Major histocompatibility complex binding affinity of an antigenic determinant is crucial for the differential secretion of interleukin 4/5 or interferon gamma by T cells.

Differential activation of CD4+ T-cell precursors in vivo leads to the development of effectors with unique patterns of lymphokine secretion. To investigate whether the differential pattern of lymphokine secretion is influenced by factors associated with either the display and/or recognition of the ligand, we have used a set of ligands with various class II binding affinities but unchanged T-cell specificity. The ligand that exhibited approximately 10,000-fold higher binding to I-Au considerably increased the frequency of interferon gamma-producing but not interleukin (IL) 4- or IL-5-secreting cells in vivo. Using an established ligand-specific, CD4+ T-cell clone secreting only IL-4, we also demonstrated that stimulation with the highest affinity ligand resulted in interferon gamma production in vitro. In contrast, ligands that demonstrated relatively lower class II binding induced only IL-4 secretion. These data suggest that the major histocompatibility complex binding affinity of antigenic determinants, leading to differential interactions at the T cell-antigen-presenting cell interface, can be crucial for the differential development of cytokine patterns in T cells.

Inference of binding affinity from neuronal receptors in humans

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016

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).

Cathepsin X cleavage of the beta2 integrin regulates talin-binding and LFA-1 affinity in T cells.

T cell migration, essential for immune surveillance and response, is mediated by the integrin LFA-1. CatX, a cysteine carboxypeptidase, is involved in the regulation of T cell migration by interaction with LFA-1. We show that sequential cleavage of C-terminal amino acids from the β(2) cytoplasmic tail of LFA-1, by CatX, enhances binding of the adaptor protein talin to LFA-1 and triggers formation of the latter's high-affinity form. As shown by SPR analysis of peptides constituting the truncated β(2) tail, the cleavage of three C-terminal amino acids by CatX resulted in a 1.6-fold increase of talin binding. Removal of one more amino acid resulted in a 2.5-fold increase over the intact tail. CatX cleavage increased talin-binding affinity to the MD but not the MP talin-binding site on the β(2) tail. This was shown by molecular modeling of the β(2) tail/talin F3 complex to be a result of conformational changes affecting primarily the distal-binding site. Analysis of LFA-1 by conformation-specific mAb showed that CatX modulates LFA-1 affinity, promoting formation of high-affinity from intermediate-affinity LFA-1 but not the initial activation of LFA-1 from a bent to extended form. CatX post-translational modifications may thus represent a mechanism of LFA-1 fine-tuning that enables the trafficking of T cells.

Water regulation of actinomycin-D binding to DNA: the interplay among drug affinity, DNA long-range conformation, and hydration

Actiaomycin-D (actD) binds to natural DNA at two different classes of binding sites, weak and strong. The affinity for these sites is highly dependent on DNA se(sequence and solution conditions, and the interaction appears to be purely entropic driven Although the entropic character of this reaction has been attributed to the release of water molecules upon drug to DNA complex formation, the mechanism by which hydration regulates actD binding and discrimination between different classes of binding sites on natural DNA is still unknown. In this work, we investigate the role of hydration on this reaction using the osmotic stress method. We skew that the decrease of solution water activity, due to the addition of sucrose, glycerol ethylene glycol, and betaine, favors drug binding to the strong binding sites on DNA by increasing both the apparent binding affinity Delta G, and the number of DNA base pairs apparently occupied by the bound drug n(bp/actD). These binding parameters vary linearly with the logarithm of the molar fraction of water in solution log(X-w), which indicates the contribution of water binding to the energetic of the reaction. It is demonstrated that the hydration change measured upon binding increases proportionally to the apparent size of the binding site n(bp/uctD). This indicates that n(bp/actD) measured from the Scatchard plod is a measure of the size of the DNA molecule changing conformation due to ligand binding. We also find that the contribution of DNA deformation, gauged by n(bp/act) to the total free energy of binding Delta G, is given by Delta G = Delta G(local) + n(bp/actD) x delta G(DNA), where Delta G(local), = -8020 +/- 51 cal/mol of actD bound and delta G(DNa) = -24.1 +/- 1.7cal/mol of base pair at 25 degrees C. We interpret Delta G(local), as the energetic contribution due to the direct interactions of actD with the actual tetranucleotide binding site, and it n(bp/actB) X delta G(DNA) as that due to change inconformation, induced by binding, of it n(bp/actD) DNA base pairs flanking the local site. This interpretation is supported by the agreement found between the value of delta G(DNA) and the torsional free energy change measured independently. We conclude suggesting an allosteric model for ligand binding to DNA, such that the increase in binding affinity is achieved by increasing the relaxation of the unfavorable free energy of binding storage at the local site through a larger number of DNA base pairs. The new aspect on this model is that the size of the complex is not fixed but determined by solutions conditions, such as water activity, which modulate the energetic barrier to change helix conformation. These results may suggest that long-range allosteric transitions of duplex DNA are involved in the inhibition of RNA synthesis by actD, and more generally, in the regulation of transcription. (C) 2000 John Wiley & Sons, Inc.

