Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies

Nyárády, Zoltán; Czömpöly, Tamás; Bősze, Sz.; Nagy, Gergely; Petrohai, A; Pál, József; Hudecz, Ferenc; Berki, Tímea; Németh, Pèter

doi:10.1016/j.molimm.2005.06.044

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…This, combined with the large MHC specificity diversity in the human population, makes direct experimental immunology, where overlapping peptides covering the genome in question are experimentally investigated for their immunogenicity, an extremely costly process. Many studies have therefore applied in silico screening methods to the identification of T‐cell epitopes 89–95 . The majority of these studies, including even the recent ones, uses the TEPITOPE prediction algorithm for the in silico screening.…”

Section: Identification Of Cd4 Epitopes and Mhc Class II Ligandsmentioning

confidence: 99%

“…Many studies have therefore applied in silico screening methods to the identification of T-cell epitopes. [89][90][91][92][93][94][95] The majority of these studies, including even the recent ones, uses the TEPITOPE prediction algorithm for the in silico screening. This is surprising because many benchmark studies have shown that state-of-the-art data-driven methods significantly outperform the TEPI-TOPE method when it comes to identification of MHC class II ligands (see above).…”

Section: Identification Of Cd4 Epitopes and Mhc Class II Ligandsmentioning

confidence: 99%

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MHC Class II epitope predictive algorithms

et al. 2010

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Summary Major histocompatibility complex class II (MHC‐II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide–MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC‐II. Despite recent progress in method development, the predictive performance for MHC‐II remains significantly lower than what can be obtained for MHC‐I. One reason for this is that the MHC‐II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC‐II‐bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data‐driven methods. Thousands of different MHC‐II alleles exist in humans. Recently developed pan‐specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC‐II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan‐specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.

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Section: Identification Of Cd4 Epitopes and Mhc Class II Ligandsmentioning

confidence: 99%

Section: Identification Of Cd4 Epitopes and Mhc Class II Ligandsmentioning

confidence: 99%

MHC Class II epitope predictive algorithms

et al. 2010

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“…B‐cell epitope mapping using a series of pin‐attached overlapping synthetic peptides can be a very efficient way to identify linear antigenic determinants recognized by either serum antibodies or a protein specific antibody in an immunoassay . This strategy has successfully been used in several studies , including our works , to identify antigenic epitope regions of viral, bacterial, and other target proteins.…”

Section: Introductionmentioning

confidence: 99%

The mapping of linear B‐cell epitope regions in the extracellular parts of the desmoglein 1 and 3 proteins: recognition of immobilized peptides by pemphigus patients' serum autoantibodies

Szabados

Bősze

Silló

et al. 2013

Journal of Peptide Science

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Desmosomal transmembrane glycoproteins desmogleins (Dsg) 1 and 3 are targets of life-threatening autoimmune blistering disorders such as Pemphigus vulgaris (PV) and Pemphigus foliaceus (PF). In these diseases, pemphigus autoantibodies are produced against Dsg1 and Dsg3 proteins. The autoantibodies bind to these transmembrane elements leading to a loss of desmosomal cell-cell adhesion and clinically, to the presence of blisters and erosions. Identification, characterization, and detailed analysis of the binding sites of autoantibodies have an outstanding importance in understanding the immunopathology of the disease and also in the design of novel diagnostics. Here, we describe the localization of the B-cell epitope regions of Dsg1 and Dsg3 proteins' extracellular parts recognized by IgG-type serum autoantibodies of patients with PV and PF. In our study, overlapping pentadecapeptides were synthesized on hydroxypropyl methacrylate pins based on the results of in silico predictions. To detect the interaction between the serum autoantibodies and the immobilized synthetic peptides, modified Enzyme Linked Immunosorbent Assay (ELISA) was performed with pin-attached peptides testing the serum samples of ten patients and four healthy donors. We identified five possible epitope regions (aa86-110, aa196-220, aa226-250, aa326-340, and aa486-520) within the extracellular part of the Dsg1 and four possible epitope regions (aa64-78, aa330-344, aa375-399, and aa446-460) within that of the Dsg3 protein sequence using these methods. Our data showed that serum autoantibodies of patients, previously identified as Dsg1 and Dsg3 positive, are able to recognize continuous linear epitope regions of both Dsg1 and Dsg3 proteins using pin-bound overlapping peptides in modified ELISAs.

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“…We address these issues using computational methods, including automated molecular docking and MD simulations. Classical MD simulations make possible the detailed analysis of the individual movements of the atoms in the molecules as a function of time, resulting in an ideal model for understanding the atomic and molecular mechanisms involved in the formation of non-covalent enzymeligand complex [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Conformational changes in 2-trans-enoyl-ACP (CoA) reductase (InhA) from M. tuberculosis induced by an inorganic complex: a molecular dynamics simulation study

et al. 2011

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InhA, the NADH-dependent 2-trans-enoyl-ACP reductase enzyme from Mycobacterium tuberculosis (MTB), is involved in the biosynthesis of mycolic acids, the hallmark of mycobacterial cell wall. InhA has been shown to be the primary target of isoniazid (INH), one of the oldest synthetic antitubercular drugs. INH is a prodrug which is biologically activated by the MTB catalase-peroxidase KatG enzyme. The activation reaction promotes the formation of an isonicotinyl-NAD adduct which inhibits the InhA enzyme, resulting in reduction of mycolic acid biosynthesis. As a result of rational drug design efforts to design alternative drugs capable of inhibiting MTB's InhA, the inorganic complex pentacyano(isoniazid)ferrate(II) (PIF) was developed. PIF inhibited both wild-type and INH-resistant Ile21Val mutants of InhA and this inactivation did not require activation by KatG. Since no three-dimensional structure of the InhA-PIF complex is available to confirm the binding mode and to assess the molecular interactions with the protein active site residues, here we report the results of molecular dynamics simulations of PIF interaction with InhA. We found that PIF strongly interacts with InhA and that these interactions lead to macromolecular instabilities reflected in the long time necessary for simulation convergence. These instabilities were mainly due to perturbation of the substrate binding loop, particularly the partial denaturation of helices α6 and α7. We were also able to correlate the changes in the SASAs of Trp residues with the recent spectrofluorimetric investigation of the InhA-PIF complex and confirm their suggestion that the changes in fluorescence are due to InhA conformational changes upon PIF binding. The InhA-PIF association is very strong in the first 20.0 ns, but becomes very week at the end of the simulation, suggesting that the PIF binding mode we simulated may not reflect that of the actual InhA-PIF complex.

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Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies

Cited by 14 publications

References 40 publications

MHC Class II epitope predictive algorithms

MHC Class II epitope predictive algorithms

The mapping of linear B‐cell epitope regions in the extracellular parts of the desmoglein 1 and 3 proteins: recognition of immobilized peptides by pemphigus patients' serum autoantibodies

Conformational changes in 2-trans-enoyl-ACP (CoA) reductase (InhA) from M. tuberculosis induced by an inorganic complex: a molecular dynamics simulation study

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