The COMPARE Database: A Public Resource for Allergen Identification, Adapted for Continuous Improvement

Introduction

Food allergy is a growing food safety and public health concern. Well-curated, publicly accessible allergen databases serve a range of public health roles, including informing experimental research programs, clinicians, and allergists and/or providing critical data to patients or the public. In the food safety space, an allergen database serves also the purpose of evaluating concerns over the transfer of known allergens or potentially cross-reactive proteins to genetically-modified (GM) food crops or animals. In fact, regulatory agencies evaluating the safety of biotechnology products for genetically-modified food and feeds require an assessment for potential allergenicity, including comparing novel or newly discovered proteins at the level of their primary amino acid sequence to known allergens, which further emphasizes the need for a comprehensive allergen database. The goal in that assessment is to identify proteins that may be known allergens or have sufficient structural similarity to warrant further experimental investigation into their potential to elicit allergic responses based on cross-reactivity.

Therefore, allergen databases support allergy science and safety by two distinct but related processes: (1) comparative processes to identify potential similarity between query sequences and allergens using bioinformatics tools, and (2) identification of the source organism for any listed allergen, allowing researchers to assess taxonomic relatedness among the organisms producing the allergens/proteins.

Some individual companies maintain their own allergen databases. Additionally, several public databases are now available, such as the University of Texas Medical Branch's Structural Database of Allergen Proteins—SDAP (https://fermi.utmb.edu/) (1, 2), World Health Organization (WHO)/International Union of Immunological Societies (IUIS) Allergen Nomenclature Database (http://www.allergen.org/) (3–7), and AllergenOnline (http://www.allergenonline.org) supported by the University of Nebraska Food Allergy Research and Resource Program (FARRP) (8). Others include AllFam (9), Allergome (10, 11) and Allermatch (12), released in 2003 and 2004, respectively. Recently, another allergen database, AllerBase was established (13). Some additional allergen databases are available but not all are maintained regularly and have varying inclusion criteria (14). Reviews of these various allergen databases are available (15–17), in which the features and specific purposes of each resource are described. SDAP for example, can be used to find sequence and structural neighbors for an allergen, and to search for the presence of an epitope (18–20). AllerML (21) is a markup language designed to facilitate exchange of information between databases. Currently SDAP is the only allergen database using AllerML and therefore we have not yet implemented this attribute in the COMPARE database. We do not see this as an impediment to efficient searching in COMPARE as the entities in the COMPARE database are clearly annotated and distinct from the content. Data is easily available for download in both tabular and fasta formats.

COMPARE data is presently being used by other recent databases and tools: AllerCatPro, developed to predict the allergenic potential of proteins based on their 3D protein structure as well as their amino acid sequence (22) and a Random Forest allergenicity prediction model (23).

The initial number of sequences to be filtered when screening new potential allergens has grown exponentially, necessitating higher-density data handling methods to sort and identify information relevant for food safety. As the publication of genomic sequence information increased, the challenge of updating an accurate allergen database intensified as public repositories become diluted with automatically annotated sequence information and sequences that are not expressed proteins. For example, only a minute proportion of proteins in the National Center for Biotechnology Information (NCBI) Protein sequence database have been identified as allergens, with almost 60% of these belonging to four protein families (24, 25). In contrast, the February 2021 release of UniProtKB/TrEMBL counted over 200 million protein sequences; of these, over 67% are “predicted” sequences, 31.7% are inferred from homology, and only about 1% have evidence at the transcript level (0.65%) or protein level (0.08%) data obtained from “UniProtKB/TrEMBL Protein Database Release Statistics,” https://www.ebi.ac.uk/uniprot/TrEMBLstats, last accessed February 12, 2021.

To address both the dilution effect and annotation quality issues, the Health and Environmental Sciences Institute (HESI) Protein Allergens, Toxins and Bioinformatics (PATB) committee has developed the COMprehensive Protein Allergen REsource (COMPARE) database, a new protein allergen database along with a sequence and literature screening process for public access. COMPARE's structure and process is designed to facilitate efficient annual updates. In its initial build, COMPARE started from the foundational sequence list developed by FARRP AllergenOnline database v.16, which shares COMPARE's commitment to blinded peer review and annual curation and updating, to which new sequences identified through the COMPARE process have been added to in 2017 and on each subsequent annual update.

The critical methodological components of the COMPARE database consist of scientific, structural, procedural, and quality/rigor assurance components. The COMPARE process engages a broad international multi-sector scientific steering committee with representation from government (US Environmental Protection Agency and US Food and Drug Administration [FDA]), academia, and private-sector scientists who bring real-world know-how and experience of gaps and challenges to be considered. Oversight and coordination by HESI, an independent and objective institute dedicated to advancing the understanding of scientific issues related to human health and the environment, as well as programmatic support from the Joint Institute for Food Safety and Nutrition (established between the FDA and the University of Maryland, JIFSAN; www.jifsan.umd.edu) in the development of bioinformatics analytical tools associated with the COMPARE database provide organizational support for a structured collaborative effort, made possible with pooled financial support from private sector partners, as well as significant in-kind support from both public and private sector collaborators. The quality and scientific integrity of the COMPARE resource is safeguarded through various mechanisms inherent to HESI's operating model: maintaining public-private representation, transparent documentation, and open access to the database. All database design and search algorithm decisions are publicly documented on the COMPARE website (http://www.comparedatabase.org), under the “Process Development” tab and presently, in this article.

System and Methods

The Database Build Process: Criteria-Based, Transparent, and Reproducible

The COMPARE process implements a consistent and transparent mechanism to identify protein allergens via the following:

(1) The development of a high-throughput, automated sequence sorting algorithm;

(2) The systematic collection of scientific literature supporting the identification of allergens;

(3) The coordinated review by an external peer review panel (PRP) of internationally recognized allergy experts from the public sector;

(4) An annual public release of the database by repeating steps 1–3 to identify new allergens; and

(5) Independent management and documentation practices by the non-profit scientific organization HESI, that ensures the integrity of the database and transparency of the process used to populate the database (e.g., “Documentation” link in the database page, which comprises important information about the content of updates, new features or list of upgrades accompanying a new database release as well as a “COMPARE transparency document” listing all entries reviewed by PRP, their comments and decisions).

Detailed descriptions of each of these procedures (scientific rationale, methodological approach, and assurance of balance/transparency) follow below.

Development of a High-Throughput, Automated Sequence Sorting Algorithm

The Pilot Process Development and Validation

Presently, three primary databases contain all the existing sequence data so far generated (GenBank, EMBL database and DNA Data Bank of Japan). As these databanks are interconnected through the International Nucleotide Sequence Database Collaboration (http://www.insdc.org/), data submitted to any one of these databases are shared by, and hence can be retrieved from, all three.

The starting point for the COMPARE screening process is the National Center for Biotechnology Information (NCBI) Protein sequence database (https://www.ncbi.nlm.nih.gov/protein), which includes translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB.

COMPARE's initial pilot search was designed in 2016 to identify NCBI Protein sequence entries that contain the term “allerg^*” (which encompasses allergen, allergy, allergenic, and allergic) and include proteins that are derived from animals, plants, fungi, and protists. This pilot search of the NCBI Protein sequence database was completed through the main query window (https://www.ncbi.nlm.nih.gov/protein) using the following Boolean search:

“allerg^* AND [time period: from Jan 1, 2014 to Dec 31, 2014 or Jan 1, 2015 to Dec 31, 2015] AND [species: animals, plants, fungi and protists].”

By design, the initial search term “allerg^*” is very broad, can be found anywhere in each GenPept record, and does not necessarily relate to the identification of a protein allergen. Extended search criteria were thus applied in a stepwise fashion to further refine the broad “allerg^*” search results. Keywords for filtering were selected by informatics, allergy, toxicology, and clinical experts (participating members of the public-private COMPARE Steering Team) through an iterative observation-based process, taking into account contextual categorical features such as the sequence's source organism, whether the sequence was submitted through an automated annotation pipeline (i.e., genome sequencing projects), whether “allerg^*” appeared in a protein definition description, and feature lines such as ‘/note = “allergenic/antifungal thaumatin-like proteins.”

Manual examination of random subsets of the search results at various points of the filter helped identify additional keyword-based rules capable of distinguishing between potential allergen candidates (those related to allergenicity and submitted to the expert panel for review) and irrelevant entries (those unrelated to allergenicity and filtered out to reduce the “noise” in the review). These rules were evaluated and compared for their efficiency to eliminate non-allergen entries and the potential for overly aggressive exclusion, which might lead to undesirable omission of potential allergen candidates. Of the two to five rules evaluated in each step, the rule with the highest efficiency and lowest potential for overly discriminate exclusion was selected. When more than one rule had comparably high efficiency, preference was given to the one with a lower chance of omitting potential allergen entries.

Testing and Validation of the Filtering Algorithm

The COMPARE process was validated by testing it against the AllergenOnline v.16 database. The results indicated that many of the sequences containing “allerg^*” in their annotation were classified as candidates by the COMPARE algorithm, as expected. Yet this testing also found that some sequences in AllergenOnline v.16 (GenPept format) do not contain the term “allerg^*” in their annotation, and as such were not being captured. Manual examination of those sequences revealed that they are typically associated with specific allergen designations such as profilin or tropomyosin. This validation step allowed the identification of additional keywords (Supplementary Table 1) to be used in parallel to the “allerg^*” search of the NCBI Protein database. This supplemental search and filtering was performed for the 2017 and 2018 COMPARE database builds. After conducting the supplemental search in parallel of the main search for 2 years, it appeared that the results did not produce significantly new information compared to the outcome of the main algorithm search. This parallel approach was therefore discontinued in subsequent years.

The final COMPARE keyword-based filter consisted of 13 steps and 28 elements (i.e., decision steps/points) (Supplementary Figure 1).

Continuous Improvement Strategies

Additional Sources of Allergen “Candidates”

A robust search for potential new allergen candidates needs to cast a broader net than a year's worth of NCBI Protein database entries for several reasons. First, an allergen may have been identified but not submitted to the NCBI Protein database. This is likely to occur when the allergen discovery is extremely recent. Second, there may be older entries in the NCBI Protein database for which new evidence of allergenicity may have become available. A solution to both situations is a thorough search of the scientific literature each year to capture new evidence. Such a process was implemented in the development of COMPARE 2018 and future iterations of the database, the parallel direct literature search strategy used is described in (Appendix-1 in Supplementary Materials). Lastly, there are other important protein sequence databases available to the scientific community. For example, UniProtKB is a protein database partially hand-curated by experts (26, 27), it was included in the bioinformatics screening for identification of candidate allergens as of COMPARE 2019. There are other allergen-specific databases with various degrees of overlap, as reviewed by Radauer (16) and Radauer and Breiteneder (15)>, from which new allergen candidates not included in COMPARE could be evaluated by the COMPARE expert panel. Such databases include the World Health Organization (WHO)/International Union of Immunological Societies (IUIS) Allergen Nomenclature Database (http://www.allergen.org/) (6), responsible for maintaining and developing a unique, unambiguous, and systematic nomenclature for allergenic proteins, and AllergenOnline (http://www.allergenonline.org) (8), as both are updated regularly (on an ongoing basis and yearly, respectively). As part of 2017' and 2018' workstreams, and a commitment to continuous improvement, the COMPARE process incorporated progressively new sources of allergen candidates (Table 1).

TABLE 1

Table 1. COMPARE database year-by-year evolution of the process development and resulting end-products.

Incorporation of Sequence Comparison Capabilities Accessible to COMPARE Users

A key to understanding cross-reactivity from a sequence-based perspective is to identify a minimum degree of similarity between a query protein and a known allergen that can prompt further investigation. Global guidelines on a minimum similarity (e.g., >35% sequence identity over a sliding window of at least 80 amino acids) have evolved in several ways (28–35) and the validity of the various criteria has been discussed in various reviews (36–41). However, while the scientific merits of these criteria are still evaluated and discussed, all approaches rely on a common element: a database containing protein sequences of all currently recognized allergens, to enable in silico processes of assessing a protein's potential allergenicity.

In January 2019, COMPARE 2019 was released with a more dynamic interface to facilitate the ease of access to relevant information to all users (clickable links for easier access to literature sources) and for increased functionality. This new interface allowed the incorporation of a built-in FASTA-based sequence comparison tools, “COMPASS” (COMPare Analysis of Sequences with Software), where users can compare their selected (query) protein sequence to the allergens in the database. The software of choice has traditionally been the FASTA algorithm (42) and is recommended for assessing similarity between protein sequences. Therefore, COMPASS operates with the open source FASTA software package (FASTA v36 at the time of this publication; https://fasta.bioch.virginia.edu/fasta_www2/fasta_list2.shtml). The incorporation of FASTA search algorithm as the core of the COMPASS search tool on the COMPARE database supports real-time sequence comparisons using the following three approaches without downloading the database and installing software on local computers/servers: (1) full-length sequence comparison; (2) 80-aa (amino acids) sliding window comparison; (3) 8-aa exact match [based on FAO/WHO (30) and CODEX Alimentarius Commission (28, 43) guidelines on the evaluation of proteins derived from modern biotechnology for allergenicity]. As of July 2020, the COMPASS search tool offers a visualization component, allowing users to view results in a graphical display. Additionally, other features such as a reference list and synopses of relevant literature and guidelines, as well as a sort-and-search function of the database listing are available for educational purposes in the COMPASS homepage and “About” tab.

Literature Procurement

One of the key requirements of the COMPARE process is that a “candidate allergen sequence” retained for review by the expert panel has at least one supporting scientific publication related to the specific candidate sequence. This rule is strictly applied in COMPARE, including in cases where a sequence is listed in IUIS but a publication is not yet available (e.g., Der p 37, Der f 26 and Per a 13). These entries are accepted by IUIS based on information provided in direct communication between the submitters of the allergen and the IUIS committee. In relation to other databases, for example, this may also explain differences between COMPARE and AllergenOnline, discussed in section 4.1. Examples of entries in COMPARE not found in IUIS or AllergenOnline include QCI56569.1, Q9YGJ8.2, and CAA58223.1. The identification and procurement of primary scientific literature that relates to the sequences identified as new “allergen candidates” is the second step in the COMPARE process, after the bioinformatics screenings and parallel direct literature search (Appendix-1 in Supplementary Materials). JIFSAN collaborates in the COMPARE process by facilitating (among other program components) the procurement of the literature associated with the candidate allergens and administrating a custom-built web-based portal where the candidate-specific information from all sources is integrated for peer review by an independent panel of international experts.

Peer Review Process

Candidate allergens and associated primary literature data are provided to the COMPARE PRP. The scientific literature review process and voting is the exclusive purview of the PRP and is conducted independently of other COMPARE stakeholder involvement. The PRP determines whether the candidate protein sequence has enough supporting evidence of allergenicity to be included in COMPARE, based on a documented set of criteria (described below) to determine the quality and extent of the data presented in the literature upon which a decision to include/exclude a candidate sequence can be based.

The COMPARE PRP is an international group of five academic and clinical allergy experts (see list of authors' contributions for names of experts in the panel) renowned for their research and expertise in areas such as the nature of protein allergenicity and molecular characterization of allergens, allergenic cross-reactivity, immune-based mechanisms of allergy, immunotherapy, allergen diagnostics, and component-resolved diagnosis. The panel participants operate as volunteers with support from HESI's scientific program management staff and from JIFSAN's staff for information management of the sequences, associated literature data, and implementation of a custom system to collect decisions from PRP experts (PRP review tool). PRP members receive a nominal honorarium via HESI.

Criteria for Inclusion and Exclusion of Allergens in COMPARE

The PRP evaluation criteria are consistent with those widely adopted for inclusion of sequences in other allergen databases (8, 44, 45). The minimum criterion adopted for inclusion of a candidate is peer-reviewed evidence of IgE binding, either in the published literature or other peer-reviewed documentation. This criterion is highly conservative, with no requirement for demonstration of IgE functionality (e.g., cross-linking for degranulation of IgE-bearing cells).

The evaluation of evidence for IgE binding includes consideration of the experimental approach, the quality of sera, and characterization of the IgE binding itself. Ideally, IgE binding is demonstrated to a purified full-length recombinant version of the allergen. In case evidence of IgE binding is based on purified natural protein, the peer-review panel gives special attention to the evidence provided with respect to purity on a case-by-case basis. Similarly, if evidence of IgE binding is based on proteomic approaches combining IgE 1D- or 2D-blots and mass spectrometry, single bands or spots should be discrete enough and each contain sequence evidence from one protein only. Peptides (10 amino acids in length or above) explicitly identified by mass spectrometry and which sequences are included in the supporting literature are accepted as individual entries. As a result, a single protein may be represented in the database by several peptide fragments, listed as individual entries. Table 2 summarizes the inclusion and exclusion criteria, as well as considerations for the scientific review decision process, defined by the COMPARE PRP. Table 3 is a concise overview of the criteria used to declare an allergen in the COMPARE database.

TABLE 2

Table 2. COMPARE peer review panel: scientific review decision process and inclusion/exclusion criteria defined by the experts in the independent panel.

TABLE 3

Table 3. COMPARE database criteria for an allergen sequence.

