Selective Silencing of Disease-Associated B Lymphocytes from Hashimoto's Thyroiditis Patients by Chimeric Protein Molecules.

Hashimoto's thyroiditis is one of the most common endocrine disorders, affecting up to 20% of the adult population. No treatment or prevention exists except hormonal substitution for hypothyroidism. We hypothesize that it may be possible to selectively suppress anti-thyroglobulin (Tg) IgG antibody-producing B lymphocytes from HT patients by a chimeric protein molecule containing a monoclonal antibody specific for the human inhibitory receptor CR1, coupled to peptide epitopes derived from Tg protein.

Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer's disease.

Many gaps in our understanding of Alzheimer's disease remain despite intense research efforts. One such prominent gap is the mechanism of Tau condensation and fibrillization. One viewpoint is that positively charged Tau is condensed by cytosolic polyanions.

Bridging solvent molecules mediate RNase A - Ligand binding.

Due to its high catalytic activity and readily available supply, ribonuclease A (RNase A) has become a pivotal enzyme in the history of protein science. Moreover, this great interest has carried over to computational chemistry and molecular dynamics, where RNase A has become a model system for various types of studies, all the while being an important drug design target in its own right. Here, we present a detailed molecular dynamics study of RNase-ligand binding involving 22 compounds, spanning nearly five orders of magnitude in affinity, and totaling 8.

Suppression of Disease-Associated B Lymphocytes by GAD65 Epitope-Carrying Protein-Engineered Molecules in a Streptozotocin-Induced Mouse Model of Diabetes.

Type 1 diabetes mellitus is an autoimmune syndrome defined by the presence of autoreactive T and B cells, which results in destruction of insulin-producing beta cells. Autoantibodies against GAD65 (glutamic acid decarboxylase 65)-a membrane-bound enzyme on pancreatic beta cells, contribute to beta cells' destruction and the loss of pancreatic functions. Mouse FcγRIIb on B lymphocytes possesses an inhibitory effect on the activity of these cells.

In silico prediction of cancer immunogens: current state of the art.

Cancer kills 8 million annually worldwide. Although survival rates in prevalent cancers continue to increase, many cancers have no effective treatment, prompting the search for new and improved protocols. Immunotherapy is a new and exciting addition to the anti-cancer arsenal.

Computer aided selection of candidate vaccine antigens.

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods.

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

Background: 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.

Methodology/findings: Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model.

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.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods.

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression.

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell.

VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines.

Background: Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach--such as speed and cost efficiency--its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens.

Using databases and data mining in vaccinology.

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties.

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner.

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.

Modeling the peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique.

A set of 38 epitopes and 183 non-epitopes, which bind to alleles of the HLA-A3 supertype, was subjected to a combination of comparative molecular similarity indices analysis (CoMSIA) and soft independent modeling of class analogy (SIMCA). During the process of T cell recognition, T cell receptors (TCR) interact with the central section of the bound nonamer peptide; thus only positions 4-8 were considered in the study. The derived model distinguished 82% of the epitopes and 73% of the non-epitopes after cross-validation in five groups.

Synthesis and antiproliferative activity of benzo[d]isothiazole hydrazones.

Several benzo[d]isothiazole hydrazones have been evaluated for their potential antiretroviral activity. Since a number of these compounds were found to be inactive against viruses, but showed cytotoxicity at micromolar concentrations against the human CD4+ lymphocytes (MT-4) that were used to support HIV-1 growth, they were further tested for antiproliferative activity. The compounds resulted as being cytotoxic for MT-4 cells and new derivatives which were rationally designed and synthesized, were tested for antiproliferative activity against several leukaemia and solid tumour cell lines.

MHCPred 2.0: an updated quantitative T-cell epitope prediction server.

Unlabelled: The accurate computational prediction of T-cell epitopes can greatly reduce the experimental overhead implicit in candidate epitope identification within genomic sequences. In this article we present MHCPred 2.0, an enhanced version of our online, quantitative T-cell epitope prediction server.

EpiJen: a server for multistep T cell epitope prediction.

Background: The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes.

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.

AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data.

AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries.

Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201.

Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) analysis of proteins and peptides. In the present study, descriptors were used to characterize peptides binding to the human MHC allele HLA-A0201. Two sets of amino acid descriptors were chosen: 93 descriptors taken from the amino acid descriptor database AAindex and the z descriptors defined by Wold and Sandberg.

In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study.

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 prediction of peptide-major histocompatibility complex (MHC) affinity. A three-dimensional quantitative structure-activity relationship (3D-QSAR) for the prediction of peptide binding to class I MHC molecules was established using the comparative molecular similarity index analysis (CoMSIA) method. Three MHC alleles were studied: H2-D(b), H2-K(b), and H2-K(k).

Towards the chemometric dissection of peptide--HLA-A*0201 binding affinity: comparison of local and global QSAR models.

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity chi indices, kappa shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume.

In silico identification of supertypes for class II MHCs.

The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints.

Quantitative structure-activity relationships and the prediction of MHC supermotifs.

The underlying assumption in quantitative structure-activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele.