A Method to Determine an E-score Threshold to Identify Potential Cross Reactive Allergens

Similar documents
Bioinformatic analyses: methodology for allergen similarity search. Zoltán Divéki, Ana Gomes EFSA GMO Unit

Protein allergenicity potential: What makes a protein an allergen?

Protein Safety Assessments Toxicity and Allergenicity

Search settings MaxQuant

A

Expanded View Figures

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

DECISION DOCUMENT. Food and Feed Safety Assessment of Soybean Event MON x MON (OECD: MON-877Ø1-2 x MON )

Advice on preliminary reference doses for allergens in foods

Multi-Stage Stratified Sampling for the Design of Large Scale Biometric Systems

Mustard Allergy Review and Discussion of Mustard Data. Dr. Sébastien La Vieille Bureau of Chemical Safety Food Directorate

Influenza Virus HA Subtype Numbering Conversion Tool and the Identification of Candidate Cross-Reactive Immune Epitopes

Statistical analysis of RIM data (retroviral insertional mutagenesis) Bioinformatics and Statistics The Netherlands Cancer Institute Amsterdam

Allergen Sequence Databases

DECISION DOCUMENTAL. Food and feed safety assessment of maize event MIR604 OECD: SYN-IR6Ø4-5. Directorate of Agrifood Quality

Network Analysis of Toxic Chemicals and Symptoms: Implications for Designing First-Responder Systems

Overview Detection epileptic seizures

For all of the following, you will have to use this website to determine the answers:

Allergenicity Prediction using Sequence Profiles

Sebastian Jaenicke. trnascan-se. Improved detection of trna genes in genomic sequences

Using the NC Controlled Substances Reporting System to Identify Providers Manifesting Unusual Prescribing Practices

Precautionary allergen labelling Are we ready to quantify risk? Dr. Chun-Han Chan Anaphylaxis Campaign Corporate Conference 2016

Hands-On Ten The BRCA1 Gene and Protein

In search for hypoallergenic trees: Screening for genetic diversity in birch pollen allergens, a multigene family of Bet v 1 (PR 10) proteins MJM

Meta Analysis. David R Urbach MD MSc Outcomes Research Course December 4, 2014

Online Supplementary Appendix

An analysis of the use of animals in predicting human toxicology and drug safety: a review

Opioid Addiction and the Criminal Justice System. Original Team Name: Yugal Subedi, Gabri Silverman, Connor Kennedy, Arianna Kazemi

OECD QSAR Toolbox v.4.2. An example illustrating RAAF scenario 6 and related assessment elements

Psychology Research Process

National Child Measurement Programme Changes in children s body mass index between 2006/07 and 2010/11

Probability-Based Protein Identification for Post-Translational Modifications and Amino Acid Variants Using Peptide Mass Fingerprint Data

Hypersensitivity Reactions and Peanut Component Testing 4/17/ Mayo Foundation for Medical Education and Research. All rights reserved.

DECISION DOCUMENT. Directorate of Agrifood Quality. Office of Biotechnology and Industrialized Agrifood Products

Fat Facts. Permission to Copy - This document may be reproduced for non-commercial educational purposes Copyright 2009 General Electric Company

POL 242Y Final Test (Take Home) Name

SMPD 287 Spring 2015 Bioinformatics in Medical Product Development. Final Examination

Incorporating Clinical Information into the Label

Title: Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs

Statistical Considerations: Study Designs and Challenges in the Development and Validation of Cancer Biomarkers

Uptake and outcome of manuscripts in Nature journals by review model and author characteristics

Type of intervention Treatment. Economic study type Cost-effectiveness analysis.

