Bioinformatics approaches to classifying allergens and predicting cross-reactivity

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.

Immunoinformatics Strategy to Develop a Novel Universal Multiple Epitope-Based COVID-19 Vaccine

Currently available COVID vaccines are effective in reducing mortality and severity but do not prevent transmission of the virus or reinfection by the emerging SARS-CoV-2 variants. There is an obvious need for better and longer-lasting effective vaccines for various prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of a broad-spectrum vaccine that can be used to prevent infection by reducing both the transmission rate and re-infection. During the initial phases of SARS-CoV-2 infection, the nucleocapsid (N) protein is one of the most abundantly expressed proteins. Additionally, it has been identified as the most immunogenic protein of SARS-CoV-2. In this study, state-of-the-art bioinformatics techniques have been exploited to design novel multiple epitope vaccines using conserved regions of N proteins from prevalent strains of SARS-CoV-2 for the prediction of B- and T-cell epitopes. These epitopes were sorted based on their immunogenicity, antigenicity score, and toxicity. The most effective multi-epitope construct with possible immunogenic properties was created using epitope combinations. EAAAK, AAY, and GPGPG were used as linkers to connect epitopes. The developed vaccines have shown positive results in terms of overall population coverage and stimulation of the immune response. Potential expression of the chimeric protein construct was detected after it was cloned into the Pet28a/Cas9-cys vector for expression screening in Escherichia coli. The developed vaccine performed well in computer-based immune response simulation and covered a diverse allelic population worldwide. These computational findings are very encouraging for the further testing of our candidate vaccine, which could eventually aid in the control and prevention of SARS-CoV-2 infections globally.

In silico tools to assess the potential allergenicity of shiitake mushrooms (Lentinula edodes)

BACKGROUND

Computational tools may have an edge over conventional methods for the preliminary evaluation of food allergenicity. In this study, the allergenic potential of Lentinula edodes was evaluated and validated using in silico tools.

RESULTS

The potential cross-reactivity of mushroom proteins with fungal allergens was determined using sequence alignment - the Fast Alignment (FASTA) and Basic Local Alignment Search Tool (BLAST) algorithm. Eight L. edodes proteins were cross-reactive with allergens from fungal origin, showing 52%-89% sequence identity using FASTA algorithm-based alignment. The BLAST data were corroborated by percentage identity and query coverage. Physico-chemical property-based allergenicity was deciphered by AlgPred, Allermatch, and AllergenFP software, which predicted six out of eight proteins as potential allergens. Sequence alignment showed 66%-86% conservancy between mushroom protein and known fungal allergens. Secondary structure and amino acid composition supported structural affinity between query and fungal proteins. Three-dimensional structures of five mushroom proteins were generated, quality assessed, and superimposed with fungal allergens, suggesting possible allergenicity of mushroom proteins. An enzyme-linked immunosorbent assay (ELISA) demonstrated immunoglobulin E (IgE) binding in 13 out of 21 food-hypersensitive patients' sera.

CONCLUSION

In silico tools provide preliminary indications about the potential allergenicity and cross-reactivity of mushroom proteins. This approach may be used for the prelusive allergenicity assessment of allergen sources. © 2022 Society of Chemical Industry.

IgE epitopes of Ara h 9, Jug r 3, and Pru p 3 in peanut-allergic individuals from Spain and the US

Non-specific lipid transfer proteins (LTPs) are well studied allergens that can lead to severe reactions, but often cause oral allergy syndrome in the Mediterranean area and other European countries. However, studies focused on LTP reactivity in allergic individuals from the United States are lacking because they are not considered major allergens. The goal of this study is to determine if differences in immunoglobulin (Ig) E binding patterns to the peanut allergen Ara h 9 and two homologous LTPs (walnut Jug r 3 and peach Pru p 3) between the US and Spain contribute to differences observed in allergic reactivity. Synthetic overlapping 15-amino acid-long peptides offset by five amino acids from Ara h 9, Jug r 3, and Pru p 3 were synthesized, and the intact proteins were attached to microarray slides. Sera from 55 peanut-allergic individuals from the US were tested for IgE binding to the linear peptides and IgE binding to intact proteins using immunofluorescence. For comparison, sera from 17 peanut-allergic individuals from Spain were also tested. Similar IgE binding profiles for Ara h 9, Jug r 3, and Pru p 3 were identified between the US and Spain, with slight differences. Certain regions of the proteins, specifically helices 1 and 2 and the C-terminal coil, were recognized by the majority of the sera more often than other regions of the proteins. While serum IgE from peanut-allergic individuals in the US binds to peptides of Ara h 9 and its homologs, only IgE from the Spanish subjects bound to the intact LTPs. This study identifies Ara h 9, Jug r 3, and Pru p 3 linear epitopes that were previously unidentified using sera from peanut-allergic individuals from the US and Spain. Certain regions of the LTPs are recognized more often in US subjects, indicating that they represent conserved and possible cross-reactive regions. The location of the epitopes in 3D structure models of the LTPs may predict the location of potential conformational epitopes bound by a majority of the Spanish patient sera. These findings are potentially important for development of peptide or protein-targeting diagnostic and therapeutic tools for food allergy.