Candidate sequences rejected by the panel for lack of evidence can be revisited in subsequent years if new evidence becomes available.

Information regarding all candidate allergens submitted to the PRP each year, whether ultimately “accepted” or “rejected,” are retained in internal records for tracking purposes. With the release of COMPARE 2019, the reviewers' decisions and comments about all “accepted” or “rejected” candidates were made publicly available as part of the COMPARE transparency documentation (see the “Database” page and “Documentation” tab at http://www.comparedatabase.org).

In summary, Figure 1 provides a description of the COMPARE peer review cycle and workflow applied at each annual update.

FIGURE 1

Figure 1. Summary of the COMPARE Peer Review Cycle and Workflow for each annual update.

Note that since COMPARE started building upon the existing AllergenOnline v.16 database, allergens with an entry date of 2016 or prior years were not subjected to the COMPARE peer review process.

Maintaining Data Currency and Accuracy

A sequence database that is used to identify potential hazards related to food safety is of maximum utility if it contains the most recent allergens. Therefore, COMPARE maintains its utility via an annual screening of protein sequence databases (NCBI Protein, UniProtKB) and other sources (as described in sections 2.4 and 2.5). It is expected that the COMPARE process will continue to evolve each year, to adapt to the changes in sequencing technology and informatics/annotation trends observed in a previous update cycle. An annual update process facilitates confidence in communicating the presence of new allergens in a timely fashion, while still allowing for a thorough, high-quality review process. The established cyclic annual workflow supports a January (of the next new year) release of the updated database with sequence culling from public databases conducted early enough in the year (typically from May to May).

COMPARE's open processes provide continued opportunities to ensure that the most contemporary evidence is evaluated and incorporated. The COMPARE website includes a dedicated “Contact Us” tab (https://comparedatabase.org/contact-us/) and any user can submit comments through the website portal or the contact email provided.

Implementation and Results

To date, the COMPARE process as described above, with some limited modifications, has been implemented to produce the first release of the database (COMPARE 2017) and four subsequent annual updates (COMPARE 2018–2021 data releases; Table 1). The web interface presents the database main fields in a searchable table view with the following fields for each allergen sequence entry: Species (of the source organism); Common Name, Description (of the allergen sequence), IUIS Name, Accession, Length, Year Adopted (the year of inclusion in the database—note that for sequences marked “2016” or years prior, this corresponds to the year listed for entry of the sequence in AllergenOnline, the foundational data from which COMPARE developed, as marked in AllergenOnline v.16), and a “VIEW” clickable box leading to a window with the individual data for each sequence (the actual sequence, and supporting scientific publications with evidence of allergenicity).

Figure 2 provides an overview of the 2017 COMPARE database build process and quantitative outcomes of the screening process. Candidate entries for the 2017 COMPARE database were obtained from the NCBI Protein database as accessed through the main query window (https://www.ncbi.nlm.nih.gov/protein) on May 14, 2016 using the following Boolean search:

FIGURE 2

Figure 2. COMPARE 2017 screening process quantitative outcomes (supporting information provided in Appendix-2 of the Supplementary Materials).

“allerg^* AND [time period: from May 30, 2015 to May 14, 2016] AND [species: animals, plants, fungi and protists].”

The original methodology was improved by broadening the sources of sequences to include IUIS and AllergenOnline as new sources, in addition to the NCBI Protein database. Therefore, sequences under the IUIS listing, going back to June 2015, were evaluated in the build of COMPARE 2018, and any sequences present in AllergenOnline v.17 that were not already included in COMPARE 2017 were evaluated for potential inclusion. Additionally, a targeted peer-reviewed literature search was implemented to identify new allergen sequences (Appendix-1 in Supplementary Materials). Supplementary Table 2 describes the number of candidate allergens identified through the various sources and the final number of allergens accepted in COMPARE 2018, from each category. The 2018 COMPARE database included 68 allergen sequences added to the 1,970 allergens constituting COMPARE 2017 (2,038 total).

The COMPARE 2019 evaluation process results are presented in Supplementary Table 3; there are 2,081 sequences in COMPARE 2019.

The COMPARE 2020 evaluation process results are presented in Supplementary Table 4; there are 2,248 sequences in COMPARE 2020.

The COMPARE 2021 evaluation process results are presented in Supplementary Table 5; there are 2,348 sequences in COMPARE 2021.

Along with the continuous database update and process improvement, sequence comparison tools associated with the database were implemented to provide bioinformatics analysis of proteins for potential allergenicity. Currently, there are three sequence comparison approaches accessible to the public: (1) FASTA search of the whole protein sequence as a query against the up-to-date COMPARE database. The search parameters such as E-value can be set by the user. The output alignments can be downloaded for further evaluation; (2) FASTA searches (default search parameters) of each sequentially overlapping 80aa fragment (sliding window) from a query protein against the up-to-date COMPARE database using the default search parameters of FASTA. Any alignment >35% identity over ≥ 80 aa will be displayed as a hit that needs further evaluation based on the guideline of FAO/WHO (30) and CODEX Alimentarius Commission (28, 43); (3) A FASTA search (default search parameters) of the whole protein sequence as a query against the up-to-date COMPARE database for the exact match of eight contiguous amino acids with the allergens in the database, although this approach adds little value to the risk assessment of proteins for potential allergenicity due to high false positive rates (47–49).