Statistical Audit. Summary. Conceptual and. framework. MICHAELA SAISANA and ANDREA SALTELLI European Commission Joint Research Centre (Ispra, Italy)

Thinking Critically with Psychological Science

EXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE

Food Allergy Advances in Diagnosis

Prediction of Post-translational modification sites and Multi-domain protein structure

4 Diagnostic Tests and Measures of Agreement

Potential use of High iron and low phytate GM rice and their Bio-safety Assessment

HOST-PATHOGEN CO-EVOLUTION THROUGH HIV-1 WHOLE GENOME ANALYSIS

Playing By The Rules:

The Impact of Relative Standards on the Propensity to Disclose. Alessandro Acquisti, Leslie K. John, George Loewenstein WEB APPENDIX

Supplementary information for: Human micrornas co-silence in well-separated groups and have different essentialities

Supporting Information

AutoOrthoGen. Multiple Genome Alignment and Comparison

SELF-RATED MENTAL HEALTH

PHARMACOPHORE MODELING

Pre-Calculus Multiple Choice Questions - Chapter S4

ADVANCES ON RISK ASSESSMENT FOR ALLERGENS IN FOOD: AN OVERVIEW AND SUPPORTING TOOLS

Surveillance report Published: 6 April 2016 nice.org.uk. NICE All rights reserved.

The RoB 2.0 tool (individually randomized, cross-over trials)

A response by Servier to the Statement of Reasons provided by NICE

Inferring Biological Meaning from Cap Analysis Gene Expression Data

THAI AGRICULTURAL STANDARD TAS National Bureau of Agricultural Commodity and Food Standards Ministry of Agriculture and Cooperatives

Mutation Detection and CNV Analysis for Illumina Sequencing data from HaloPlex Target Enrichment Panels using NextGENe Software for Clinical Research

Discussion Meeting for MCP-Mod Qualification Opinion Request. Novartis 10 July 2013 EMA, London, UK

Overview. Survey Methods & Design in Psychology. Readings. Significance Testing. Significance Testing. The Logic of Significance Testing

Decision Document. Assessment of event LY038 X MON810 (High Lysine Corn and Lepidoptera insects resistant) for human and animal consumption

National Lung Cancer Audit outlier policy 2017

IEEE Proof Web Version

Mangrove: Genetic risk prediction on trees

Statistical analysis DIANA SAPLACAN 2017 * SLIDES ADAPTED BASED ON LECTURE NOTES BY ALMA LEORA CULEN

starbase Pan-Cancer Tutorial

On the purpose of testing:

Clinical Utility of Diagnostic Tests

DNA Sequence Bioinformatics Analysis with the Galaxy Platform

NOVEL BIOMARKERS PERSPECTIVES ON DISCOVERY AND DEVELOPMENT. Winton Gibbons Consulting

OUTLIER SUBJECTS PROTOCOL (art_groupoutlier)

Unsupervised Identification of Isotope-Labeled Peptides

4. Model evaluation & selection

The Loss of Heterozygosity (LOH) Algorithm in Genotyping Console 2.0

The clinical Link. Distortion Product Otoacoustic Emission

MEA DISCUSSION PAPERS

Missing data in clinical trials: making the best of what we haven t got.

Supplementary materials for: Executive control processes underlying multi- item working memory

Marilyn M. Schapira, MD, MPH University of Pennsylvania

User Guide. Protein Clpper. Statistical scoring of protease cleavage sites. 1. Introduction Protein Clpper Analysis Procedure...

Protein Structure & Function. University, Indianapolis, USA 3 Department of Molecular Medicine, University of South Florida, Tampa, USA

A micropower support vector machine based seizure detection architecture for embedded medical devices

Cochrane Pregnancy and Childbirth Group Methodological Guidelines

The Immune Epitope Database Analysis Resource: MHC class I peptide binding predictions. Edita Karosiene, Ph.D.

Drug repurposing and therapeutic anti-mirna predictions in oxldl-induced the proliferation of vascular smooth muscle cell associated diseases

Molecular Allergy Diagnostics Recombinant or native Allergens in Type I Allergy Diagnostics

Discovery of Novel Human Gene Regulatory Modules from Gene Co-expression and

Long-term Outcomes of the Western Australian Trial of Screening for Abdominal Aortic Aneurysms Secondary Analysis of a Randomized Clinical Trial

Insects as a Potential Food Allergens. Phil Johnson

Cochrane Breast Cancer Group

Psychology Research Process

Food Allergen Thresholds & Probabilistic Risk Assessment: Current State of the Science. Joe Baumert, Ph.D.