DGraph Clusters Flaviviruses and β-Coronaviruses According to Their Hosts, Disease Type, and Human Cell Receptors

Motivation

There is a need for rapid and easy-to-use, alignment-free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is an application developed to generate 2-dimensional (2D) maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such "PD-graphs" show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.

Results

Property distance graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors, and disease phenotypes. Property distance graphs separate the tick- from the mosquito-borne FV, cluster viruses that infect bats, camels, seabirds, and humans separately. The clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Middle East respiratory syndrome (MERS) sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

Insights into Peptide Mediated Antibiofilm Treatment in Chronic Wound: A Bench to Bedside Approach

Chronic wound biofilm infections are a threat to the population with respect to morbidity and mortality. The presence of multidrug-resistant bacterial pathogens in chronic wound renders the action of antibiotics and antibiofilm agents difficult. Therefore an alternative therapy is essential for reducing bacterial biofilm burden. In this scenario, the peptide-based antibiofilm therapy for chronic wound biofilm management seeks more attention. A synthetic peptide with a broad range of antibiofilm activity against preformed and established biofilms, having the ability to kill multispecies bacteria within biofilms and possessing combinatorial activity with other antimicrobial agents, provides significant insights. In this review, we portray the possibilities and difficulties of peptide-mediated treatment in chronic wounds biofilm management and how it can be clinically translated into a product.

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.

D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

Motivation:

There is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such “PD-graphs” show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.

Results:

PD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

Epitopes with similar physicochemical properties contribute to cross reactivity between peanut and tree nuts

Many individuals with peanut (PN) allergy have severe reactions to tree nuts (TN) such as walnuts or cashews. Although allergenic proteins in TN and PN have overall low identity, they share discrete sequences similar in physicochemical properties (PCP) to known IgE epitopes. Here, PCP-consensus peptides (cp, 13 aa and 31 aa) were identified from an alignment of epitope rich regions of walnut vicilin, Jug r 2, leader sequence (J2LS) and cross-reactive epitopes in the 2S albumins of peanut and synthesized. A peptide similarity search in the Structural Database of Allergenic Proteins (SDAP) revealed a network of peptides similar (low property distance, PD) to the 13 aa cp (13cp) in many different plant allergens. Peptides similar to the 13cp in PN and TN allergens bound IgE from sera of patients allergic to PN and TN in peptide microarray analysis. The 13cp was used to produce a rabbit consensus peptide antibody (cpAB) that detected proteins containing repeats similar to the 13cp in western blots of various nut extracts, in which reactive proteins were identified by mass spectrometry. The cpAB bound more specifically to allergens and nut extracts containing multiple repeats similar to the 13 cp, such as almond (Pru du 6), peanut (Ara h 2) and walnut (Jug r 2). IgE binding to various nut extracts is inhibited by recombinant J2LS sequence and synthetic 31cp. Thus, several repeated sequences similar to the 13cp are bound by IgE. Multiple similar repeats in several allergens could account for reaction severity and clinically relevant cross-reactivity to PN and TN. These findings may help improve detection, diagnostic, and therapeutic tools.

Development and Challenges of Antimicrobial Peptides for Therapeutic Applications

More than 3000 antimicrobial peptides (AMPs) have been discovered, seven of which have been approved by the U.S. Food and Drug Administration (FDA). Now commercialized, these seven peptides have mostly been utilized for topical medications, though some have been injected into the body to treat severe bacterial infections. To understand the translational potential for AMPs, we analyzed FDA-approved drugs in the FDA drug database. We examined their physicochemical properties, secondary structures, and mechanisms of action, and compared them with the peptides in the AMP database. All FDA-approved AMPs were discovered in Gram-positive soil bacteria, and 98% of known AMPs also come from natural sources (skin secretions of frogs and toxins from different species). However, AMPs can have undesirable properties as drugs, including instability and toxicity. Thus, the design and construction of effective AMPs require an understanding of the mechanisms of known peptides and their effects on the human body. This review provides an overview to guide the development of AMPs that can potentially be used as antimicrobial drugs.

