The importance of the 2S albumins for allergenicity and cross-reactivity of peanuts, tree nuts, and sesame seeds.

Allergies to peanuts, tree nuts, and sesame seeds are among the most important food-related causes of anaphylaxis. Important clinical questions include: Why is there a variable occurrence of coallergy among these foods and Is this immunologically mediated? The clinical and immunologic data summarized here suggest an immunologic basis for these coallergies that is based on similarities among the 2S albumins. Data from component resolved diagnostics have highlighted the relationship between IgE binding to these allergens and the presence of IgE-mediated food allergy. Furthermore, in vitro and in vivo experiments provide strong evidence that the 2S albumins are the most important allergens in peanuts for inducing an allergic effector response. Although the 2S albumins are diverse, they have a common disulfide-linked core with similar physicochemical properties that make them prime candidates to explain much of the observed coallergy among peanuts, tree nuts, and sesame seeds. The well-established frequency of cashew and pistachio nut coallergy (64%-100%) highlights how the structural similarities among their 2S albumins may account for observed clinical cross-reactivity. A complete understanding of the physicochemical properties of the 2S albumins in peanuts, tree nuts, and sesame seeds will enhance our ability to diagnose, treat, and ultimately prevent these allergies.

IgE binding to linear epitopes of Ara h 2 in peanut allergic preschool children undergoing oral Immunotherapy.

BACKGROUND: For patients with peanut allergy, there are currently no methods to predict who will develop sustained unresponsiveness (SU) after oral immunotherapy (OIT). OBJECTIVE: Assess IgE binding to peanut (PN), Ara h 2, and specific linear epitopes of Ara h 2 as predictors of the important clinical parameters: eliciting dose threshold and attainment of SU following OIT. METHODS: Samples and clinical data were collected from children undergoing OIT. PN- and Ara h 2-sIgE were quantified by ImmunoCAP® . IgE binding to linear peptides of Ara h 2 and Ara h 6 was measured with peptide microarrays. RESULTS: Values of PN-sIgE correlated with eliciting dose (P = .001) and with a higher likelihood of achieving SU (P < .0001), but these relationships were lost at higher values for PN-sIgE (≥14 kIU for eliciting dose and ≥35 kIU/L for SU). In subjects with PN-sIgE ≥ 14 kIU/L, binding of IgE to epitopes 5 and 6 of Ara h 2 was associated with a lower eliciting dose at baseline challenge (P < .001; Pc  < .02). In subjects with PN-sIgE ≥ 35 kIU/L, a combined model of IgE binding to epitopes 1, 5 and 6 with PN-sIgE was highly predictive of attainment of SU (AUC of 0.86; P = .0067). CONCLUSION: In young patients with peanut allergy, measurement of PN-sIgE and IgE binding to specific linear epitopes of Ara h 2 in baseline samples may allow stratification of patients regarding sensitivity to challenge and outcome of OIT.

Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity.

The phenomenon of cross-reactivity between allergenic proteins plays an important role to understand how the immune system recognizes different antigen proteins. Allergen proteins are known to cross-react if their sequence comparison shows a high sequence identity which also implies that the proteins have a similar 3D fold. In such cases, linear sequence alignment methods are frequently used to predict cross-reactivity between allergenic proteins. However, the prediction of cross-reactivity between distantly related allergens continues to be a challenging task. To overcome this problem, we developed a new structure-based computational method, Cross-React, to predict cross-reactivity between allergenic proteins available in the Structural Database of Allergens (SDAP). Our method is based on the hypothesis that we can find surface patches on 3D structures of potential allergens with amino acid compositions similar to an epitope in a known allergen. We applied the Cross-React method to a diverse set of seven allergens, and successfully identified several cross-reactive allergens with high to moderate sequence identity which have also been experimentally shown to cross-react. Based on these findings, we suggest that Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity. Availability and Implementation: : Cross-React is available at: http://curie.utmb.edu/Cross-React.html. Contact: ssnegi@utmb.edu.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Conformational IgE epitopes of peanut allergens Ara h 2 and Ara h 6.

