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.

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).

Oxidized phospholipid inhibition of toll-like receptor (TLR) signaling is restricted to TLR2 and TLR4 roles for cd14, lps-binding protein, and md2 as targets for specificity of inhibition

The generation of reactive oxygen species is a central feature of inflammation that results in the oxidation of host phospholipids. Oxidized phospholipids, such as 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine (OxPAPC), have been shown to inhibit signaling induced by bacterial lipopeptide or lipopolysac-charide (LPS), yet the mechanisms responsible for the inhibition of Toll-like receptor (TLR) signaling by OxPAPC remain incompletely understood. Here, we examined the mechanisms by which OxPAPC inhibits TLR signaling induced by diverse ligands in macrophages, smooth muscle cells, and epithelial cells. OxPAPC inhibited tumor necrosis factor- production, IB degradation, p38 MAPK phosphorylation, and NF-B-dependent reporter activation induced by stimulants of TLR2 and TLR4 (Pam3CSK4 and LPS) but not by stimulants of other TLRs (poly(I·C), flagellin, loxoribine, single-stranded RNA, or CpG DNA) in macrophages and HEK-293 cells transfected with respective TLRs and significantly reduced inflammatory responses in mice injected subcutaneously or intraperitoneally with Pam3CSK4. Serum proteins, including CD14 and LPS-binding protein, were identified as key targets for the specificity of TLR inhibition as supplementation with excess serum or recombinant CD14 or LBP reversed TLR2 inhibition by OxPAPC, whereas serum accessory proteins or expression of membrane CD14 potentiated signaling via TLR2 and TLR4 but not other TLRs. Binding experiments and functional assays identified MD2 as a novel additional target of OxPAPC inhibition of LPS signaling. Synthetic phospholipid oxidation products 1-palmitoyl-2-(5-oxovaleryl)-sn-glycero-3-phosphocholine and 1-palmitoyl-2-glutaryl-sn-glycero-3-phosphocholine inhibited TLR2 signaling from 30 µM. Taken together, these results suggest that oxidized phospholipid-mediated inhibition of TLR signaling occurs mainly by competitive interaction with accessory proteins that interact directly with bacterial lipids to promote signaling via TLR2 or TLR4.

Characterization of metal ion-induced [3H]inositol hexakisphosphate binding to rat cerebellar membranes

The binding of [3H]inositol hexakisphosphate ([3H] InsP6) to rat cerebellar membranes has been characterized with the objective of establishing the role, if any, of a membrane protein receptor. In the presence of EDTA, we have previously identified an InsP6-binding site with a capacity of approximately 20 pmol/mg protein (Hawkins, P. T., Reynolds, D. J. M., Poyner, D. R., and Hanley, M. R. (1990) Biochem. Biophys. Res. Commun. 167, 819-827). However, in the presence of 1 mM Mg2+, the capacity of [3H]InsP6 binding to membranes was increased approximately 9-fold. This enhancing effect of Mg2+ was reversed by addition of 10 microM of several cation chelators, suggesting that the increased binding required trace quantities of other metal cations. This is supported by experiments where it was possible to saturate binding by addition of excess membranes, despite not significantly depleting radioligand, pointing to removal of some other factor. Removal of endogenous cations from the binding assay by pretreatment with chelex resin also prevents the Mg(2+)-induced potentiation. Consideration of the specificity of the chelators able to abolish this potentiation suggested involvement of Fe3+ or Al3+. Both these ions (but not several others) were able to increase [3H]InsP6 binding to chelex-pretreated membranes at concentrations of 1 microM. It is possible to demonstrate synergy between Fe3+ and Mg2+ under these conditions. We propose that [3H]InsP6 may interact with membranes through non-protein recognition possibly via phospholipids, in a manner dependent upon trace metals. The implications of this for InsP6 biology are considered.

Identification of a novel inositol phosphate recognition site: specific [3H]inositol hexakisphosphate binding to brain regions and cerebellar membranes

[3H]Inositol hexakisphosphate (InsP6) binds with a heterogeneous distribution to frozen sections of unfixed rat brain and is displaced by unlabelled InsP6. The pattern of binding correlates with binding to neuronal cell bodies. [3H]InsP6 binding to cerebellar membranes has been further characterised, is reversible, and saturable, and exhibits high specificity for inositol polyphosphates. The IC50 for competition by unlabelled InsP6 is approximately 100nM, whereas inositol 1,3,4,5,6 pentakisphosphate (Ins(13456)P5), inositol 1,3,4,5 tetrakisphosphate (Ins(1345)P4), and inositol 1,4,5 trisphosphate (Ins(145)P3) bind with an affinity at least one order of magnitude lower. [3H]InsP6 binding is clearly distinct from previously characterised Ins(145)P3 (ref. 1, 2) and Ins(1345)P4 (ref. 3) binding, both in terms of pharmacology and brain distribution.

