Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Identifiying human MHC supertypes using bioinformatic methods

Classification of MHC molecules into supertypes in terms of peptide-binding specificities is an important issue, with direct implications for the development of epitope-based vaccines with wide population coverage. In view of extremely high MHC polymorphism (948 class I and 633 class II HLA alleles) the experimental solution of this task is presently impossible. In this study, we describe a bioinformatics strategy for classifying MHC molecules into supertypes using information drawn solely from three-dimensional protein structure. Two chemometric techniques–hierarchical clustering and principal component analysis–were used independently on a set of 783 HLA class I molecules to identify supertypes based on structural similarities and molecular interaction fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77. action fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

MHCPred : a server for quantitative prediction of peptide-MHC binding

Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-base multivariate statistical approach to the quantitative prediction of peptide binding to major histocom-patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive,cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203,HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401and HLA-DRB* 0701).

Histidine hydrogen bonding in MHC at pH 5 and pH 7 modeled by molecular docking and molecular dynamics simulations

Hydrogen bonds play important roles in maintaining the structure of proteins and in the formation of most biomolecular protein-ligand complexes. All amino acids can act as hydrogen bond donors and acceptors. Among amino acids, Histidine is unique, as it can exist in neutral or positively charged forms within the physiological pH range of 5.0 to 7.0. Histidine can thus interact with other aromatic residues as well as forming hydrogen bonds with polar and charged residues. The ability of His to exchange a proton lies at the heart of many important functional biomolecular interactions, including immunological ones. By using molecular docking and molecular dynamics simulation, we examine the influence of His protonation/deprotonation on peptide binding affinity to MHC class II proteins from locus HLA-DP. Peptide-MHC interaction underlies the adaptive cellular immune response, upon which the next generation of commercially-important vaccines will depend. Consistent with experiment, we find that peptides containing protonated His residues bind better to HLA-DP proteins than those with unprotonated His. Enhanced binding at pH 5.0 is due, in part, to additional hydrogen bonds formed between peptide His+ and DP proteins. In acidic endosomes, protein His79β is predominantly protonated. As a result, the peptide binding cleft narrows in the vicinity of His79β, which stabilizes the peptide - HLA-DP protein complex. © 2014 Bentham Science Publishers.

Estudio en aves silvestres de canto ("Passeriformes") de las moléculas MHC y reclasificación molecular de la familia "Carduelini"

The Major Histocompatibility Complex (MHC) comprises the most polymorphic loci in animals. MHC plays an important role during the first steps of the immune response in vertebrates. In humans, MHC molecules (also named human leukocyte antigens, HLA) were initially regarded as class I or class II molecules. Each of them, presents to different T cells subsets. MHC class I molecules, are heterodimers in which the heavy chain (alpha) has three extracellular domains, two of which (alpha 1 and alpha 2) are polymorphic and conform the antigen recognition sites (ARS). The ARS is thought to be subjected to balancing selection for variability, which is the cause of the very high polymorphism of the MHC molecules. Different pathogenic epitopes would be the evolutionary force causing balancing selection. MHC class I genes have been completely sequenced (α1 and α2 protein domains) and thoroughly studied in Gallus gallus (chicken) as well as in mammals. In fact, the MHC locus was first defined in chicken, specifically in the highly consanguineous variety „Leghorn‟. It has been found that, in the case of chickens the MHC genetic region is considerably smaller than it is in mammals (remarkably shorter introns were found in chickens), and is organized quite differently. The noteworthy presence of short introns in chickens; supported the hypothesis that chicken‟s MHC represented a „minimal essential MHC‟. Until now, it has been assumed that chicken (order Galliformes) MHC was similar to all species included in the whole class Aves...

Mucopolysaccharidosis Type Ii: Identification Of 30 Novel Mutations Among Latin American Patients.

In this study, 103 unrelated South-American patients with mucopolysaccharidosis type II (MPS II) were investigated aiming at the identification of iduronate-2-sulfatase (IDS) disease causing mutations and the possibility of some insights on the genotype-phenotype correlation The strategy used for genotyping involved the identification of the previously reported inversion/disruption of the IDS gene by PCR and screening for other mutations by PCR/SSCP. The exons with altered mobility on SSCP were sequenced, as well as all the exons of patients with no SSCP alteration. By using this strategy, we were able to find the pathogenic mutation in all patients. Alterations such as inversion/disruption and partial/total deletions of the IDS gene were found in 20/103 (19%) patients. Small insertions/deletions/indels (<22 bp) and point mutations were identified in 83/103 (88%) patients, including 30 novel mutations; except for a higher frequency of small duplications in relation to small deletions, the frequencies of major and minor alterations found in our sample are in accordance with those described in the literature.

Reduced Plasma Angiotensin Ii Levels Are Reversed By Hydroxyurea Treatment In Mice With Sickle Cell Disease.