BACKGROUND: Cross-linking of IgE antibody by specific epitopes on the surface of mast cells is a prerequisite for triggering symptoms of peanut allergy. IgE epitopes are frequently categorized as linear or conformational epitopes. Although linear IgE-binding epitopes of peanut allergens have been defined, little is known about conformational IgE-binding epitopes. OBJECTIVE: To identify clinically relevant conformational IgE epitopes of the two most important peanut allergens, Ara h 2 and Ara h 6, using phage peptide library. METHODS: A phage 12mer peptide library was screened with allergen-specific IgE from 4 peanut-allergic patients. Binding of the mimotopes to IgE from a total of 29 peanut-allergic subjects was measured by ELISA. The mimotope sequences were mapped on the surface areas of Ara h 2 and Ara h 6 using EpiSearch. RESULTS: Forty-one individual mimotopes were identified that specifically bind anti- Ara h 2/Ara h 6 IgE as well as rabbit anti-Ara h 2 and anti-Ara h 6 IgG. Sequence alignment showed that none of the mimotope sequences match a linear segment of the Ara h 2 or Ara h 6 sequences. EpiSearch analysis showed that all the mimotopes mapped to surface patches of Ara h 2 and Ara h 6. Eight of the mimotopes were recognized by more than 90% of the patients, suggesting immunodominance. Each patient had distinct IgE recognition patterns but the recognition frequency was not correlated to the concentration of peanut specific IgE or to clinical history. CONCLUSIONS: The mimotopes identified in this study represent conformational epitopes. Identification of similar surface patches on Ara h 2 and Ara h 6 further underscores the similarities between these two potent allergens.

Western equine encephalitis virus: evolutionary analysis of a declining alphavirus based on complete genome sequences.

UNLABELLED: Western equine encephalitis virus (WEEV) is an arbovirus from the genus Alphavirus, family Togaviridae, which circulates in North America between birds and mosquitoes, occasionally causing disease in humans and equids. In recent decades, human infection has decreased dramatically; the last documented human case in North America occurred in 1994, and the virus has not been detected in mosquito pools since 2008. Because limited information exists regarding the evolution of WEEV, we analyzed the genomic sequences of 33 low-passage-number strains with diverse geographic and temporal distributions and performed comprehensive phylogenetic analyses. Our results indicated that WEEV is a highly conserved alphavirus with only approximately 5% divergence in its most variable genes. We confirmed the presence of the previously determined group A and B lineages and further resolved group B into three sublineages. We also observed an increase in relative genetic diversity during the mid-20th century, which correlates with the emergence and cocirculation of several group B sublineages. The estimated WEEV population size dropped in the 1990s, with only the group B3 lineage being sampled in the past 20 years. Structural mapping showed that the majority of substitutions in the envelope glycoproteins occurred at the E2-E2 interface. We hypothesize that an event occurred in the mid-20th century that resulted in the increased genetic diversity of WEEV in North America, followed by genetic constriction due to either competitive displacement by the B3 sublineage or stochastic events resulting from a population decline. IMPORTANCE: Western equine encephalitis virus (WEEV) has caused several epidemics that resulted in the deaths of thousands of humans and hundreds of thousands of equids during the past century. During recent decades, human infection decreased drastically and the virus has not been found in mosquito pools since 2008. Because limited information exists regarding the evolution of WEEV, we analyzed 33 complete genome sequences and conducted comprehensive phylogenetic analyses. We confirmed the presence of two major lineages, one of which diverged into three sublineages. Currently, only one of those sublineages is found circulating in nature. Understanding the evolution of WEEV over the past century provides a unique opportunity to observe an arbovirus that is in decline and to better understand what factors can cause said decline.

AllergenAI: a deep learning model predicting allergenicity based on protein sequence.

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens' physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using∼ 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

The updated Structural Database of Allergenic Proteins (SDAP 2.0) provides 3D models for allergens and incorporated bioinformatics tools.

Background: Allergenic proteins can cause IgE-mediated adverse reactions in sensitized individuals. Although the sequences of many allergenic proteins have been identified, bioinformatics data analysis with advanced computational methods and modeling is needed to identify the basis for IgE binding and cross-reactivity. Objective: We aim to present the features and use of the updated Structural Database of Allergenic Proteins 2.0 (SDAP 2.0) webserver, a unique, publicly available resource to compare allergens using specially designed computational tools and new high-quality 3-D models for most known allergens. Methods: Previously developed and novel software tools for identifying cross-reactive allergens using sequence and structure similarity are implemented in SDAP 2.0. A comprehensive set of high-quality 3-D models of most allergens was generated with the state-of-the-art AlphaFold 2 software. A graphics tool enables the interactive visualization of IgE epitopes on experimentally determined and modeled 3-D structures. Results: A user can search for allergens similar to a given input sequence with the FASTA algorithm or the window-based World Health Organization/International Union of Immunological Societies (WHO/IUIS) guidelines on safety concerns of novel food products. Peptides similar to known IgE epitopes can be identified with the property distance tool and conformational epitopes by the Cross-React method. The updated database contains 1657 manually curated sequences including all allergens from the IUIS database, 334 experimentally determined X-ray or NMR structures, and 1565 3-D models. Each allergen/isoallergen is classified according to its protein family. Conclusions: SDAP provides access to the steadily increasing information on allergenic structures and epitopes with integrated bioinformatics tools to identify and analyze their similarities. In addition to serving the research and regulatory community, it provides clinicians with tools to identify potential coallergies in a sensitive patient and can help companies to design hypoallergenic foods and immunotherapies.

