Ulf Hammerling

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.

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.

Mitogenically Uncoupled Insulin and IGF-I Receptors of Differentiated Human Neuroblastoma Cells Are Functional and Mediate Ligand-Induced Signals

The SH-SY5Y human neuroblastoma cell line is differentiated in vitro with nanomolar concentrations of 12-O-tetradecanoyl-phorbol-13-acetate (TPA). Untreated cells express insulin receptors, and both type I and type II insulin-like growth factor (IGF) receptors, as has been shown by agonist binding and immunoprecipitation studies. Via interaction with its own receptor and the IGF-I receptor, insulin induced a mitogenic response in these cells. IGF-I and IGF-II are also mitogens for SH-SY5Y cells, as shown by a transient increase of the c-fos mRNA level, ornithin decarboxylase activity, thymidine incorporation, and, finally, cell division. TPA-differentiated cells do not respond mitogenically to any of these factors, although insulin and IGF-I receptors are still present on the cell surface and remain functional, as demonstrated by ligand-stimulated autophosphorylation, actin reorganization, and c-fos induction. However, other prereplicative responses, i.e., increased ornithin decarboxylase activity and c-myc mRNA levels, cannot be induced. These phenomena, may be part of a receptor uncoupling mechanism(s). The findings are discussed in terms of differentiation stage-dependent signaling of growth factor receptors. We suggest that these receptors switch from controlling cell division in replicative neuronal cells to mediating externally controlled functions related to the differentiated neuronal phenotype.

A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses

For decades, the standard procedure when screening for candidate anticancer drug combinations has been to search for synergy, defined as any positive deviation from trivial cases like when the drugs are regarded as diluted versions of each other (Loewe additivity), independent actions (Bliss independence), or no interaction terms in a response surface model (no interaction). Here, we show that this kind of conventional synergy analysis may be completely misleading when the goal is to detect if there is a promising in vitro therapeutic window. Motivated by this result, and the fact that a drug combination offering a promising therapeutic window seldom is interesting if one of its constituent drugs can provide the same window alone, the largely overlooked concept of therapeutic synergy (TS) is reintroduced. In vitro TS is said to occur when the largest therapeutic window obtained by the best drug combination cannot be achieved by any single drug within the concentration range studied. Using this definition of TS, we introduce a procedure that enables its use in modern massively parallel experiments supported by a statistical omnibus test for TS designed to avoid the multiple testing problem. Finally, we suggest how one may perform TS analysis, via computational predictions of the reference cell responses, when only the target cell responses are available. In conclusion, the conventional error-prone search for promising drug combinations may be improved by replacing conventional (toxicology-rooted) synergy analysis with an analysis focused on (clinically motivated) TS. Mol Cancer Ther; 13(7); 1964–76. ©2014 AACR.

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.

Consumption of Red/processed Meat and Colorectal Carcinoma: Possible Mechanisms underlying the Significant Association

Epidemiology and experimental studies provide an overwhelming support of the notion that diets high in red or processed meat accompany an elevated risk of developing pre-neoplastic colorectal adenoma and frank colorectal carcinoma (CRC). The underlying mechanisms are disputed; thus several hypotheses have been proposed. A large body of reports converges, however, on haem and nitrosyl haem as major contributors to the CRC development, presumably acting through various mechanisms. Apart from a potentially higher intestinal mutagenic load among consumers on a diet rich in red/processed meat, other mechanisms involving subtle interference with colorectal stem/progenitor cell survival or maturation are likewise at play. From an overarching perspective, suggested candidate mechanisms for red/processed meat-induced CRC appear as three partly overlapping tenets: (i) increased N-nitrosation/oxidative load leading to DNA adducts and lipid peroxidation in the intestinal epithelium, (ii) proliferative stimulation of the epithelium through haem or food-derived metabolites that either act directly or subsequent to conversion, and (iii) higher inflammatory response, which may trigger a wide cascade of pro-malignant processes. In this review, we summarize and discuss major findings of the area in the context of potentially pertinent mechanisms underlying the above-mentioned association between consumption of red/processed meat and increased risk of developing CRC.

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.

Automated QuantMap for rapid quantitative molecular network topology analysis

Summary: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment. Availability: http://galaxy.predpharmtox.org Contact: mats.gustafsson@medsci.uu.se or ola.spjuth@farmbio.uu.se Supplementary information: Supplementary data are available at Bioinformatics online.