Discussion and Strategies for Harmonization

Reliable resources to assess the allergenic potential of novel proteins expressed in foods are essential to confidently make safety determinations and provide robust resources with public accessibility. The internationally recognized standard for safety assessment of foods derived from biotechnology is the Codex Alimentarius Commission (43) and recommends that novel proteins expressed in foods be analyzed for any amino acid sequence similarities to known allergens. Using a comparative sequence-level approach for the initial scan of potential allergens allows for a reproducible and fully documented process to establish a protein allergen database. Clinical relevance of allergen sequences by a panel of scientific experts in allergology provides validation that entries added in COMPARE are known allergens, allowing the database to be a reliable resource. There are many constituencies that may be interested in this type of database: product developers providing safety information on novel proteins; regulatory agencies responsible for food and feed safety assessments; medical personnel in the allergy field; the public, who may be interested in identifying sources of allergens. Overall, the transparent and peer-reviewed method to generate the COMPARE database continues to meet the requirements of regulatory agencies to communicate the output results of protein allergenicity screens to the public.

Learnings From 5 Years of COMPARE

The results portrayed in Figure 2 (quantitative outcomes from the screening and review processes for COMPARE 2017) illustrate how important it is to implement an automated sorting algorithm to sort through the vast amount of “noise” retrieved from protein sequence database screenings, when searching for any protein related to “allerg^*” (55,641 hit sequences) or when using keywords very specific to allergens such as profilins, tropomyosins, and so on (15,704 hit sequences). The analysis of the results of the second-year process suggests that the noise increases year by year: the combined retrieved numbers for “allerg^*” and specific allergen keywords in the initial screening were close to 118,000 hits for COMPARE 2018, almost double that for COMPARE 2017 (data not shown).

Furthermore, the analysis of the number of candidate allergens identified through the various sources compared to the final number of allergens accepted from COMPARE 2018 to 2021 (Supplementary Tables 2–5), for each category, sheds light on questions about the differences between the total number of allergens in COMPARE and AllergenOnline. In fact, some AllergenOnline candidate entries do not meet the COMPARE criteria: in most years the PRP rejected around 50% or more of the candidate sequences from AllergenOnline, on the basis that the articles associated with those entries in AllergenOnline did not contain the specific sequence being evaluated; the IgE binding data provided in the article was not related to the exact candidate sequence; or the candidate sequence and the data in the associated article referred to different species. In other words, the requirement of an exact match between the candidate sequence being evaluated and the sequence(s) tested in the associated literature was not met in these specific instances (specific examples can be found in the COMPARE Database webpage, by clicking on the “Documentation” hyperlink, and then opening the “Transparency file”).

Additionally, the standardized key-word search of literature databases has proven to be a valuable complementary source of valid candidates and resulted in some instances in additional allergens being picked up by COMPARE that were not listed in AllergenOnline of the same year's update. Examples of this include accessions ADV71357.1, NP_001036878.1, NP_001138311.1, XP_392204.2, which are not found in AllergenOnline at the time of these writings.

Strategies for Harmonization

Given the scientific and public health value of a curated allergen database, opportunities to continually enhance its utility are paramount. As such, several improvements have been identified and have been completed or are under active development. These activities (further described in the paragraphs below) include: (1) retrospective historic screenings of the years prior to 2016 to identify possible extra allergens that may not have been included in AllergenOnline v.16, and (2) curation of the COMPARE database to include all available WHO/IUIS recognized allergen designations.

Applying Current COMPARE Approaches to Historical Datasets

As described in this article, COMPARE builds on the foundational sequence list developed over a 10-year period by the University of Nebraska FARRP, which publishes AllergenOnline (8). The list of sequences from AllergenOnline v16 were imported “as is” and were not evaluated by COMPARE PRP. Recently, a screening of historic datasets with records dated from 2016 and years prior was undertaken by applying the COMPARE process to harmonize the database content to up-to-date COMPARE processes (since from the beginning, starting in 2016, the process always consisted in screenings of the previous “1 year” time-window). In this process, the decision was taken to not remove sequences uploaded into COMPARE 2017 from the foundational sequence list from AllergenOnline v.16, but only to complement the database with additional sequences identified using the COMPARE algorithm applied retrospectively and reviewed by PRP. Therefore, the data from AllergenOnline v.16 has remained intact over the years, as imported for the built of COMPARE 2017. This choice was made to remain conservative and to avoid confusion amongst users. Overall, based on the high-throughput COMPARE screening process, and after PRP review, the historic screenings contributed to 57 new sequences added in COMPARE 2020, a substantial part of them not included in the current version of AllergenOnline. The supporting articles associated with these sequences are dated from 2016 or before, and it is unclear why they were not part of AllergenOnline v16. Sequences from the historic screenings are labeled as “2020H” or “2020H_MS” in the field “year adopted” of the database, as well as in column B of the “COMPARE-2020 Transparency file” (available under the “Documentation” tab of the COMPARE database website), to distinguish them from entries resulting from the regular annual screening process, labeled “2020”.

Curating Allergen Designations in COMPARE to Nomenclature Standards

Standardized allergen naming conventions, as set by the WHO/IUIS Allergen Nomenclature Sub-Committee, help reduce the use of non-standard allergen names and descriptions and are a key aspect of understanding related proteins across species (6). Unfortunately, not all sequence records of allergens comply with IUIS standards: allergens identified in older research have been named according to various non-standard conventions; many allergen sequences identified in more recent years often include irrelevant automated annotations from sequence records. For these reasons, it is important to undertake a curation of allergen designations to generate meaningful description lines to the eyes of allergy experts and allergen database users, based on official IUIS allergen designations when available, as opposed to automatically adopting description lines from source protein records (generally from the NCBI Protein database or UniProtKB). Such curation has been started in COMPARE 2019 (for all new entries added in 2019) based on a defined stepwise priority approach described in the “COMPARE 2019 Documentation File” (available at http://db.comparedatabase.org/docs/COMPARE-2019-Documentation-2019-01-17.pdf?v=20190117), and has continued in future iterations of COMPARE. The COMPARE management team expects the adoption of allergen names to current standards, as set by the WHO/IUIS Allergen Nomenclature Sub-Committee (http://www.allergen.org), to be an ongoing quality improvement process that will occur in parallel with the curation of the “Description” fields, by the experts in the PRP. The goal will be to alleviate confusion over multiple names and make clearer the link between the publications that support a protein's inclusion in COMPARE as an allergen and sequences themselves.

Conclusions

The COMPARE database provides a rigorous, transparent, and annually updated resource for screening potential allergens. The data-driven and clinical research–based screening processes allow for comprehensive and clearly documented sequence identification and evaluation by an independent panel of experts. The process has flexibility to account for changes in technology, variability in terminology, and annotation trends by incorporating the observations and learnings of a given cycle into the following build cycle, reflecting ongoing continuous improvement. With oversight and coordination by HESI, additional enhancements to COMPARE are anticipated to provide added utility to meet contemporary safety assessment and research needs into the future. In addition, the bioinformatics analytical capability through COMPASS provides tools to the public for the risk assessment of potential protein allergenicity.

Data Availability Statement

The COMPARE database is publicly available at: http://www.comparedatabase.org. Bioinformatics analytical tools (COMPASS) associated with the COMPARE allergen database are publicly available directly from the database page, or via: https://comparefasta.foodrisk.org/. The original contributions presented in the study are included in the article and Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

RR: writing/critically reviewing of manuscript, COMPARE steering team member, and peer-review panel chair. DS: programmatic support for: database design, design & development of online tool for peer-review panel, development of the COMPASS tool, and critically reviewing of manuscript. MB: critically reviewing of manuscript, peer-review panel member (until 2019 update, replaced by GG of the University of Salzburg). LB, SM, AS, PS, and EI: writing/critically reviewing of manuscript, COMPARE steering team member (scientific & technical advisory). AC: programmatic support for: database design, design & development of online tool for peer-review panel, development of the COMPASS tool, and critically reviewing of manuscript. GG: critically reviewing of manuscript and peer-review panel member. PG: programmatic support for: database design, design & development of online tool for peer-review panel, and critically reviewing of manuscript. KH-S: critically reviewing of manuscript and peer-review panel member. LK: critically reviewing of manuscript and COMPARE program manager. JK and GL: critically reviewing of manuscript and COMPARE steering team member (scientific & technical advisory). KM: programmatic support for: database design, design and development of online tool for peer-review panel, development of the COMPASS tool, and critically reviewing of manuscript. SM-R: critically reviewing of manuscript and COMPARE program manager. CN: critically reviewing of manuscript and design/construction online tool for peer-review panel. LPM: writing/critically reviewing of manuscript and COMPARE program manager. SP: project design and stakeholders' involvement and critically reviewing of manuscript. LKP: critically reviewing of manuscript, COMPARE steering team member, and peer-review panel member. ST: critically reviewing of manuscript and peer-review panel member. MB: writing/critically reviewing of manuscript and COMPARE steering team member (2016–2017). All authors contributed to the article and approved the submitted version.

Funding

This HESI scientific initiative is primarily supported by the in-kind contributions (from public and private sector participants) of time, expertise, and experimental and/or development effort. These contributions are supplemented by direct funding (that primarily supports program infrastructure and management and some project-related direct expenses) provided by HESI's corporate sponsors. A list of supporting organizations (public and private) is available at http://hesiglobal.org. HESI is a non-profit scientific organization that facilitates public and private partnerships in human and environmental health. The PATB committee is committed to advancing the scientific understanding of the relevant parameters defining allergenic proteins and protein toxins, and to encourage the development of reliable and accurate methodologies for characterizing the allergenic potential of novel proteins leveraging the power of bioinformatics approaches.

Conflict of Interest

LB was employee of Limagrain Field Seeds. EI was employee of BASF Corporation. GL was employee of DuPont Co., Nutrition and Biosciences. SM was employee of Syngenta Crop protection. AS was employee of Bayer Crop Science. PS was employee of Corteva Agriscience LLC. CB was employed by Bayer Crop Science. GS was employed by company IFF Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors gratefully acknowledge the input from the government, academic, and industry scientists of the HESI Protein Allergens, Toxins and Bioinformatics (PATB) committee for their contributions to this work, as well as the Joint Institute for Food Safety and Nutrition (established between the FDA and the University of Maryland) for their partnership in developing and maintaining the COMPARE allergen database The authors also thank former HESI staff Brianna Farr and Oscar Bermudez for their support at various stages of the project, as well as Christina West for the manuscript review and editorial support.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/falgy.2021.700533/full#supplementary-material

References

1. Ivanciuc O, Schein CH, Braun W. Data mining of sequences and 3D structures of allergenic proteins. Bioinformatics. (2002) 18:1358–64. doi: 10.1093/bioinformatics/18.10.1358

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Ivanciuc O, Schein CH, Braun W. SDAP: database and computational tools for allergenic proteins. Nucleic Acids Res. (2003) 31:359–62. doi: 10.1093/nar/gkg010

PubMed Abstract | CrossRef Full Text | Google Scholar

3. King TP, Hoffman D, Lowenstein H, Marsh DG, Platts-Mills TA, Thomas W. Allergen nomenclature. WHO/IUIS allergen nomenclature subcommittee. Int Arch Allergy Immunol. (1994) 105:224–33. doi: 10.1159/000236761

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Chapman MD, Pomes A, Breiteneder H, Ferreira F. Nomenclature and structural biology of allergens. J Allergy Clin Immunol. (2007) 119:414–40. doi: 10.1016/j.jaci.2006.11.001

CrossRef Full Text | Google Scholar

5. Radauer C, Nandy A, Ferreira F, Goodman RE, Larsen JN, Lidholm J, et al. Update of the WHO/IUIS allergen nomenclature database based on analysis of allergen sequences. Allergy. (2014) 69:413–9. doi: 10.1111/all.12348

