergen detector design procedure are presented together with information about which algorithms and parameter settings that are most frequently selected. Proteins 2005.

Assessing relative bioactivity of chemical substances using quantitative molecular network topology analysis.

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17β-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.

Interrogating health-related public databases from a food toxicology perspective : Computational analysis of scoring data

Over the last 15 years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database - HSDB - was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category.

Abundance and functional roles of intrinsic disorder in allergenic proteins and allergen representative peptides

The pathological process of allergies generally involves an initial activation of certain immune cells, tied to an ensuing inflammatory reaction on renewed contact with the allergen. In IgE‐mediated hypersensitivity, this typically occurs in response to otherwise harmless food‐ or air‐borne proteins. As some members of certain protein families carry special properties that make them allergenic, exploring protein allergens at the molecular level is instrumental to an improved understanding of the disease mechanisms, including the identification of relevant antigen features. For this purpose, we inspected a previously identified set of allergen representative peptides (ARPs) to scrutinize protein intrinsic disorder. The resulting study presented here focused on the association between these ARPs and protein intrinsic disorder. In addition, the connection between the disorder‐enriched ARPs and UniProt functional keywords was considered. Our analysis revealed that ∼ 20% of the allergen peptides are highly disordered, and that ∼ 77% of ARPs are either located within disordered regions of corresponding allergenic proteins or show more disorder/flexibility than their neighbor regions. Furthermore, among the subset of allergenic proteins, ∼ 70% of the predicted molecular recognition features (MoRFs that consist of short interactive disordered regions undergoing disorder‐to‐order transitions at interaction with binding partners) were identified as ARPs. These results suggest that intrinsic disorder and MoRFs may play functional roles in IgE‐mediated allergy. Proteins 2011;

Discovery and characterisation of dietary patterns in two Nordic countries

This Nordic study encompasses multivariate data analysis (MDA) of preschool Danish as well as pre- and elementary school Swedish consumers. Contrary to other counterparts the study incorporates two ...