Phage display of a catalytic antibody to optimize affinity for transition-state analog binding

Catalytic antibodies have shown great promise for catalyzing a tremendously diverse set of natural and unnatural chemical transformations. However, few catalytic antibodies have efficiencies that approach those of natural enzymes. In principle, random mutagenesis procedures such as phage display could be used to improve the catalytic activities of existing antibodies; however, these studies have been hampered by difficulties in the recombinant expression of antibodies. Here, we have grafted the antigen binding loops from a murine-derived catalytic antibody, 17E8, onto a human antibody framework in an effort to overcome difficulties associated with recombinant expression and phage display of this antibody. “Humanized” 17E8 retained similar catalytic and hapten binding properties as the murine antibody while levels of functional Fab displayed on phage were 200-fold higher than for a murine variable region/human constant region chimeric Fab. This construct was used to prepare combinatorial libraries. Affinity panning of these resulted in the selection of variants with 2- to 8-fold improvements in binding affinity for a phosphonate transition-state analog. Surprisingly, none of the affinity-matured variants was more catalytically active than the parent antibody and some were significantly less active. By contrast, a weaker binding variant was identified with 2-fold greater catalytic activity and incorporation of a single substitution (Tyr-100aH → Asn) from this variant into the parent antibody led to a 5-fold increase in catalytic efficiency. Thus, phage display methods can be readily used to optimize binding of catalytic antibodies to transition-state analogs, and when used in conjunction with limited screening for catalysis can identify variants with higher catalytic efficiencies.

A novel assay for drug-DNA binding mode, affinity, and exclusion number: scanning force microscopy.

Determining the mode-of-binding of a DNA ligand is not always straightforward. Here, we establish a scanning force microscopic assay for mode-of-binding that is (i) direct: lengths of individual DNA-ligand complexes are directly measured; (ii) rapid: there are no requirements for staining or elaborate sample preparation; and (iii) unambiguous: an observed increase in DNA length upon addition of a ligand is definitive evidence for an intercalative mode-of-binding. Mode-of-binding, binding affinity, and site-exclusion number are readily determined from scanning force microscopy measurements of the changes in length of individual drug-DNA complexes as a function of drug concentration. With this assay, we resolve the ambiguity surrounding the mode of binding of 2,5-bis(4-amidinophenyl) furan (APF) to DNA and show that it binds to DNA by nonintercalative modes. APF is a member of an important class of aromatic dicationic drugs that show significant activity in the treatment of Pneumocystis carinii pneumonia, an opportunistic infection that is the leading cause of death in AIDS patients.

Structure of an Odorant-Binding Protein from the Mosquito Aedes aegypti Suggests a Binding Pocket Covered by a pH-Sensitive ""Lid""

Background: The yellow fever mosquito, Aedes aegypti, is the primary vector for the viruses that cause yellow fever, mostly in tropical regions of Africa and in parts of South America, and human dengue, which infects 100 million people yearly in the tropics and subtropics. A better understanding of the structural biology of olfactory proteins may pave the way for the development of environmentally-friendly mosquito attractants and repellents, which may ultimately contribute to reduction of mosquito biting and disease transmission. Methodology: Previously, we isolated and cloned a major, female-enriched odorant-binding protein (OBP) from the yellow fever mosquito, AaegOBP1, which was later inadvertently renamed AaegOBP39. We prepared recombinant samples of AaegOBP1 by using an expression system that allows proper formation of disulfide bridges and generates functional OBPs, which are indistinguishable from native OBPs. We crystallized AaegOBP1 and determined its three-dimensional structure at 1.85 angstrom resolution by molecular replacement based on the structure of the malaria mosquito OBP, AgamOBP1, the only mosquito OBP structure known to date. Conclusion: The structure of AaegOBP1 (= AaegOBP39) shares the common fold of insect OBPs with six alpha-helices knitted by three disulfide bonds. A long molecule of polyethylene glycol (PEG) was built into the electron-density maps identified in a long tunnel formed by a crystallographic dimer of AaegOBP1. Circular dichroism analysis indicated that delipidated AaegOBP1 undergoes a pH-dependent conformational change, which may lead to release of odorant at low pH (as in the environment in the vicinity of odorant receptors). A C-terminal loop covers the binding cavity and this ""lid"" may be opened by disruption of an array of acid-labile hydrogen bonds thus explaining reduced or no binding affinity at low pH.