Progress in the Analysis of Food Allergens through Molecular Biology Approaches

Food allergies associated with class E immunoglobulins (IgE) are a serious health problem that affects between 1% and 10% of the population of developing countries, with a variability that depends on the geographical area and age range considered. These allergies are caused by a cross-link reaction between a specific food protein (the allergen) and the host IgE. Allergic reactions can range from mild itching to anaphylactic shock and there are no clues to predict the effects of an allergen. Strict avoidance of allergenic food is the only way to avoid possible serious allergic reactions. In the last 30 years a growing number of molecular studies have been conducted to obtain information on the diffusion of food allergens and to establish the structural basis of their allergenicity. At the same time, these studies have also allowed the development of molecular tools (mainly based on synthetic peptides and recombinant allergens) that can be of great help for diagnostic and therapeutic approaches of food allergies. Accordingly, this review focuses on advances in the study of food allergens made possible by molecular technologies and how results and technologies can be integrated for the development of a systematic food molecular allergology. The review may be of interest both to scientists approaching this field of investigation and to physicians who wish to have an update on the progress of research in diagnosis and therapy of food allergies.

T cell epitopes of Per a 10 modulate local-systemic immune responses and airway inflammation by augmenting Th1 and T regulatory cell functions in murine model

Peptide immunotherapy (PIT) represents a safe and efficacious therapeutic modality for allergic diseases. Present study evaluates immunotherapeutic potential of T cell peptides of major cockroach allergen, Per a 10 in murine model of airway allergy. Treatment with peptides T-P8 and T-P10 demonstrated maximal resolution of pathophysiological features such as reduced recruitment of inflammatory cells to airways, lowered specific IgE, induction of IgG2a antibodies in serum, immune deviation towards Th1 cytokine milieu, suppression of Th2 cytokines in BALF and splenocyte culture supernatant and resolution of lung inflammation. A significant increase in CD4⁺Foxp3⁺ cells in spleen indicate towards induction of T regulatory cell mediated peripheral tolerance characterized by shift in cytokine milieu from Th2 to T regulatory cytokines. PIT modulates regulation of immune responses at both local and systemic levels, contributes towards holistic improvement in allergic features in mice and thus demonstrate potential for safe, specific and efficacious treatment for cockroach allergy.

A robust method for the estimation and visualization of IgE cross-reactivity likelihood between allergens belonging to the same protein family

Among the vast number of identified protein families, allergens emanate from relatively few families which translates to only a small fraction of identified protein families. In allergy diagnostics and immunotherapy, interactions between immunoglobulin E and allergens are crucial because the formation of an allergen-antibody complex is necessary for triggering an allergic reaction. In allergic diseases, there is a phenomenon known as cross-reactivity. Cross-reactivity describes a situation where an individual has produced antibodies against a particular allergenic protein, but said antibodies fail to discriminate between the original sensitizer and other similar proteins that usually belong to the same family. To expound the concept of cross-reactivity, this study examines ten protein families that include allergens selected specifically for the analysis of cross-reactivity. The selected allergen families had at least 13 representative proteins, overall folds that differ significantly between families, and include relevant allergens with various potencies. The selected allergens were analyzed using information on sequence similarities and identities between members of the families as well as reports on clinically relevant cross-reactivities. Based on our analysis, we propose to introduce a new A-RISC index (Allergens’–Relative Identity, Similarity and Cross-reactivity) which describes homology between two allergens belonging to the same protein family and is used to predict the likelihood of cross-reactivity between them. Information on sequence similarities and identities, as well as on the values of the proposed A-RISC index is used to introduce four categories describing a risk of a cross-reactive reaction, namely: high, medium-high, medium-low and low. The proposed approach can facilitate analysis in component-resolved allergy diagnostics, generation of avoidance guidelines for allergic individuals, and help with the design of immunotherapy.

Tree nut allergies: Allergen homology, cross-reactivity, and implications for therapy

Tree nut allergy is a potentially life-threatening disease that is increasing in prevalence, now affecting 1% of the general population in the United States. While other food allergies often resolve spontaneously, tree nut allergies are outgrown in less than 10% of cases. Due to the likelihood of cross-sensitization to multiple tree nut allergens, the current treatment guideline is strict avoidance of all nuts once one tree nut allergy has been diagnosed. For example, walnut and pecan are highly cross-reactive, along with cashew and pistachio, but the extent of clinical, IgE-mediated cross-reactivity among other tree nuts remains unclear, therefore making avoidance of all tree nuts a safe approach. There have been recent advances in immunotherapy for food allergies. For instance, there are investigational immunotherapies for milk, egg and peanut allergies, specifically oral immunotherapy, sublingual immunotherapy and epicutaneous immunotherapy. However, there are no large randomized controlled clinical trials for tree nut allergies. Even though there has been less research into tree nut allergy immunotherapies, the evidence of T-cell cross-reactivity among tree nuts exists in animal models and in T cells from allergic patients indicates that immunotherapeutic interventions may be possible. Here, we review the literature regarding epidemiology, allergen homology and cross-reactivity among tree nuts, and explore how current findings can be employed for effective therapy.

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.

