Investigation of the role of protein kinase C (PKC) in cytostasis induced by tumour promoting phorbol esters and related compounds

Protein kinase C (PKC) is considered to be the major receptor for tumour promoting phorbol esters such as 12-0- tetradecanoylphorbol-13-acetate (TPA). These agents evoke a plethora of biological effects on cells in culture. The growth of A549 human lung carcinoma cells maintained in medium fortified with 10% foetal calf serum (FCS) is arrested for 6 days by TPA and other biologically active phorbol esters. In the work described in this thesis, the hypothesis was tested that modulation of PKC activity is closely related to events pivotal for cytostasis to occur. The effect of several phorbol esters, of newly synthesized analogues of diacylglycerols (DAG) and of bryostatins (bryos) on cell growth and ability to modulate activity of PKC has been investigated.Determination of the subcellular distribution of PKC following treatment of cells with TPA and partial enzyme purification by non-denaturing poly-acrylamide gel electrophoresis revealed translocation of enzyme activity from cytosoUc to paniculate fraction. Chronic exposure of cells to TPA resulted in a time and concentration dependent degradation of enzyme activity. Synthetic DAG and DAG analogues, unable to arrest the growth of cells at non-toxic concentrations, were neither able to affect subcellular PKC distribution nor compete effectively for phorbol ester binding sites at physiologically relevant concentrations. Bryos 1,2,4 and 5, natural products, possessing antineoplastic activity in mice, elicited transient arrest of A549 cell growth in vitro. They successfully competed for phorbol ester receptors in A549 cells with exquisite affinity and induced a shift in sub-cellular PKC distribution, though not to the same extent as PTA. Enzyme down-regulation resulted from prolonged exposure of cells to nanomolar concentrations of bryos. In vivo studies demonstrated that neither PDBu nor bryo 1 was able to inhibit A549 xenograft growth in athymic mice. The growth of A549 cell populations cultured under conditions of serum-deprivation was inhibited only transiently by biologically active phorbol esters. Fortification of serum-free medium with EGF or fetuin was able to partially restore sensitivity to maintained growth arrest by PTA. PKC translocation to the paniculate cellular fraction and subsequent enzyme down-regulation, induced by TPA, occurred in a manner similar to that observed in serum-supplemented cells. However, total PKC activity and cytosolic phorbol ester binding potential were greatly reduced in the serum-deprived cell population. Western blot analysis using monospecific monoclonal antibodies revealed the presence of PKC-a in both A549 cell populations, with significantly reduced protein levels in serum- deprived cells. PKC-/9 was not detected in either cell population.

Peptide length significantly influences in vitro affinity for MHC class II molecules

Class II Major Histocompatibility Complex (MHC) molecules have an open-ended binding groove which can accommodate peptides of varying lengths. Several studies have demonstrated that peptide flanking residues (PFRs) which lie outside the core binding groove can influence peptide binding and T cell recognition. By using data from the AntiJen database we were able to characterise systematically the influence of PFRs on peptide affinity for MHC class II molecules.

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.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

Affinity partitioning of plasmid DNA with a zinc finger protein

The affinity isolation of pre-purified plasmid DNA (pDNA) from model buffer solutions using native and poly(ethylene glycol) (PEG) derivatized zinc finger–GST (Glutathione-S-Transferase) fusion protein was examined in PEG–dextran (DEX) aqueous two-phase systems (ATPSs). In the absence of pDNA, partitioning of unbound PEGylated fusion protein into the PEG-rich phase was confirmed with 97.5% of the PEGylated fusion protein being detected in the PEG phase of a PEG 600–DEX 40 ATPS. This represents a 1322-fold increase in the protein partition coefficient in comparison to the non-PEGylated protein (Kc = 0.013). In the presence of pDNA containing a specific oligonucleotide recognition sequence, the zinc finger moiety of the PEGylated fusion protein bound to the plasmid and steered the complex to the PEG-rich phase. An increase in the proportion of pDNA that partitioned to the PEG-rich phase was observed as the concentration of PEGylated fusion protein was increased. Partitioning of the bound complex occurred to such an extent that no DNA was detected by the picogreen assay in the dextran phase. It was also possible to partition pDNA using a non-PEGylated (native) zinc finger–GST fusion protein in a PEG 1000–DEX 500 ATPS. In this case the native ligand accumulated mainly in the PEG phase. These results indicate good prospects for the design of new plasmid DNA purification methods using fusion proteins as affinity ligands.

Affinity purification of plasmid DNA directly from crude bacterial cell lysates

We have shown previously that a sequence-specific DNA-binding protein based on the Lac repressor protein can isolate pre-purified DNA efficiently from simple buffer solution but our attempts to purify plasmids directly from crude starting materials were disappointing with unpractically low DNA yields. We have optimized tbe procedure and present a simple affinity methodology whereby plasmid DNA is purified directly by mixing two crude cell lysates, one cell lysate containing the plasmid and the other the protein affinity ligand, without the need for treatment by RNaseA. After IMAC chromatography, high purity supercoiled DNA is recovered in good yields of 100-150 μg plasmid per 200 mL shake flask culture. Moreover, the resulting DNA is free from linear or open-circular plasmid DNA, genomic DNA, RNA, and protein, to the limits of our detection. Furthermore, we show that lyophilized affinity ligand can be stored at room temperature and re-hydrated for use when required. © 2007 Wiley Periodicals, Inc.

Morphology modification of polyethylene/clay nanocomposite samples under convergent flow

The morphology of PE/CL nanocomposite samples subjected to convergent flows is studied. Elongational flow – the typical flow involved in spinning and film-blowing processing operations – significantly increases with the reduction of the capillary diameter. The values of the convergent extensional stress (calculated by Cogswell's formula) for the PE/CL systems, for all the adopted capillary geometries, are greater than the calculated values for pure polyethylene. The applied convergent flow, at the entrance of the capillary, is able to change the clay morphology and consequently the final material properties on the PE/CL system with limited affinity between the matrix and organo-modified clay particles.

Protein-mediated isolation of plasmid DNA by a zinc finger-glutathione S-transferase affinity linker

The sequence-specific affinity chromatographic isolation of plasmid DNA from crude lysates of E. coli DH5α fermentations is addressed. A zinc finger-GST fusion protein that binds a synthetic oligonucleotide cassette containing the appropriate DNA recognition sequence is described. This cassette was inserted into the Smal site of pUC19 to enable the affinity isolation of the plasmid. It is shown that zinc finger-GST fusion proteins can bind both their DNA recognition sequence and a glutathione-derivatized solid support simultaneously. Furthermore, a simple procedure for the isolation of such plasmids from clarified cell lysates is demonstrated. Cell lysates were clarified by cross-flow Dean vortex microfiltration, and the permeate was incubated with zinc finger-GST fusion protein. The resulting complex was adsorbed directly onto glutathione-Sepharose. Analysis of the glutathione-eluted complex showed that plasmid DNA had been recovered, largely free from contamination by genomic DNA or bacterial cell proteins. © 2002 Wiley Periodicals, Inc.

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.

Class I T-cell epitope prediction:improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

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).