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Pomés A, Davies JM, Gadermaier G, Hilger C, Holzhauser T, Lidholm J, et al. WHO/IUIS allergen nomenclature: providing a common language. Mol Immunol. (2018) 100:3–13. doi: 10.1016/j.molimm.2018.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Goodman RE, Breiteneder H. The WHO/IUIS allergen nomenclature. Allergy. (2019) 74:429–31. doi: 10.1111/all.13693

CrossRef Full Text | Google Scholar

8. Goodman RE, Ebisawa M, Ferreira F, Sampson HA, van Ree R, Vieths S, et al. AllergenOnline: a peer-reviewed, curated allergen database to assess novel food proteins for potential cross-reactivity. Mol Nutr Food Res. (2016) 60:1183–98. doi: 10.1002/mnfr.201500769

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Radauer C, Bublin M, Wagner S, Mari A, Breiteneder H. Allergens are distributed into few protein families and possess a restricted number of biochemical functions. J Allergy Clin Immunol. (2008) 121:847–52.e7. doi: 10.1016/j.jaci.2008.01.025

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Mari A, Scala E, Palazzo P, Ridolfi S, Zennaro D, Carabella G. Bioinformatics applied to allergy: allergen databases, from collecting sequence information to data integration. The Allergome platform as a model. Cell Immunol. (2006) 244:97–100. doi: 10.1016/j.cellimm.2007.02.012

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Mari A, Rasi C, Palazzo P, Scala E. Allergen databases: current status and perspectives. Curr Allergy Asthma Rep. (2009) 9:376–83. doi: 10.1007/s11882-009-0055-9

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Fiers MWEJ, Kleter GA, Nijland H, Peijnenburg AACM, Nap JP, van Ham RCHJ. Allermatch™, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines. BMC Bioinf. (2004) 5:133. doi: 10.1186/1471-2105-5-133

CrossRef Full Text | Google Scholar

13. Kadam K, Karbhal R, Jayaraman VK, Sawant S, Kulkarni-Kale U. AllerBase: a comprehensive allergen knowledgebase. Database. (2017) 2017:1–12. doi: 10.1093/database/bax066

CrossRef Full Text | Google Scholar

14. Veličković TC, Gavrović-Jankulović M. Predicting potential allergenicity of new proteins introduced by biotechnology. In: Food Allergens. New York, NY: Springer. (2014). p. 175–92. doi: 10.1007/978-1-4939-0841-7_8

CrossRef Full Text | Google Scholar

15. Radauer C, Breiteneder H. Allergen databases-A critical evaluation. Allergy. (2019) 74:2057–60. doi: 10.1111/all.13841

CrossRef Full Text | Google Scholar

16. Radauer C. Navigating through the jungle of allergens: features and applications of allergen databases. Int Arch Allergy Immunol. (2017) 173:1–11. doi: 10.1159/000471806

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Sircar G, Sarkar D, Bhattacharya SG, Saha S. Allergen databases. Methods Mol Biol. (2014) 1184:165–81. doi: 10.1007/978-1-4939-1115-8_9

CrossRef Full Text | Google Scholar

18. Oezguen N, Zhou B, Negi SS, Ivanciuc O, Schein CH, Labesse G, et al. Comprehensive 3D-modeling of allergenic proteins and amino acid composition of potential conformational IgE epitopes. Mol Immunol. (2008) 45:3740–7. doi: 10.1016/j.molimm.2008.05.026

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Power TD, Ivanciuc O, Schein CH, Braun W. Assessment of 3D models for allergen research. Proteins. (2013) 81:545–54. doi: 10.1002/prot.24239

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Baek M, Park T, Woo H, Seok C. Prediction of protein oligomer structures using GALAXY in CASP13. Proteins. (2019) 87:1233–40. doi: 10.1002/prot.25814

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Ivanciuc O, Gendel SM, Power TD, Schein CH, Braun W. AllerML: markup language for allergens. Regul Toxicol Pharmacol. (2011) 60:151–60. doi: 10.1016/j.yrtph.2011.03.006

CrossRef Full Text | Google Scholar

22. Maureer-Stroh S, Krutz NL, Kern PS, Gunalan V, Nguyen MN, Limviphuvadh V, et al. AllerCatPro - prediction of the protein allergenicity potential from the protein sequence. Bioinformatics. (2019) 35:3020–7. doi: 10.1093/bioinformatics/btz029

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Westerhout J, Krone T, Snippe A, Babé L, McClain S, Ladics GS, et al. Allergenicity prediction of novel and modified proteins: Not a mission impossible! Development of a random forest allergenicity prediction model. Regul Toxicol Pharmacol. (2019) 107:10442. doi: 10.1016/j.yrtph.2019.104422

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Breiteneder H, Radauer C. A classification of plant food allergens. J Allergy Clin Immunol. (2004) 113:821–830. doi: 10.1016/j.jaci.2004.01.779

CrossRef Full Text | Google Scholar

25. Taylor SL, Hefle SL. Will genetically modified foods be allergenic? J Allergy Clin Immunol. (2001) 107:765–71. doi: 10.1067/mai.2001.114241

PubMed Abstract | CrossRef Full Text | Google Scholar

26. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. (2014) 43:D204–12. doi: 10.1093/nar/gku989

CrossRef Full Text | Google Scholar

27. UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. (2016) 45:D158–69. doi: 10.1093/nar/gkw1099

CrossRef Full Text | Google Scholar

28. Codex Alimentarius Commission. Appendix III (Guideline for the conduct of food safety assessment of foods derived from recombinant-DNA plants) and Appendix IV (Annex on the assessment of possible allergenicity). in Alinorm 03/34: Joint FAO/WHO Food Standard Programme, Codex Alimentarius Commission, Twenty-Fifth Session. Rome: FAO/WHO Food Standard Programme, Codex Alimentarius Commission (2003). p. 47–60.