Oxidative modification of a specific apolipoprotein B lysine residue confers altered receptor specificity on LDL

During inflammation, many cell types release reactive oxygen species (ROS) via the respiratory burst. These ROS are potent oxidants of LDL and its major protein, apolipoprotein B. Whilst native LDL is taken up by endothelial cells via a feedback controlled receptor-regulated process, oxidative modification of LDL renders it a ligand for many scavenger receptors. Scavenger receptors include CD-36, LOX-1 and the prototypic macrophage SR A I/II, all of which are variably expressed. Uncontrolled uptake of oxidised LDL is implicated in the pathogenesis of atherosclerosis. In addition, oxidised LDL increases CCR2 protein and mRNA expression on monocytes, and thus may contribute to monocyte retention and perpetuation in inflammatory, unstable atherosclerotic lesions. However, little data are available on the effects of specific minor modifications to apolipoprotein B. In order to identify the sequence specificity and nature of oxidative modifications which confer altered properties on LDL, we have investigated the effects of modified peptides (which correspond to the putative LDLR binding domain) on LDL uptake by HUVECs and U937 monocytes.

Discovering properties of new DNA-binding activity of proteins

Protein-DNA interactions are an essential feature in the genetic activities of life, and the ability to predict and manipulate such interactions has applications in a wide range of fields. This Thesis presents the methods of modelling the properties of protein-DNA interactions. In particular, it investigates the methods of visualising and predicting the specificity of DNA-binding Cys2His2 zinc finger interaction. The Cys2His2 zinc finger proteins interact via their individual fingers to base pair subsites on the target DNA. Four key residue positions on the a- helix of the zinc fingers make non-covalent interactions with the DNA with sequence specificity. Mutating these key residues generates combinatorial possibilities that could potentially bind to any DNA segment of interest. Many attempts have been made to predict the binding interaction using structural and chemical information, but with only limited success. The most important contribution of the thesis is that the developed model allows for the binding properties of a given protein-DNA binding to be visualised in relation to other protein-DNA combinations without having to explicitly physically model the specific protein molecule and specific DNA sequence. To prove this, various databases were generated, including a synthetic database which includes all possible combinations of the DNA-binding Cys2His2 zinc finger interactions. NeuroScale, a topographic visualisation technique, is exploited to represent the geometric structures of the protein-DNA interactions by measuring dissimilarity between the data points. In order to verify the effect of visualisation on understanding the binding properties of the DNA-binding Cys2His2 zinc finger interaction, various prediction models are constructed by using both the high dimensional original data and the represented data in low dimensional feature space. Finally, novel data sets are studied through the selected visualisation models based on the experimental DNA-zinc finger protein database. The result of the NeuroScale projection shows that different dissimilarity representations give distinctive structural groupings, but clustering in biologically-interesting ways. This method can be used to forecast the physiochemical properties of the novel proteins which may be beneficial for therapeutic purposes involving genome targeting in general.

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.

Receptor activity-modifying protein-directed G protein signaling specificity for the calcitonin gene-related peptide family of receptors

The calcitonin gene-related peptide (CGRP) family of G protein- coupled receptors (GPCRs) is formed through the association of the calcitonin receptor-like receptor (CLR) and one of three receptor activity-modifying proteins (RAMPs). Binding of one of the three peptide ligands, CGRP, adrenomedullin (AM), and intermedin/adrenomedullin 2 (AM2), is well known to result in aGαs-mediated increase in cAMP. Here we used modified yeast strains that couple receptor activation to cell growth, via chimeric yeast/Gα subunits, and HEK-293 cells to characterize the effect of different RAMP and ligand combinations on this pathway. We not only demonstrate functional couplings to both Gαs and Gαq but also identify a Gαi component to CLR signaling in both yeast and HEK-293 cells, which is absent in HEK-293S cells. We show that the CGRP family of receptors displays both ligand- and RAMPdependent signaling bias among the Gαs, Gαi, and Gαq/11 pathways. The results are discussed in the context of RAMP interactions probed through molecular modeling and molecular dynamics simulations of the RAMP-GPCR-G protein complexes. This study further highlights the importance of RAMPs to CLR pharmacology and to bias in general, as well as identifying the importance of choosing an appropriate model system for the study of GPCR pharmacology.