Mark Halling-Brown

Objective assessment of cancer genes for drug discovery

Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.

canSAR: an integrated cancer public translational research and drug discovery resource

canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at .

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale †

The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system—that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it—together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?

GPCRTree: online hierarchical classification of GPCR function

BackgroundG protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.FindingsUsing techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a proteins physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level.ConclusionA selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/.

A computational Grid framework for immunological applications

We have developed a computational Grid that enables us to exploit through a single interface a range of local, national and international resources. It insulates the user as far as possible from issues concerning administrative boundaries, passwords and different operating system features. This work has been undertaken as part of the European Union ImmunoGrid project whose aim is to develop simulations of the immune system at the molecular, cellular and organ levels. The ImmunoGrid consortium has members with computational resources on both sides of the Atlantic. By making extensive use of existing Grid middleware, our Grid has enabled us to exploit consortium and publicly available computers in a unified way, notwithstanding the diverse local software and administrative environments. We took 40 000 polypeptide sequences from 4000 avian and mammalian influenza strains and used a neural network for class I T-cell epitope prediction tools for 120 class I alleles and haplotypes to generate over 14 million high-quality protein–peptide binding predictions that we are mapping onto the three-dimensional structures of the proteins. By contrast, the Grid is also being used for developing new methods for class T-cell epitope predictions, where we have running batches of 120 molecular dynamics free-energy calculations.

Computational Simulations of the Immune System for Personalized Medicine: State of the Art and Challenges

The main goal of pharmacogenomics is to study the effects of genetic variation on patient responses to therapies. Its applications range from the evaluation of safety and efficacy of treatment to the optimization of therapies and therapeutic regimens. Pharmacogenomics is becoming increasingly important in immunology, for the development of new generation vaccines, immunotherapies and transplantation. The human immune system is a complex and adaptive learning system which operates at multiple levels: molecules, cells, organs, organisms, and groups of organisms. Immunologic research, both basic and applied, needs to deal with this complexity. We increasingly use mathematical modeling and computational simulation in the study of the immune system and immune responses. Thus, quantitative models that appropriately capture the complexity in architecture and function of the immune system are an integral component of the personalized medicine efforts. In silico models of the immune system can provide answers to a variety of questions, including understanding the general behavior of the immune system, the course of disease, effects of treatment, analysis of cellular and molecular interactions, and simulation of laboratory experiments. We herein present the ImmunoGrid project that integrates molecular and system level models of the immune system and processes for in silico studies of the immune function. The ImmunoGrid simulator uses Grid technologies, enabling computational simulation of the immune system at the natural scale, perform a large number of simulated experiments, capture the diversity of the immune system between individuals, and provide a basis for therapeutic approaches tailored to the individual genetic make-up.

Are bacterial vaccine antigens T-cell epitope depleted?

For many infectious diseases, protective immunity can be elicited by vaccination with pathogen-derived proteins. Peptides derived from these proteins are bound to major histocompatibility complex (MHC) molecules and presented to T-cell receptors to stimulate an immune response. We show here that, paradoxically, bacterial proteins known experimentally to elicit a protective immune response are relatively depleted in peptides predicted to bind to human MHC alleles. We propose three nonconflicting reasons for this: the lack of precision of current predictive software, the low incidence of hydrophobic residues in vaccine antigens or evolutionary pressure exerted on bacteria by the immune system. We suggest that there is little value in predicting candidate vaccines based on high MHC-binding epitope density.

Constructing Computational Pipelines

Many bioinformatics tasks involve creating a computational pipeline from existing software components and algorithms. The traditional approach is to glue components together using scripts written in a programming language such as Perl. However, a new, more powerful approach is emerging that promises to revolutionise the way bioinformaticians create applications from existing components, an approach based on the concept of the scientific workflow. Scientific workflows are created in graphical environments known as workflow management systems. They have many benefits over traditional program scripts, including speed of development, portability, and their suitability for developing complex, distributed applications. This chapter explains how to design and implement bioinformatics workflows using free, Open Source software tools, such as the Taverna workflow management system. We also demonstrate how new and existing tools can be deployed as Web services so that they can be easily integrated into novel computational pipelines using the scientific workflow paradigm.

Proteins accessible to immune surveillance show significant T-cell epitope depletion: Implications for vaccine design

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.