29. Cressman RF, Ladics G. Further evaluation of the utility of “sliding window” FASTA in predicting cross-reactivity with allergenic proteins. Regul Toxicol Pharmacol. (2009) 54:S20–5. doi: 10.1016/j.yrtph.2008.11.006

PubMed Abstract | CrossRef Full Text | Google Scholar

30. FAO/WHO. Evaluation of Allergenicity of Genetically Modified Foods. Report of a Joint FAO/WHO Expert Consultation on Allergenicity of Foods Derived From Biotechnology. Rome: Food and Agriculture Organization of the United Nations (2001).

Google Scholar

31. Metcalfe DD, Astwood JD, Townsend R, Sampson HA, Taylor SL, Fuchs RL. Assessment of the allergenic potential of foods derived from genetically engineered crop plants. Crit Rev Food Sci Nutr. (1996) 36:165–86. doi: 10.1080/10408399609527763

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Mirsky HP, Cressman RF, Ladics GS. Comparative assessment of multiple criteria for the in silico prediction of cross-reactivity of proteins to known allergens. Regul Toxicol Pharmacol. (2013) 67:232–9. doi: 10.1016/j.yrtph.2013.08.001

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Silvanovich A, Bannon G, McClain S. The use of E-scores to determine the quality of protein alignments. Regul Toxicol Pharmacol. (2009) 54:S26–31. doi: 10.1016/j.yrtph.2009.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Song P, Herman RA, Kumpatla S. Evaluation of global sequence comparison and one-to-one FASTA local alignment in regulatory allergenicity assessment of transgenic proteins in food crops. Food Chem Toxicol. (2014) 71:142–8. doi: 10.1016/j.fct.2014.06.008

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Song P, Herman R, Kumpatla S. FASTA update: using the power of E–values in FASTA to detect potential allergen cross–reactivity. Toxicol Rep. (2015) 2:1145–8. doi: 10.1016/j.toxrep.2015.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Goodman RE, Tetteh AO. Suggested improvements for the allergenicity assessment of genetically modified plants used in foods. Curr Allergy Asthma Rep. (2011) 11:317–24. doi: 10.1007/s11882-011-0195-6

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Goodman RE, Hefle SL, Taylor SL, van Ree R. Assessing genetically modified crops to minimize the risk of increased food allergy: a review. Int Arch Allergy Immunol. (2005) 137:153–66. doi: 10.1159/000086314

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Goodman RE, Vieths S, Sampson HA, Hill D, Ebisawa M, Taylor SL, et al. Allergenicity assessment of genetically modified crops-what makes sense? Nat Biotechnol. (2008) 26:73. doi: 10.1038/nbt1343

CrossRef Full Text | Google Scholar

39. König A, Cockburn A, Crevel RWR, Debruyne E, Grafstroem R, Hammerling U, et al. Assessment of the safety of foods derived from genetically modified (GM) crops. Food Chem Toxicol. (2004) 42:1047–88. doi: 10.1016/j.fct.2004.02.019

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Song P. Bioinformatics application in regulatory assessment for potential allergenicity of transgenic proteins in food crops. In: Watson RR, Preedy VR, editors. Genetically Modified Organisms in Food: Production, Safety, Regulation and Public Health. London: Academic Press (2015). p. 397–411. doi: 10.1016/B978-0-12-802259-7.00035-X

CrossRef Full Text

41. Vaughan K, Peters B, Larche M, Pomes A, Broide D, Sette A. Strategies to query and display allergy-derived epitope data from the immune epitope database. Int Arch Allergy Immunol. (2013) 160:334–45. doi: 10.1159/000343880

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Pearson WR, Lipman DJ. Improved tools for biological sequence comparison. Proc Natl Acad Sci USA. (1988) 85:2444–8. doi: 10.1073/pnas.85.8.2444

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Codex Alimentarius Commission. Codex Alimentarius: Foods Derived from Modern Biotechnology. 2nd ed. Joint FAO/WHO Food Standards Programme. Rome: World Health Organization (2009).

Google Scholar

44. Breiteneder H, Chapman MD. Allergen nomenclature. In: Lockey RF, Ledford DK, editors. Allergens and Allergen Immunotherapy: Subcutaneous, Sublingual, and Oral. 5th ed. Boca Raton, FL: CRC Press (2014). p. 37–50. doi: 10.1201/b16539-3

CrossRef Full Text | Google Scholar

45. Goodman R, van Ree R, Vieths S, Ferreira F, Ebisawa M, Sampson HA, et al. Criteria used to categorise proteins as allergens for inclusion in allergenonline.org: a curated database for risk assessment. Clin Transl Allergy. (2014) 4:P12. doi: 10.1186/2045-7022-4-S2-P12

CrossRef Full Text | Google Scholar

46. Holzweber F, Svehla E, Fellner W, Dalik T, Stubler S, Hemmer W, et al. Inhibition of IgE binding to cross-reactive carbohydrate determinants enhances diagnostic selectivity. Allergy. (2013) 68:1269–77. doi: 10.1111/all.12229

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Silvanovich A, Nemeth MA, Song P, Herman R, Tagliani L, Bannon GA. The value of short amino acid sequence matches for prediction of protein allergenicity. Toxicol Sci. (2006) 90:252–8. doi: 10.1093/toxsci/kfj068

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Herman RA, Song P, ThirumalaiswamySekhar A. Value of eight-amino-acid matches in predicting the allergenicity status of proteins: an empirical bioinformatic investigation. Clin Mol Allergy. (2009) 7:9. doi: 10.1186/1476-7961-7-9

PubMed Abstract | CrossRef Full Text | Google Scholar

49. EFSA Panel on Genetically Modified Organisms. (GMO Panel) Scientific Opinion on the assessment of allergenicity of GM plants and microorganisms and derived food and feed. EFSA J. (2010) 8:1700. doi: 10.2903/j.efsa.2010.1700

CrossRef Full Text | Google Scholar