INTRODUCTION TO MACHINE LEARNING. Decision tree learning

Transcription:

A Method to Determine an E-score Threshold to Identify Potential Cross Reactive Allergens Andre Silvanovich Ph. D. Gary Bannon Ph. D. Scott McClain Ph. D. Monsanto Company Regulatory Product Characterization Center St. Louis, Missouri, USA 63167

The Sliding Window FASTA Search A means to identify potential cross reacting allergens FAO/WHO recommend a 35% or greater identity in 8 amino acids or greater threshold for a sliding window search By design, the FASTA algorithm reports and assesses the significance of alignments on the basis of shared global similarity The E-score is a composite measure of similarity between proteins that are aligned by FASTA is Pearson, the developer of FASTA states: The E() is the first number you should look at when deciding whether to analyze further a high-ranking sequence

Objectives To propose a method to determine an E- score threshold Model allergens and non-allergens Identify a statistically relevant threshold Apply a test case for a family of known allergens

E-score is an assessment of structural similarity What is an E-score? The probability that an alignment was the product of chance An alignment yielding an E-score of 1 is comparable to searching the database with a random sequence, i.e, no significance attached to this value An E-score of.1 indicates that there is a 1 in 1 probability that the alignment is the product of chance The E-score reflects not only amino acid identity, it includes amino acid similarity, the length of the alignment and the size of the database being searched

Unshuffled Query E- score Shuffled sequence E- scores Random sequences and E-scores Likely nonallergen Potential Allergen Allergen Allergen 1.E+ 3.4E-7 4.1E-75 3.4E-154 > 1 3 2 8 >= 1. <1. 522 489 525 543 >=.1 <1. 47 423 411 384 >=.1 <.1 63 8 59 61 >=.1 <.1 4 5 3 9 >=.1 <.1 1 3 1 >=1E-5 <.1 >=1E-6 <1E-5 >=1E-7 <1E-6 Number of unique database sequences yielding a best match Number of 35% over 8 aa matches with shuffled queries 43 421 453 425 1 3 1 6

Establishing an E-Score Threshold What is the relationship between chance and an alignment that may reflect potential cross reactivity? A query dataset of 7695 protein sequences derived from corn was evaluated The query dataset contains known allergens However, the overwhelming majority of the query dataset should not be allergens Corn has a low incidence of human allergy FASTA analysis was performed using each of the 7695 query sequences and the E-score distribution for the top alignment for each query was plotted Sliding window searches were performed with the 7695 query sequences The FARRP allergen database and FASTA version 3.4t26 were used to conduct the analyses

1E+1 or greater 1.E-1 1.E-3 1.E-5 1.E-7 1.E-9 1.E-11 1.E-13 1.E-15 1.E-17 1.E-19 1.E-21 1.E-23 1.E-25 1.E-27 1.E-29 1E-31 or lower 35 3 25 2 15 1 5 The E-score Distribution for 7695 Query Sequences 35 3 25 2 15 1 5 E-Score Number of hits 1.E-3 1.E-5 1.E-7 1.E-9 1.E-11 1.E-13 1.E-15 1.E-17 1.E-19 1.E-21 1.E-23 1.E-25 1.E-27 1.E-29 1E-31 or lower E-Score

Threshold Modeling Estimates E-score threshold Rank out of 7695 alignments False negative rate Potential false positive rate.99 4683 % 99.29 %.99 1786 % 98.15 %.99 1176 % 97.19 %.99 876 % 96.23 % 9.9E-5 818 % 95.97 % 9.9E-6 757 % 95.64 % 9.9E-7 696 % 95.26 % 4.2E-7 66 % 95. % 5.E-11 519 % 93.64 % 2.4E-31 295 % 88.81 % 1.5E-55 165 58 % 8. % 4683-33 4683 X 1% 33 of the 7695 queries displayed 1% identity to known allergens

Threshold Modeling Estimates Derived from the 35% over 8 aa Search Results E-score threshold Rank out of 7695 alignments False negative rate Potential false positive rate.43 173 % 96.92 % 3.9E-7 654 % 94.95 % This value is based upon the 35% over 8 amino acid window threshold using a sliding window yielding 173 matches with the query dataset. This value is based upon the 35% over 8 amino acid window threshold using a full length query yielding 654 matches with the query dataset.

