Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

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.

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background - MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results - A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion - The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

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).

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

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.

Degradation, receptor binding, insulin secreting and antihyperglycaemic actions of palmitate-derivatised native and Ala8-substituted GLP-1 analogues

The hormone glucagon-like peptide-1(7-36)amide (GLP-1) is released in response to ingested nutrients and acts to promote glucose-dependent insulin secretion ensuring efficient postprandial glucose homeostasis. Unfortunately, the beneficial actions of GLP-1 which give this hormone many of the desirable properties of an antidiabetic drug are short lived due to degradation by dipeptidylpeptidase IV (DPP IV) and rapid clearance by renal filtration. In this study we have attempted to extend GLP-1 action through the attachment of palmitoyl moieties to the E-amino group in the side chain of the LyS26 residue and to combine this modification with substitutions of the Ala 8 residue, namely Val or amino-butyric acid (Abu). In contrast to native GLP-1, which was rapidly degraded, [Lys(pal) 26]GLP-1, [Abu8,Lys(pal)26]GLP-1 and [Val8,Lys-(pal)26]GLP-1 all exhibited profound stability during 12 h incubations with DPP IV and human plasma. Receptor binding affinity and the ability to increase cyclic AMP in the clonal β-cell line BRIN-BD11 were decreased by 86- to 167-fold and 15- to 62-fold, respectively compared with native GLP-1. However, insulin secretory potency tested using BRIN-BD11 cells was similar, or in the case of [Val8,Lys(pal)26]GLP-1 enhanced. Furthermore, when administered in vivo together with glucose to diabetic (ob/ob) mice, [Lys(pal)26]GLP-1, [Abu8,Lys(pal) 26]GLP-1 and [Val8,Lys(pal) 26]GLP-1 did not demonstrate acute glucose-lowering or insulinotropic activity as observed with native GLP-1. These studies support the potential usefulness of fatty acid linked analogues of GLP-1 but indicate the importance of chain length for peptide kinetics and bioavailability. Copyright © by Walter de Gruyter.

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

Mycobacterium tuberculosis peptides presented by HLA-E molecules are targets for human CD8 T-cells with cytotoxic as well as regulatory activity

Tuberculosis (TB) is an escalating global health problem and improved vaccines against TB are urgently needed. HLA-E restricted responses may be of interest for vaccine development since HLA-E displays very limited polymorphism (only 2 coding variants exist), and is not down-regulated by HIV-infection. The peptides from Mycobacterium tuberculosis (Mtb) potentially presented by HLA-E molecules, however, are unknown. Here we describe human T-cell responses to Mtb-derived peptides containing predicted HLA-E binding motifs and binding-affinity for HLA-E. We observed CD8(+) T-cell proliferation to the majority of the 69 peptides tested in Mtb responsive adults as well as in BCG-vaccinated infants. CD8(+) T-cells were cytotoxic against target-cells transfected with HLA-E only in the presence of specific peptide. These T cells were also able to lyse M. bovis BCG infected, but not control monocytes, suggesting recognition of antigens during mycobacterial infection. In addition, peptide induced CD8(+) T-cells also displayed regulatory activity, since they inhibited T-cell proliferation. This regulatory activity was cell contact-dependent, and at least partly dependent on membrane-bound TGF-beta. Our results significantly increase our understanding of the human immune response to Mtb by identification of CD8(+) T-cell responses to novel HLA-E binding peptides of Mtb, which have cytotoxic as well as immunoregulatory activity.