Antibacterial Peptides in Dermatology-Strategies for Evaluation of Allergic Potential

During recent decades, the market for peptide-based drugs, including antimicrobial peptides, has vastly extended and evolved. These drugs can be useful in treatment of various types of disorders, e.g., cancer, autoimmune diseases, infections, and non-healing wounds. Although peptides are less immunogenic than other biologic therapeutics, they can still induce immune responses and cause allergies. It is important to evaluate the immunogenic and allergic potential of peptides before they are forwarded to the expensive stages of clinical trials. The process of the evaluation of immunogenicity and cytotoxicity is complicated, as in vitro models and bioinformatics tools cannot fully simulate situations in the clinic. Nevertheless, several potentially promising tests for the preclinical evaluation of peptide drugs have been implemented (e.g., cytotoxicity assays, the basophil activation test, and lymphocyte activation assays). In this review, we focus on strategies for evaluation of the allergic potential of peptide-based therapeutics.

Approaches to assess IgE mediated allergy risks (sensitization and cross-reactivity) from new or modified dietary proteins

The development and introduction of new dietary protein sources has the potential to improve food supply sustainability. Understanding the potential allergenicity of these new or modified proteins is crucial to ensure protection of public health. Exposure to new proteins may result in de novo sensitization, with or without clinical allergy, or clinical reactions through cross-reactivity. In this paper we review the potential of current methodologies (in silico, in vitro degradation, in vitro IgE binding, animal models and clinical studies) to address these outcomes for risk assessment purposes for new proteins, and especially to identify and characterise the risk of sensitization for IgE mediated allergy from oral exposure. Existing tools and tests are capable of assessing potential crossreactivity. However, there are few possibilities to assess the hazard due to de novo sensitization. The only methods available are in vivo models, but many limitations exist to use them for assessing risk. We conclude that there is a need to understand which criteria adequately define allergenicity for risk assessment purposes, and from these criteria develop a more suitable battery of tests to distinguish between proteins of high and low allergenicity, which can then be applied to assess new proteins with unknown risks.

Contribution of Leptosphaeria species ascospores to autumn asthma in areas of oilseed rape production

BACKGROUND

An increase in the number of hospital admissions from September to November in the northern hemisphere has been frequently reported. At this time, some species of fungal genus Leptosphaeria produce numerous ascospores, which are easily airborne. However, we lack knowledge about whether Leptosphaeria produces allergenic proteins.

OBJECTIVE

To evaluate the potential of Leptosphaeria ascospores to contribute to autumn asthma.

METHODS

Detailed bioinformatic analysis of proteins produced by Leptosphaeria maculans available in databases was performed and the data compared with allergens found in other airborne fungi. The concentrations of Leptosphaeria ascospores detected at 2 sites were compared to these obtained in other environments worldwide.

RESULTS

We found that Leptosphaeria species produce proteins with a high identity to commonly known aeroallergens of several well-characterized molds. The level of amino acid identity significantly exceeded the allergen identity thresholds recommended by the Food and Agricultural Organization/World Health Organization (35%), which indicates allergenic properties of L maculans and ensures the same properties in the other Leptosphaeria species.

CONCLUSION

High concentrations of Leptosphaeria species ascospores in the autumn and postulated allergenicity of their proteins strongly suggest that this genus contributes to worldwide reported autumn asthma. The finding opens the question of allergenicity of the other never studied fungal species present in aeroplankton.

Common Amino Acid Subsequences in a Universal Proteome--Relevance for Food Science

A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept.

In vitro assessment of allergenicity features and localization of probable IgE binding regions

Rice is cultivated as a staple grain crop in many countries, especially in Asia. In the present study, recombinant rice chitinase was expressed, purified and characterized by in silico and immunobiochemical methods. Rice chitinase was affinity purified and it resolved at 24 kDa on SDS-PAGE. Purified protein was analyzed for pepsin resistance, heat stability, and IgE binding using atopic patients' sera. Chitinase was resistant to pepsin digestion and heat treatment at 90 °C for 1 h. It showed significant IgE binding with 7 of 110 patients' sera positive to different food allergens. Homology modeled 3D structure of rice chitinase was used for B cell epitope prediction. In silico predicted B cell peptides were assessed for IgE binding by ELISA using food allergic patients' sera, epitope RC2 showed IgE binding comparable to chitinase. In conclusion, chitinase was identified as a potential allergen and may share cross reactive epitopes with food allergens.

Genome-wide analysis of potential cross-reactive endogenous allergens in rice (Oryza sativa L.)

The proteins in the food are the source of common allergic components to certain patients. Current lists of plant endogenous allergens were based on the medical/clinical reports as well as laboratory results. Plant genome sequences made it possible to predict and characterize the genome-wide of putative endogenous allergens in rice (Oryza sativa L.). In this work, we identified and characterized 122 candidate rice allergens including the 22 allergens in present databases. Conserved domain analysis also revealed 37 domains among rice allergens including one novel domain (histidine kinase-, DNA gyrase B-, and HSP90-like ATPase, PF13589) adding to the allergen protein database. Phylogenetic analysis of the allergens revealed the diversity among the Prolamin superfamily and DnaK protein family, respectively. Additionally, some allergens proteins clustered on the rice chromosome might suggest the molecular function during the evolution.