Alanine Scanning of the Unstructured Region of Ara h 2 and of a Related Mimotope Reveals Critical Amino Acids for IgE Binding.

SCOPE: The unstructured region of Ara h 2, referred to as epitope 3, contains a repeated motif, DYPSh (h = hydroxyproline) that is important for IgE binding. METHODS AND RESULTS: IgE binding assays to 20mer and shorter peptides of epitope 3, defines a 16mer core sequence containing one copy of the DPYSh motif, DEDSYERDPYShSQDP. This study performs alanine scanning of this and a related 12mer mimotope, LLDPYAhRAWTK. IgE binding, using a pool of 10 sera and with individual sera, is greatly reduced when alanine is substituted for aspartate at position 8 (D8; p < 0.01), tyrosine at position 10 (Y10; p < 0.01), and hydroxyproline at position 12 (h12; p < 0.001). IgE binding to alanine-substituted peptides of a mimotope containing the DPY_h motif confirm the critical importance of Y (p < 0.01) and h (p < 0.01), but not D. Molecular modeling of the core and mimotope suggests an h-dependent conformational basis for the recognition of these sequences by polyclonal IgE. CONCLUSIONS: IgE from pooled sera and individual sera differentially bound amino acids throughout the sequences of Epitope 3 and its mimotope, with Y10 and h12 being most important for all sera. These results are highly significant for designing hypoallergenic forms of Ara h 2.

Engineering proteins with enhanced mechanical stability by force-specific sequence motifs.

Use of atomic force microscopy (AFM) has recently led to a better understanding of the molecular mechanisms of the unfolding process by mechanical forces; however, the rational design of novel proteins with specific mechanical strength remains challenging. We have approached this problem from a new perspective that generates linear physical-chemical properties (PCP) motifs from a limited AFM data set. Guided by our linear sequence analysis, we designed and analyzed four new mutants of the titin I1 domain with the goal of increasing the domain's mechanical strength. All four mutants could be cloned and expressed as soluble proteins. AFM data indicate that at least two of the mutants have increased molecular mechanical strength. This observation suggests that the PCP method is useful to graft sequences specific for high mechanical stability to weak proteins to increase their mechanical stability, and represents an additional tool in the design of novel proteins besides steered molecular dynamics calculations, coarse grained simulations, and ϕ-value analysis of the transition state.

Validation of a phage display and computational algorithm by mapping a conformational epitope of Bla g 2.

BACKGROUND: Bla g 2, one of the major cockroach allergens, induces a strong IgE response against conformational epitopes, and on reexposure, sensitized individuals often display symptoms of allergic rhinitis and asthma. The aim of the current study was to perform a test of the efficacy of a modified phage display screening, characterization of selected phages and an automated algorithm, EpiSearch, in locating an important conformational epitope. METHODS: The monoclonal antibody 7C11, which partially inhibits the binding of patient IgE antibodies to Bla g 2, was used to screen a random peptide phage library. After 3 rounds of panning, 32 phage clones were isolated and the amino acid sequences of their peptides were determined. The relative affinity and specificity of the binding of these peptides to 7C11 were tested in ELISAs. The amino acid composition of these peptides was then matched with clusters of residues on the surface of the 3-dimensional (3D) structure of Bla g 2, using our EpiSearch algorithm. RESULTS: The amino acid sequences of the peptides on selected phages differed at only one position, occupied by 1 of 2 negatively charged residues. The two 12-mer sequences bound to 7C11 with similar avidity and specificity. There was good concordance between the residues in the 3D clusters identified from our phage display/computational method with the co-crystal structural analysis. CONCLUSION: Conformational epitopes may be mapped through screening of clones from random peptide phage display libraries and EpiSearch.

Regional and temporal coordinated mutation patterns in SARS-CoV-2 spike protein revealed by a clustering and network analysis.