Synthesis and biological properties of 2-phenylbenzothiazole derivatives

2-Phenylbenzothiazoles have structural similarities to the antioestrogenic 2-phenylindole, zindoxifene and to the oestrogenic isoflavone, genistein which also inhibits tyrosine kinases. Hydroxylated 2-phenylbenzothiazole derivatives were therefore produced and tested for oestrogenic and tyrosine kinase inhibitory activity. Synthesis of methoxy substituted 2-phenylbenzothiazoles was via the Jacobson method, demethylation being effected by boron tribromide at -70oC. Three amino substituted 2-phenylbenzothiazoles were also synthesised and tested for activity. Data is presented for oestrogen receptor binding activity, aromatase inhibitory activity, epidermal growth factor receptor tyrosine kinase (EGFRTK) inhibitory activity and cytotoxicity to ANN-1, 3T3, MCF-7 and WIDR cells. Oestrogen receptor binding affinity (RBA) was shown by five of the nine compounds tested. 2-(4-hydroxy)-6-hydroxybenzo-thiazole was the most active of the benzothiazoles tested (RBA 0.7). This is low but comparable to that of genistein. EGFRTK inhibitory activity was shown by four of the six benzothiazole derivatives tested; activity was comparable to that of genistein. Cytotoxicity assays have shown no selective toxicity of 2-phenylbenzothiazoles to any of the cell lines tested. Toxicity to MCF-7 cells was similar to that for other cell lines despite some compounds showing oestrogen receptor binding capacity. Amino-substituted 2-phenylbenzothiazoles showed selective toxicity towards transformed ANN-1 cells compared to normal 3T3 cells but the mechanism of this selectivity has not been established. Molecular modelling techniques, including CHEM-X, QUANTA and MOPAC were used to compare known ATP-competitive tyrosine kinase inhibitors with a model of ATP built from the crystal structure of the ATP-phosphoglycerate kinase complex. Structural features thought to be important to kinase inhibition were found and used to suggest further 2-phenylbenzothiazole analogues which may have improved activity.

Non-classical inhibitors of dihydrofolate reductase

This thesis comprises two main objectives. The first objective involved the stereochemical studies of chiral 4,6-diamino-1-aryl-1,2-dihydro-s-triazines and an investigation on how the different conformations of these stereoisomers may affect their binding affinity to the enzyme dihydrofolate reductase (DHFR). The ortho-substituted 1-aryl-1,2-dihydro-s-triazines were synthesised by the three component method. An ortho-substitution at the C6' position was observed when meta-azidocycloguanil was decomposed in acid. The ortho-substituent restricts free rotation and this gives rise to atropisomerism. Ortho-substituted 4,6-diamino-1-aryl-2-ethyl-1,2-dihydro-2-methyl-s-triazine contains two elements of chirality and therefore exists as four stereoisomers: (S,aR), (R,aS), (R,aR) and (S,aS). The energy barriers to rotation of these compounds were calculated by a semi-empirical molecular orbital program called MOPAC and they were found to be in excess of 23 kcal/mol. The diastereoisomers were resolved and enriched by C18 reversed phase h.p.l.c. Nuclear overhauser effect experiments revealed that (S,aR) and (R,aS) were the more stable pair of stereoisomers and therefore existed as the major component. The minor diastereoisomers showed greater binding affinity for the rat liver DHFR in in vitro assay. The second objective entailed the investigation into the possibility of retaining DHFR inhibitory activity by replacing the classical diamino heterocyclic moiety with an amidinyl group. 4-Benzylamino-3-nitro-N,N-dimethyl-phenylamidine was synthesised in two steps. One of the two phenylamidines indicated weak inhibition against the rat liver DHFR. This weak activity may be due to the failure of the inhibitor molecule to form strong hydrogen bonds with residue Glu-30 at the active site of the enzyme.

Solid-supported boronic acid conjugates for sugar and glycopeptide recognition

The Fmoc synthetic strategy was employed to synthesise two identical combinatorial peptide libraries on a hydrophilic PEG-PS resin. One library was appended with boronic acid moieties at two positionally-fixed locations. Successful inclusion of the boronic acid units was confirmed using a novel UV fluorescent colorimetric assay employing carminic acid as the dye compound. A study of the effect had by the resin-bound peptides bearing boronic acid groups on the binding characteristics of vancomycin, a medically relevant antibiotic glycoprotein, was conducted. In all, 132 library compounds were tested for their binding affinity with vancomycin, via immobilisation of the glycopeptide onto the solid support through hydrogen bonding or complexation with the boronic acid moieties. Subsequent cleavage via acidolysis afforded vancomycin containing solutions which were quantified by growth inhibition of methicillin susceptible Staphylococcus aureus. Comparison of the diameters of the resultant zones of inhibition and those produced by vancomycin of known concentrations afforded a means of calculating the vancomycin concentration of the cleavage solutions, and thereby determining the binding affinity of vancomycin to each peptide sequence. Five peptide sequences and twenty one of the peptidyl-boronic acid sequences showed zones of inhibition, demonstrating their reversible affinity for vancomycin. Three peptide sequences showed zones of inhibition in both libraries. The presence of boronic acid was therefore shown to impart, enhance, detract and remove the affinity of vancomycin to a range of resin-bound peptide sequences.