Allergen cross-reactivity in allergic rhinitis and oral-allergy syndrome: a bioinformatic protein sequence analysis

BACKGROUND

Clinical allergy cross-reactivity that is seen with related inhalant allergens or between unrelated inhalant allergens and foods in oral allergy syndrome (OAS) remains poorly understood. The goal of this study is to determine whether clinical cross-reactivity can be identified from primary protein sequences in allergy epitopes and food proteins.

METHODS

High-throughput analysis was performed by assembling all known allergy epitopes within the Immune Epitope Database (IEDB; http://www.iedb.org) for 5 common species from 5 inhalant allergen subclasses and comparing their protein sequences to each other, as well as to sequences of intact proteins from known cross-reactive foods in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) protein database (http://www.uniprot.org) that have been implicated in OAS. Computational methods were employed to allow for exact matching, gaps, and similar amino acids using multiple algorithms. A phylogenetic tree was created to determine evolutionary relationships between cross-reactive epitopes in OAS.

RESULTS

Twenty-three common inhalant allergens had 4429 unique epitopes; the 19 foods implicated in OAS had 9497 protein sequences. The Basic Local Alignment Search Tool (BLAST) algorithm identified interclass and intraclass sequence similarities for the 5 inhalant allergy classes with high similarity for mites, grasses, and trees. Analysis of OAS proteins identified 104 matches to inhalant allergy epitopes that are known to cross-react. The phylogenetic tree displayed relationships that mostly followed organism phylogeny.

CONCLUSION

Use of primary protein sequences was successful in explaining clinical allergy cross-reactivity. Clinical correlation is needed for use of these epitopes as diagnostic or therapeutic entities for patients with cross-reactive allergic disease.

Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach

BACKGROUND

Allergy has become a key cause of morbidity worldwide. Although many legumes (plants in the Fabaceae family) are healthy foods, they may have a number of allergenic proteins. A number of allergens have been identified and characterized in Fabaceae family, such as soybean and peanut, on the basis of biochemical and molecular biological approaches. However, our understanding of the allergens from chickpea (Cicer arietinum L.), belonging to this family, is very limited.

OBJECTIVE

In this study, we aimed to identify putative and cross-reactive allergens from Chickpea (C. arietinum) by means of in silico analysis of the chickpea protein sequences and allergens sequences from Fabaceae family.

METHODS

We retrieved known allergen sequences in Fabaceae family from the IUIS Allergen Nomenclature Database. We performed a protein BLAST (BLASTp) on these sequences to retrieve the similar sequences from chickpea. We further analyzed the retrieved chickpea sequences using a combination of in silico tools, to assess them for their allergenicity potential. Following this, we built structure models using FUGUE: Sequence-structure homology; these models generated by the recognition tool were viewed in Swiss-PDB viewer.

RESULTS

Through this in silico approach, we identified seven novel putative allergens from chickpea proteome sequences on the basis of similarity of sequence, structure and physicochemical properties with the known reported legume allergens. Four out of seven putative allergens may also show cross reactivity with reported allergens since potential allergens had common sequence and structural features with the reported allergens.

CONCLUSION

The in silico proteomic identification of the allergen proteins in chickpea provides a basis for future research on developing hypoallergenic foods containing chickpea. Such bioinformatics approaches, combined with experimental methodology, will help delineate an efficient and comprehensive approach to assess allergenicity and pave the way for a better understanding of the biological and medical basis of the same.

Evaluating the in vivo Th2 priming potential among common allergens

Exposure to allergens, both man-made and from our environment is increasingly associated with the development of significant human health issues such as allergy and asthma. Allergen induced production of the cytokine interleukin (IL-)4 by Th2 cells is central to the pathogenesis of allergic disease (Gavett et al., 1994). The development of the G4 mouse, that expresses green fluorescent protein (GFP) as a surrogate for IL-4 protein expression has made it possible to directly track the immune cells that produce IL-4. By combining a reliable intradermal immunisation technique with the transgenic G4 mouse we have been able to develop a novel & unique in vivo primary Th2 immune response model (PTh2). When allergens relevant to human disease are evaluated using the PTh2 assay a dose dependent hierarchy of allergenicity is revealed with environmental allergens (cockroach, house dust mite) the most potent and food allergens being the least. In addition, the PTh2 assay is extremely sensitive to the immunoregulatory effects of Mycobacterial extracts and immunosuppressive drugs on primary Th2 cell development. Taken together, this assay provides a standardised method for the identification of the structural and functional properties of proteins relevant to allergenicity, and is a powerful screening tool for novel lead compounds that are effective at inhibiting the primary Th2 response in allergic diseases.