The relationship between a 35% over 8 amino acid window threshold and an E-score based threshold 92 sequences 92 sequences Identified only by 3.9E-7 or lower 562 sequences including all known allergens are identified by both methods Identified only by 35% / 8 aa Identified by both methods 86% overlap 92 by 35% over 8 92 by E-score Number 18 16 14 12 1 8 6 4 2-1 -2-3 -4-5 -6-7 E-score exponent Number 2 15 1 5-7 -8-9 -1-11 -12-13 -14-15 -16-17 -18-19 -22-23 -27-31 -33-43 -44 E-score exponent

The relationship between a sliding 35% over 8 amino acid window threshold and an E-score based threshold 219 sequences 219 sequences Identified only by 4.3E-3 or lower 854 sequences including all known allergens are identified by both methods Identified only by a sliding 35% / 8 aa window Identified by both methods 8% overlap 219 by sliding window 14 219 by E-score N u m b e r 8 6 4 2 Number 12 1 8 6 4-1 -2-3 E-score exponent 2-3 -4-5 -6-7 -8-9 -1-12 -13-14 -16 E-score exponent

The Impact of Amino Acid Composition Bias on E-scores 35 3 25 2 15 1 5 E-scores in this range may be the product of compositional bias Protein amino acid composition may result in E-scores that are significant but that do not represent a meaningful alignment Searches of allergen databases are subject to compositional bias created by glutamine-rich proteins that align with gliadins and glutenins 1.E-3 1.E-5 1.E-7 1.E-9 1.E-11 1.E-13 1.E-15 1.E-17 1.E-19 1.E-21 E-Score 1.E-23 1.E-25 1.E-27 1.E-29 1E-31 or lower Composition bias can be uncovered using shuffled query sequences

Assessing E-score and Amino Acid Composition Bias Using Shuffled Sequences Unshuffled Query E- score Shuffled sequence E- scores Likely nonallergen Potential Allergen Allergen Allergen Glutamine -rich 1.E+ 3.4E-7 4.1E-75 3.4E-154 3.7E-6 > 1 3 2 8 1 >= 1. <1. 522 489 525 543 71 >=.1 <1. 47 423 411 384 351 >=.1 <.1 63 8 59 61 339 >=.1 <.1 4 5 3 9 162 >=.1 <.1 1 3 1 57 >=1E-5 <.1 16 >=1E-6 <1E-5 2 >=1E-7 <1E-6 1 Number of unique database sequences yielding a best match Number of 35% over 8 aa matches with shuffled queries 43 421 453 425 114 Δ 1 3 1 6 7 Δ Of the 114 unique sequences identified by the shuffled queries, 26 of the unique proteins were described as being gliadins and glutenins. These 26 gliadins and glutenins accounted for 844 of the 1 top alignments for the shuffled query.

Test Case: What is the E-score Threshold for Identifying Bet V 1 Homologues? 151 Bet v 1 homologues of 12 amino acids or greater were extracted from the FARRP allergen database Each homologue was used as a query for a FASTA search of the allergen database The lowest E-score between the query and a Bet V 1 homologue of equal or greater length was tabulated The least significant E-score obtained was 5.E-11; well below the observed 4.2E-7 level where a 95% false positive rate was identified Num ber of Bet v 1 hom ologues displaying E-score 35 3 25 2 15 1 5-11 -12-13 -14-15 -16-17 -18-19 E-score exponent value

Conclusions A robust modeling procedure to determine an E-score threshold is proposed Using a threshold that is comparable to the 35% identity in 8 aa criteria yields an E-score of 4.2E-7 We know its conservative based on Statistics; 95% false positive, % false negative The threshold is 1% effective in identifying know corn allergens and full length Bet V 1 homologues The threshold is also of sufficient selectivity that it will result in fewer false positive sequence identifications due to composition biased alignments