Sickle cell disease (SCD) pathogenesis leads to recurrent vaso-occlusive and hemolytic processes, causing numerous clinical complications including renal damage. As vasoconstrictive mechanisms may be enhanced in SCD, due to endothelial dysfunction and vasoactive protein production, we aimed to determine whether the expression of proteins of the renin-angiotensin system (RAS) may be altered in an animal model of SCD. Plasma angiotensin II (Ang II) was measured in C57BL/6 (WT) mice and mice with SCD by ELISA, while quantitative PCR was used to compare the expressions of the genes encoding the angiotensin-II-receptors 1 and 2 (AT1R and AT2R) and the angiotensin-converting enzymes (ACE1 and ACE2) in the kidneys, hearts, livers and brains of mice. The effects of hydroxyurea (HU; 50-75mg/kg/day, 4weeks) treatment on these parameters were also determined. Plasma Ang II was significantly diminished in SCD mice, compared with WT mice, in association with decreased AT1R and ACE1 expressions in SCD mice kidneys. Treatment of SCD mice with HU reduced leukocyte and platelet counts and increased plasma Ang II to levels similar to those of WT mice. HU also increased AT1R and ACE2 gene expression in the kidney and heart. Results indicate an imbalanced RAS in an SCD mouse model; HU therapy may be able to restore some RAS parameters in these mice. Further investigations regarding Ang II production and the RAS in human SCD may be warranted, as such changes may reflect or contribute to renal damage and alterations in blood pressure.

Phtx-ii A Basic Myotoxic Phospholipase A2 From Porthidium Hyoprora Snake Venom, Pharmacological Characterization And Amino Acid Sequence By Mass Spectrometry.

A monomeric basic PLA2 (PhTX-II) of 14149.08 Da molecular weight was purified to homogeneity from Porthidium hyoprora venom. Amino acid sequence by in tandem mass spectrometry revealed that PhTX-II belongs to Asp49 PLA2 enzyme class and displays conserved domains as the catalytic network, Ca2+-binding loop and the hydrophobic channel of access to the catalytic site, reflected in the high catalytic activity displayed by the enzyme. Moreover, PhTX-II PLA2 showed an allosteric behavior and its enzymatic activity was dependent on Ca2+. Examination of PhTX-II PLA2 by CD spectroscopy indicated a high content of alpha-helical structures, similar to the known structure of secreted phospholipase IIA group suggesting a similar folding. PhTX-II PLA2 causes neuromuscular blockade in avian neuromuscular preparations with a significant direct action on skeletal muscle function, as well as, induced local edema and myotoxicity, in mice. The treatment of PhTX-II by BPB resulted in complete loss of their catalytic activity that was accompanied by loss of their edematogenic effect. On the other hand, enzymatic activity of PhTX-II contributes to this neuromuscular blockade and local myotoxicity is dependent not only on enzymatic activity. These results show that PhTX-II is a myotoxic Asp49 PLA2 that contributes with toxic actions caused by P. hyoprora venom.

[impact Of The Activation Of Intention To Perform Physical Activity In Type Ii Diabetics: A Randomized Clinical Trial].

Type II diabetes mellitus is a highly prevalent disease among the adult Brazilian population, and one that can be controlled by interventions such as physical activity, among others. The aim of this randomized controlled study was to evaluate the impact of a traditional motivational strategy, associated with the activation of intention theory, on adherence to physical activity in patients with type II, diabetes mellitus who are part of the Unified Health System (SUS). Participants were divided into a control group (CG) and an intervention group (IG). In both groups, the traditional motivational strategy was applied, but the activation of intention strategy was only applied to the IG Group. After a two-month follow-up, statistically significant differences were verified between the groups, related to the practice of walking (p = 0.0050), number of days per week (p = 0.0076), minutes per day (p = 0.0050) and minutes walking per week (p = 0.0015). At the end of the intervention, statistically significant differences in abdominal circumference (p = 0.0048) between the groups were observed. The conclusion drawn is that the activation of intention strategy had greater impact on adherence to physical activity and reduction in abdominal circumference in type II diabetics, than traditional motivational strategy.

Comparação da eficiência do processo de ozonização e ozonização catalítica (Mn II e Cu II) na degradação de fenol

This paper discusses the results obtained with homogeneous catalytic ozonation [Mn (II) and Cu (II)] in phenol degradation. The reduction of total phenols and total organic carbon (TOC) and the ozone consumption were evaluated. The efficiency in phenol degradation (total phenol removal) at pH 3, with the catalytic process (Mn (II)), increased from 37% to 55% while the TOC removal increased from 4 to 63% in a seven-minute treatment. The ozonation process efficiency at pH 10 was 43% and 39% for phenol and TOC removal, respectively. The presence of both metallic ions (Mn2+ and Cu+2) in the ozonation process resulted in a positive effect.