Assessment of 3D models for allergen research

Allergenic proteins must crosslink specific IgE molecules, bound to the surface of mast cells and basophils, to stimulate an immune response. A structural understanding of the allergen-IgE interface is needed to predict cross-reactivities between allergens and to design hypoallergenic proteins. However, there are less than 90 experimentally determined structures available for the approximately 1500 sequences of allergens and isoallergens cataloged in the Structural Database of Allergenic Proteins. To provide reliable structural data for the remaining proteins, we previously produced more than 500 3D models using an automated procedure, with strict controls on template choice and model quality evaluation. Here, we assessed how well the fold and residue surface exposure of 10 of these models correlated with recently published experimental 3D structures determined by X-ray crystallography or NMR. We also discuss the impact of intrinsically disordered regions on the structural comparison and epitope prediction. Overall, for seven allergens with sequence identities to the original templates higher than 27%, the backbone root-mean square deviations were less than 2 Å between the models and the subsequently determined experimental structures for the ordered regions. Further, the surface exposure of the known IgE epitopes on the models of three major allergens, from peanut (Ara h 1), latex (Hev b 2), and soy (Gly m 4), was very similar to the experimentally determined structures. For the three remaining allergens with lower sequence identities to the modeling templates, the 3D folds were correctly identified. However, the accuracy of those models is not sufficient for a reliable epitope mapping.

Group 10 allergens (tropomyosins) from house-dust mites may cause covariation of sensitization to allergens from other invertebrates

Group 10 allergens (tropomyosins) have been assumed to be a major cause of cross-reactivity between house-dust mites (HDMs) and other invertebrates. Despite all of the published data regarding the epidemiology, percent IgE binding and level of sensitization in the population, the role of tropomyosin as a cross-reactive allergen in patients with multiple allergy syndrome still remains to be elucidated. Homology between amino acid sequences reported in allergen databases of selected invertebrate tropomyosins was determined with Der f 10 as the reference allergen. The 66.9 and 54.4% identities were found with selected crustacean and insect species, respectively, whereas only 20.4% identity was seen with mollusks. A similar analysis was performed using reported B-cell IgE-binding epitopes from Met e1 (shrimp allergen) and Bla g7 (cockroach allergen) with other invertebrate tropomyosins. The percent identity in linear sequences was higher than 35% in mites, crustaceans, and cockroaches. The polar and hydrophobic regions in these groups were highly conserved. These findings suggest that tropomyosin may be a major cause of covariation of sensitization between HDMs, crustaceans, and some species of insects and mollusks.

Modeling of allergen proteins found in sea food products

Shellfish are a source of food allergens, and their consumption is the cause of severe allergic reactions in humans. Tropomyosins, a family of muscle proteins, have been identified as the major allergens in shellfish and mollusks species. Nevertheless, few experimentally determined three-dimensional structures are available in the Protein Data Base (PDB). In this study, 3D models of several homologous of tropomyosins present in marine shellfish and mollusk species (Chaf 1, Met e1, Hom a1, Per v1, and Pen a1) were constructed, validated, and their immunoglobulin E binding epitopes were identified using bioinformatics tools. All protein models for these allergens consisted of long alpha-helices. Chaf 1, Met e1, and Hom a1 had six conserved regions with sequence similarities to known epitopes, whereas Per v1 and Pen a1 contained only one. Lipophilic potentials of identified epitopes revealed a high propensity of hydrophobic amino acids in the immunoglobulin E binding site. This information could be useful to design tropomyosin-specific immunotherapy for sea food allergies.

In silico assessment of the potential allergenicity of transgenes used for the development of GM food crops

Genetically modified (GM) crops require allergenicity and toxicity assessment of the novel protein(s) to ensure complete safety to the consumers. These assessments are performed in accordance with the guidelines proposed by Codex (2003) and ICMR (2008). The guidelines recommend sequence homology analysis as a preliminary step towards allergenicity prediction, later in vitro experiments may be performed to confirm allergenicity. In the present study, an in silico approach is employed to evaluate the allergenic potential of six transgenes routinely used for the development of GM food crops. Among the genes studied, manganese superoxide dismutase (MnSOD) and osmotin shares greater than 90% identity with Hev b 10 and Cap a 1w, respectively. Chitinase shares greater than 70% identity with allergens namely Pers a 1 and Hev b 11, and fungal chitinase showed significant IgE binding with 7 of 75 patients' sera positive to different food extracts. Glucanases (alfalfa, wheat) and glycine betaine aldehyde dehydrogenase gene share 50% homology with allergens like - Ole e 9, Cla h 10 and Alt a 10. The results demonstrate the allergenic potential of six genes and can serve as a guide for selection of transgenes to develop GM crops.

