Showing papers by "Vladimir Brusic published in 1999"

A neural network model approach to the study of human TAP transporter.

Abstract: We used an artificial neural network (ANN) computer model to study peptide binding to the human transporter associated with antigen processing (TAP). After validation, an ANN model of TAP-peptide binding was used to mine a database of HLA-binding peptides to elucidate patterns of TAP binding. The affinity of HLA-binding peptides for TAP was found to differ according to the HLA supertype concerned: HLA-B27, -A3 or -A24 binding peptides had high, whereas HLA-A2, -B7 or -B8 binding peptides had low affinity for TAP. These results support the idea that TAP and particular HLA molecules may have co-evolved for efficient peptide processing and presentation. The strong similarity between the sets of peptides bound by TAP or HLA-B27 suggests functional co-evolution whereas the lack of a relationship between the sets of peptides bound by TAP or HLA-A2 is against these particular molecules having co-evolved. In support of these conclusions, the affinities of HLA-A2 and HLA-B7 binding peptides for TAP show similar distributions to that of randomly generated peptides. On the basis of these results we propose that HLA alleles constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3 and -A24) and those that are TAP-inefficient (HLA-A2, -B7 and -B8). Computer modelling can be used to complement laboratory experiments and thereby speed up knowledge discovery in biology. In particular, we provide evidence that large-scale experiments can be avoided by combining initial experimental data with limited laboratory experiments sufficient to develop and validate appropriate computer models. These models can then be used to perform large-scale simulated experiments the results of which can then be validated by further small-scale laboratory experiments.

Knowledge discovery and data mining in biological databases

Abstract: The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

Computational binding assays of antigenic peptides

Abstract: Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1–5%.

Data cleansing for computer models: a case study from immunology

Abstract: Knowledge discovery from databases (KDD) in biology largely depends on the use of accurate computer models of biological processes. KDD applications in immunology include the discovery of vaccine targets and new functional relations within the immune system. We describe a process of development and refinement of artificial neural network models of the human HLA-DR1 molecule, useful for the discovery of peptide vaccines. High accuracy of these models was achieved by data cleansing techniques and by cyclical retraining using new data.

Artificial neural network applications in immunology

Abstract: Artificial neural network (ANN) applications in immunology include simulations of peptide binding to histocompatibility complex molecules, which present peptides for recognition by the immune system. These peptides are derived from protein antigens and represent prime targets for vaccine discovery. ANN models have proven superior when compared to the alternative models. Applications of ANN models help minimise the number of necessary wet-lab experiments. In this article we describe three specific applications in which targets of immune recognition have been determined from diabetes-, melanoma-, and malaria-related antigens.

SLAD-An Integrated Swine MHC Database:A Case Study of Data Warehousing in Molecular Immunology

Abstract: “Outcomes Research” in molecular immunology is driven by faster, cheaper and increasingly sophisticated methods such as miniaturisation, automation, and data integration. The latter is a prerequisite for efficient information analysis, knowledge discovery, and eventually research planning. The data warehousing approach has been successfully applied for managing clinical data [1], but rarely in exploratory biological research. In order to clarify aspects of data warehousing in molecular immunology we constructed a small model database of swine major histocompatibility antigens (swine MHC or SLA) using warehousing principles.