Modelling of HLA-DQ2 and its interaction with gluten peptides to explain molecular recognition in celiac disease
Introduction
Celiac disease (CD), or celiac sprue, or gluten-sensitive enteropathy, is a multifactorial disorder influenced by both environmental and genetic factors. It is characterized by malabsorption resulting from inflammatory injury to the mucosa of the small intestine after the ingestion of wheat gluten or related rye and barley proteins. It often starts shortly after the first introduction of wheat into the diet, and symptoms include diarrhoea, malabsorption, and failure to thrive, which is due to an inefficient uptake of nutrients by a flattening intestinal epithelium. At present the only effective treatment for the disease is the removal of gluten from the diet, but the reintroduction of gluten in the patient's diet invariably leads to the reappearance of the symptoms [1], [2], [3]. Although the molecular basis of CD is still unclear, the molecular mechanism is considered to involve the binding of gluten peptides to HLA molecules and then the specific recognition by T-cells [2]. Different gluten-derived peptides have been identified as able to be recognized by T-cell clones isolated from biopsies of CD patients [4], [5], [6]. HLA-DQ2 (DQA1 *0501/B1 *0201) is found in the great majority of CD patients, while DQ8 (DQA1 *0301/B1 *0302) is found in most of the remaining patients [1], [2]. The binding of gluten peptides to DQ2 and DQ8 molecules has been experimentally observed and it may be improved by the presence of negatively charged amino acids at known positions in the peptide [1]. Gluten proteins are very rich of glutamine and proline and contain very few negatively charged amino acids, but glutamines may be deamidated to the negatively charged glutamate by the tissue transglutaminase (tTG) enzyme, with the consequence of improving both the binding to DQ2 and the response of T-cell clones [7], [8], [9].
Different studies have been carried out in order to simulate the interaction of gluten peptides with DQ2. Some simulations were started from the knowledge of the experimental model of the DR1 dimer and based on models of the DQ2 molecule obtained by simple modelling procedures as the amino acids substitutions [10], [11]. In two other works, a probably more complete but not described homology modelling procedure was applied starting from the DR1 [12] or DQ8 dimer [13]. The assessment of more efficient modelling procedures as well as the availability of the experimental structure of the DQ8 dimer, which is more similar to DQ2 and represents a better template model than DR1, makes it possible to improve the methods previously applied and to perform more accurate simulations of the interaction of gluten peptides with DQ2, as well as an accurate analysis of the effect of deamidation and other modifications of the peptides.
In our study, we used bioinformatics and biocomputing approaches and tools for predicting the 3D structure of DQ2 molecule, validate it and deposit the model in the PDB [14], in order to make the model available to the scientific community. Then, we simulated the complex between DQ2 and different gluten-derived peptides and their modified forms, to investigate the molecular details of this interaction and the effects of modification of specific peptide amino acids, in order to propose an interpretation at the molecular level of published experimental results concerning the binding of gluten peptides to DQ2 and the effects of deamidation. We also applied the same strategy to other gluten peptides for which a relation to celiac disease has been established, but no experimental evidence exists about deamidation of glutamines. Finally, to find further information about the specificity of DQ2 recognition, we analysed the presence of functional groups in the peptide-binding site, and evidenced the relevance of DQ2 structural peculiarities in comparison to other DQ alleles not related to celiac disease. We also discuss the results of our computational work in comparison to the very recently published article describing the experimental structure of the complex between DQ2 and an immunogenic epitope from gluten [15].
Section snippets
Protein modelling
Protein sequences of DQ2 chains used for protein modelling were selected from SwissProt database (DQA1 *0501 chain: accession number P01909; DQB1 *0201 chain: accession number P01918). The 3D model of DQ2 was created following the homology modelling procedure [16], [17], [18], [19] already used with success by our group [20], [21], [22], [23], [24]. In brief, searches for sequence similarity within databases were performed with the BLAST program [25]. The searches evidenced that a very high
Homology modelling of DQ2
The sequences of both DQ2 chains have been analyzed by computer programs in order to find similar sequences in databases and perform structural predictions to obtain a theoretical 3D model of the protein. The sequences of both DQ8 chains were found to be the most similar to the corresponding DQ2 chains, in terms of sequence similarity, having 91% of sequence identity for both alpha and beta chains (see Fig. 1). In these conditions, a homology modelling strategy can be applied with very good
Comparison to published experimental structure of DQ2
We modelled the DQ2 structure and deposited it in the Protein Data Bank (PDB code: 1NBN) before the publication and availability of an experimental structure of DQ2 [15]. The comparison between the predicted model and the experimental structure confirms that the homology modelling procedure has been correctly applied. In fact, as shown in Fig. 2B and C, the superposition of chain backbones and secondary structures are very good. The few differences in the secondary structure assigned are mainly
Conclusions
For many years, studies at the molecular level of the interaction between DQ2 and gluten peptides were based on molecular simulations and modelling of DQ2. In order to improve the previous molecular modelling results by using the most recent and reliable approach, we created a model of DQ2 based on a better homologous model and by creating a full atom model instead of simple side chain substitution, and deposited the model in PDB to make it available to the scientific community for further
Acknowledgements
Ph.D. fellowship of Dr. Susan Costantini is supported by E.U.
References (39)
Current concepts of celiac disease pathogenesis
Gastroenterology
(2000)- et al.
Human genome search in celiac disease: mutated gliadin T-cell-like epitope in two human proteins promotes T-cell activation
J. Mol. Biol.
(2002) - et al.
Protein structure prediction
Curr. Opin. Biotech.
(1998) Homology modelling with low sequence identity
Methods
(1998)- et al.
Modelling of fish interleukin 1 and its receptor
Dev. Comp. Immunol.
(2004) - et al.
Basic local alignment search tool
J. Mol. Biol.
(1990) - et al.
Comparative protein modelling by satisfaction of spatial restraints
J. Mol. Biol.
(1993) - et al.
SCOP: a structural classification of proteins database for the investigation of sequences and structures
J. Mol. Biol.
(1995) - et al.
Molecular recognition. Conformational analysis of limited proteolytic sites and serine proteinase protein inhibitors
J. Mol. Biol.
(1991) - et al.
Satisfying hydrogen bonding potential in proteins
J. Mol. Biol.
(1994)
Celiac lesion T cells recognize epitopes that cluster in regions of gliadins rich in proline residues
Gastroenterology
The gluten response in children with celiac disease is directed toward multiple gliadin and glutenin peptides
Gastroenterology
Molecular basis of celiac disease
Ann. Rev. Immunol.
Celiac disease: dissecting a complex inflammatory disorder
Nat. Rev. Immunol.
HLA binding and T cell recognition of a tissue transglutaminase-modified gliadin epitope
Eur. J. Immunol.
Structure of celiac disease-associated HLA-DQ8 and non-associated HLA-DQ9 alleles in complex with two disease-specific epitopes
Int. Immunol.
The intestinal T cell response to a-gliadin in adult celiac disease is focused on a single deamidated glutamine targeted by tissue transglutaminase
J. Exp. Med.
Tissue transglutaminase selectively modifies gliadin peptides that are recognized by gut-derived T cells in celiac disease
Nat. Med.
Selective deamidation by tissue transglutaminase strongly enhances gliadin-specific T cell reactivity
J. Immunol.
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