Cross-reactions vs co-sensitization evaluated by in silico motifs and in vitro IgE microarray testing

BACKGROUND AND OBJECTIVE

Using an in silico allergen clustering method, we have recently shown that allergen extracts are highly cross-reactive. Here we used serological data from a multi-array IgE test based on recombinant or highly purified natural allergens to evaluate whether co-reactions are true cross-reactions or co-sensitizations by allergens with the same motifs.

METHODS

The serum database consisted of 3142 samples, each tested against 103 highly purified natural or recombinant allergens. Cross-reactivity was predicted by an iterative motif-finding algorithm through sequence motifs identified in 2708 known allergens.

RESULTS

Allergen proteins containing the same motifs cross-reacted as predicted. However, proteins with identical motifs revealed a hierarchy in the degree of cross-reaction: The more frequent an allergen was positive in the allergic population, the less frequently it was cross-reacting and vice versa. Co-sensitization was analyzed by splitting the dataset into patient groups that were most likely sensitized through geographical occurrence of allergens. Interestingly, most co-reactions are cross-reactions but not co-sensitizations.

CONCLUSIONS

The observed hierarchy of cross-reactivity may play an important role for the future management of allergic diseases.

In silico homology modeling to predict functional properties of cruciferin

Cruciferin is the major storage protein in Brassicaceae family oilseeds. The predominant cruciferin isoforms in Arabidopsis thaliana were investigated using homology modeling (HM) for their molecular structures and functional properties. The structure of Brassica napus procruciferin was used as the template for HM to determine the molecular structures and hypervariable regions. Hydrophobicity and electrostatic surface potential distribution on the intradisulfide-containing face (IA) and the interdisulfide-containing face (IE) indicated favorable interfacial and solubility properties. More heat-induced structural changes were predicted for the CruC homotrimer than for the CruA or CruB homotrimers. Structural features that facilitate flavor binding and limit proteolytic digestion were more readily observed in CruA and CruB than in CruC. On the basis of these comparative models, structural differences among cruciferin isoforms and their relevance to potential technofunctionalities were identified. This approach of functional property prediction will link protein structure to utilities and will be valuable in designing proteins for targeted applications.

Computationally predicted IgE epitopes of walnut allergens contribute to cross-reactivity with peanuts

BACKGROUND

Cross-reactivity between peanuts and tree nuts implies that similar immunoglobulin E (IgE) epitopes are present in their proteins.

OBJECTIVE

To determine whether walnut sequences similar to known peanut IgE-binding sequences, according to the property distance (PD) scale implemented in the Structural Database of Allergenic Proteins, react with IgE from sera of patients with allergy to walnut and/or peanut.

METHODS

  Patient sera were characterized by western blotting for IgE binding to nut protein extracts and to peptides from walnut and peanut allergens, similar to known peanut epitopes as defined by low PD values, synthesized on membranes. Competitive enzyme-linked immunosorbent assay (ELISA) was used to show that peanut and predicted walnut epitope sequences compete with purified Ara h 2 for binding to IgE in serum from a cross-reactive patient.

RESULTS

Sequences from the vicilin walnut allergen Jug r 2, which had low PD values to epitopes of the peanut allergen Ara h 2, a 2S albumin, bound to IgE in sera from five patients who reacted to either walnut or peanut or both. A walnut epitope recognized by sera from six patients mapped to a surface-exposed region on a model of the N-terminal pro-region of Jug r 2. This predicted walnut epitope competed for IgE binding to Ara h 2 in serum as well as the known IgE epitope from Ara h 2.

CONCLUSIONS

Sequences with low PD value (< 8.5) to known IgE epitopes could contribute to cross-reactivity between allergens. This further validates the PD scoring method for predicting cross-reactive epitopes in allergens.

MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.

AllerML: markup language for allergens

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis.

An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

Recent progress in the biochemical classification and structural determination of allergens and allergen-antibody complexes has enhanced our understanding of the molecular determinants of allergenicity. Databases of allergens and their epitopes have facilitated the clustering of allergens according to their sequences and, more recently, their structures. Groups of similar sequences are identified for allergenic proteins from diverse sources, and all allergens are classified into a limited number of protein structural families. A gallery of experimental structures selected from the protein classes with the largest number of allergens demonstrate the structural diversity of the allergen universe. Further comparison of these structures and identification of areas that are different from innocuous proteins within the same protein family can be used to identify features specific to known allergens. Experimental and computational results related to the determination of IgE binding surfaces and methods to define allergen-specific motifs are highlighted.

Clinical data mining and research in the allergy office

PURPOSE OF REVIEW

More data are anticipated from the expected increase in use of electronic health records (EHRs). Upcoming initiatives require reporting of quality measures, meaningful use of clinical decision support, alert systems, and pharmacovigilance - knowledge resulting through use of EHRs. Data mining is a new tool that will help us manage information and derive knowledge from these data, and is a part of evolving new disciplines of informatics and knowledge management.