SARS-CoV-2 has steadily mutated during its spread to > 300 million people throughout the world. The WHO has designated strains with certain mutations, "variants of concern" (VOC), as they may have higher infectivity and/or resist neutralization by antibodies in sera of vaccinated individuals and convalescent patients. Methods to detect regionally emerging VOC are needed to guide treatment and vaccine design. Cluster and network analysis was applied to over 1.2 million sequences of the SARS-CoV-2 spike protein from 36 countries in the GISAID database. While some mutations rapidly spread throughout the world, regionally specific groups of variants were identified. Strains circulating in each country contained different sets of high frequency mutations, many of which were known VOCs. Mutations within clusters increased in frequency simultaneously. Low frequency, but highly correlated mutations detected by the method could signal emerging VOCs, especially if they occur at higher frequency in other regions. An automated version of our method to find high frequency mutations in a set of SARS-COV-2 spike sequences is available online at http://curie.utmb.edu/SAR.html .

Still SDAPing Along: 20 Years of the Structural Database of Allergenic Proteins.

The introduction of plant extracts to mitigate the symptoms of "hay fever", about a century ago, led to discoveries beginning sixty years ago on determining the sequences and eventually structures of allergenic proteins. As more proteins were cloned, there was a need to rapidly identify and categorize those with significant similarity to known allergens. The Structural Database of Allergenic Proteins (SDAP) was created at the beginning of the 21st century as the first cross-referenced website to allow rapid overview of the structures and sequences of allergenic proteins. SDAP provides a way to identify sequence and functional similarities between these proteins, despite the complex nomenclature system based on the Latin names of their different sources. A rapid FASTA search simplifies grouping allergens from the same structural or functional family. SDAP also provides an overview of the rapidly expanding literature on the sequence, structure and epitopes of allergenic proteins and a way to estimate the potential allergenicity of novel proteins based on rules provided by the IUIS. Twenty years and a pandemic later, the list of allergenic proteins and their attributes continues to grow. SDAP is expanding and improving to allow rapid access to all this information.

Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy.

About half of the US population is sensitized to one or more allergens, as found by a National Health and Nutrition Examination Survey (NHANES). The most common treatment for seasonal allergic responses is the daily use of oral antihistamines, which can control some of the symptoms, but are not effective for nasal congestion, and can be debilitating in many patients. Peptide immunotherapy is a promising new approach to treat allergic airway diseases. The small size of the immunogens cannot lead to an unwanted allergic reaction in sensitized patients, and the production of peptides with sufficient amounts for immunotherapy is time- and cost-effective. However, it is not known what peptides are the most effective for an immunotherapy of allergens. We previously produced a unique monoclonal antibody (mAb) E58, which can inhibit the binding of multiple groups of mAbs and human IgEs from patients affected by the major group 1 allergens of ragweed (Amb a 1) and conifer pollens (Jun a 1, Cup s 1, and Cry j 1). Here, we demonstrated that a combined approach, starting from two linear E58 epitopes of the tree pollen allergen Jun a 1 and the ragweed pollen allergen Amb a 1, and residue modifications suggested by molecular docking calculations and peptide design could identify a large number of high affinity binding peptides. We propose that this combined experimental and computational approach by structural analysis of linear IgE epitopes and peptide design, can lead to potential new candidates for peptide immunotherapy.

Synthetic proteins for COVID-19 diagnostics.

There is an urgent need for inexpensive, rapid and specific antigen-based assays to test for vaccine efficacy and detect infection with SARS-CoV-2 and its variants. We have identified a small, synthetic protein (JS7), representing a region of maximum variability within the receptor binding domain (RBD), which binds antibodies in sera from nine patients with PCR-verified COVID-19 of varying severity. Antibodies binding to either JS7 or the SARS-CoV-2 recombinant RBD, as well as those that disrupt binding between a fragment of the ACE2 receptor and the RBD, are proportional to disease severity and clinical outcome. Binding to JS7 was inhibited by linear peptides from the RBD interface with ACE2. Variants of JS7, such as E484K or N501Y, can be quickly synthesized in pure form in large quantities by automated methods. JS7 and related synthetic antigens can provide a basis for specific diagnostics for SARS-CoV-2 infections.

Structural analysis of linear and conformational epitopes of allergens.

In many countries regulatory agencies have adopted safety guidelines, based on bioinformatics rules from the WHO/FAO and EFSA recommendations, to prevent potentially allergenic novel foods or agricultural products from reaching consumers. We created the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/) to combine data that had previously been available only as flat files on Web pages or in the literature. SDAP was designed to be user friendly, to be of maximum use to regulatory agencies, clinicians, as well as to scientists interested in assessing the potential allergenic risk of a protein. We developed methods, unique to SDAP, to compare the physicochemical properties of discrete areas of allergenic proteins to known IgE epitopes. We developed a new similarity measure, the property distance (PD) value that can be used to detect related segments in allergens with clinical observed cross-reactivity. We have now expanded this work to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to known IgE epitopes. In complementary work we show how sequence motifs characteristic of allergenic proteins in protein families can be used as fingerprints for allergenicity.

Comprehensive 3D-modeling of allergenic proteins and amino acid composition of potential conformational IgE epitopes.

Similarities in sequences and 3D structures of allergenic proteins provide vital clues to identify clinically relevant immunoglobulin E (IgE) cross-reactivities. However, experimental 3D structures are available in the Protein Data Bank for only 5% (45/829) of all allergens catalogued in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP). Here, an automated procedure was used to prepare 3D-models of all allergens where there was no experimentally determined 3D structure or high identity (95%) to another protein of known 3D structure. After a final selection by quality criteria, 433 reliable 3D models were retained and are available from our SDAP Website. The new 3D models extensively enhance our knowledge of allergen structures. As an example of their use, experimentally derived "continuous IgE epitopes" were mapped on 3 experimentally determined structures and 13 of our 3D-models of allergenic proteins. Large portions of these continuous sequences are not entirely on the surface and therefore cannot interact with IgE or other proteins. Only the surface exposed residues are constituents of "conformational IgE epitopes" which are not in all cases continuous in sequence. The surface exposed parts of the experimental determined continuous IgE epitopes showed a distinct statistical distribution as compared to their presence in typical protein-protein interfaces. The amino acids Ala, Ser, Asn, Gly and particularly Lys have a high propensity to occur in IgE binding sites. The 3D-models will facilitate further analysis of the common properties of IgE binding sites of allergenic proteins.

InterProSurf: a web server for predicting interacting sites on protein surfaces.

UNLABELLED: A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. AVAILABILITY: The InterProSurf web server is available at http://curie.utmb.edu/

Distinguishing allergens from non-allergenic homologues using Physical-Chemical Property (PCP) motifs.

Quantitative guidelines to distinguish allergenic proteins from related, but non-allergenic ones are urgently needed for regulatory agencies, biotech companies and physicians. In a previous study, we found that allergenic proteins populate a relatively small number of protein families, as characterized by the Pfam database. However, these families also contain non-allergenic proteins, meaning that allergenic determinants must lie within more discrete regions of the sequence. Thus, new methods are needed to discriminate allergenic proteins within those families. Physical-Chemical Properties (PCP)-motifs specific for allergens within a Pfam class were determined for 17 highly populated protein domains. A novel scoring method based on PCP-motifs that characterize known allergenic proteins within these families was developed, and validated for those domains. The motif scores distinguished sequences of allergens from a large selection of 80,000 randomly selected non-allergenic sequences. The motif scores for the birch pollen allergen (Bet v 1) family, which also contains related fruit and nut allergens, correlated better than global sequence similarities with clinically observed cross-reactivities among those allergens. Further, we demonstrated that the average scores of allergen specific motifs for allergenic profilins are significantly different from the scores of non-allergenic profilins. Several of the selective motifs coincide with experimentally determined IgE epitopes of allergenic profilins. The motifs also discriminated allergenic pectate lyases, including Jun a 1 from mountain cedar pollen, from similar proteins in the human microbiome, which can be assumed to be non-allergens. The latter lacked key motifs characteristic of the known allergens, some of which correlate with known IgE binding sites.

Functional classification of protein toxins as a basis for bioinformatic screening.

Proteins are fundamental to life and exhibit a wide diversity of activities, some of which are toxic. Therefore, assessing whether a specific protein is safe for consumption in foods and feeds is critical. Simple BLAST searches may reveal homology to a known toxin, when in fact the protein may pose no real danger. Another challenge to answer this question is the lack of curated databases with a representative set of experimentally validated toxins. Here we have systematically analyzed over 10,000 manually curated toxin sequences using sequence clustering, network analysis, and protein domain classification. We also developed a functional sequence signature method to distinguish toxic from non-toxic proteins. The current database, combined with motif analysis, can be used by researchers and regulators in a hazard screening capacity to assess the potential of a protein to be toxic at early stages of development. Identifying key signatures of toxicity can also aid in redesigning proteins, so as to maintain their desirable functions while reducing the risk of potential health hazards.