RECENT FINDINGS

Studies are reported from smaller clinic data marts to larger repositories and warehouses in various health systems, biomedical registries, and the medical literature on the Internet. Data mining technologies show promise and challenges. Outcome measures as structured data and narrative text can be mined with human assistance and newer automated natural language processing software. Despite advances, the growing diversity of clinic EHRs lack integration and interoperability with Internet-based biomedical databases.

SUMMARY

Allergists have the capability to mine clinic EHRs to discover new information, which may be hidden in charts. A central allergy computer can serve not just as a registry but also allows functionalities to enable EHRs' meaningful use. Harmonization of technological and organizational standards will allow seamless use of new natural language processing (NLP) tools and ontologies through a semantic web.

Evaluation of molecular basis of cross reactivity between rye and Bermuda grass pollen allergens

BACKGROUND

Allergenic cross reactivity between the members of the Pooids (Lolium perenne, Phleum pratense, and Poa pratensis) and Chloridoids (Cynodon dactylon and Paspalum notatum) is well established. Studies using crude extracts in the past have demonstrated limited cross reactivity between the Pooids and the Chloridoids suggesting separate diagnosis and therapy. However, little is known regarding the molecular basis for the limited cross reactivity observed between the 2 groups of grasses. The present study was undertaken to gain insights into the molecular basis of cross allergenicity between the major allergens from rye and Bermuda grass pollens.

METHODS

Immunoblot inhibition tests were carried out to determine the specificity of the proteins involved in cross reactivity. Crude pollen extract and bacterially expressed and purified recombinant Lol p 1and Lol p 5 from rye grass were subjected to cross inhibition experiments with crude and purified recombinant Cyn d 1 from Bermuda grass using sera from patients allergic to rye grass pollen.

RESULTS

The immunoblot inhibition studies revealed a high degree of cross inhibition between the group 1 allergens. In contrast, a complete lack of inhibition was observed between Bermuda grass group 1 allergen rCyn d 1, and rye grass group 5 allergen rLol p 5. Crude rye grass extract strongly inhibited IgE reactivity to Bermuda grass, whereas crude Bermuda grass pollen extract showed a weaker inhibition.

CONCLUSIONS

Our data suggests that a possible explanation for the limited cross reactivity between the Pooids and Chloridoids may, in part, be due to the absence of group 5 allergen from Chloridoid grasses. This approach of using purified proteins may be applied to better characterize the cross allergenicity patterns between different grass pollen allergens.

Graph-theoretical comparison of protein surfaces reveals potential determinants of cross-reactivity and the molecular mimicry

Different proteins, even without sequence similarity, still can contain similar surface regions involved in protein-protein interactions with common target. These regions can serve as structural determinants of cross-reactivity and molecular mimicry. Molecular mimicry, defined as the process in which structural properties of one molecule are simulated by the dissimilar molecules, is implicated in several biologically important processes, including autoimmune and allergic reactions, binding of some ligands to common receptor, and interactions in cell signaling. The problem of identification of the determinants of molecular mimicry is not completely solved at this time. We hypothesize that identification of structurally and chemically similar surface regions of two protein molecules capable of binding to the same target will allow us to identify sites involved in cross-reactivity including determinants of the molecular mimicry. We used a graph-theoretical approach in order to determine highly similar surface regions of two proteins with known three-dimensional structures. This approach uses a variation of Maximal Common Subgraph (MCS) isomorphism, where an association graph is constructed based on the surface-exposed residues of the two molecules and the matching regions are found based on the maximum cliques in the association graph. Testing the proposed method on the targets of autoantibody involved in antiphopholipid syndrome (APS)--beta2-GPI, PC, thrombin, factor IX, factor X, and plasmin allowed identifying potential epitopes for antibody that can inhibit coagulation proteases. Application of this method to the Activated Protein C and factor VII Gla-domains revealed surface regions involved in EPCR and plasma membrane binding, consistent with known experimental results. Analysis of major pollen allergen that can cause food allergies through cross-reactivity found known epitopes involved in cross-reactivity and also revealed additional surface regions that can complement the list of epitopes. Taken together, our results suggest that the proposed graph-theoretical approach can identify determinants of cross-reactivity and molecular mimicry.

Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories

We introduce here a new class of invariants for MD trajectories based on the spectral moments pi(k)(L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA-drug docking experiments of 57 furocoumarins. The combined use of psoralens+ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA-drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective pi(k)(L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity=98.75%) set1 and 226 out of 238 (Sensitivity=94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity=100%) set1 and 75 out of 78 (sensitivity=96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξ_k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξ_k (L) values are invariants of a Markov matrix (¹ Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξ_k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.