Computational modelling reveals feedback redundancy within the epidermal growth factor receptor/extracellular-signal regulated kinase signalling pathway.

The epidermal growth factor receptor (EGFR) activated extracellular-signal regulated kinase (ERK) pathway is a central cell signalling pathway that mediates many biological responses including cell proliferation, transformation, survival and motility. Deregulation of the pathway either through mutation of components or overexpression of EGFRs is associated with several forms of cancer. Under normal conditions, EGF stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown, whereas under cancerous conditions, the ERK signal cannot be shutdown and is sustained. Computational modelling techniques have been used to investigate the signalling dynamics of the EGFR/ERK pathway, focusing on identifying the key processes involved in signal termination and what role the ERK to son of sevenless (SOS) negative feedback loop plays in generating a transient response. This model predicts that this negative feedback loop is not needed to achieve a transient activation of ERK as the process of receptor degradation alone is enough to terminate the signal. Importantly, the behaviour and predictions of this model are verified with laboratory data, as is essential for modern systems biology approaches. Further analysis showed that the feedback loop and receptor degradation were both redundant processes, as each could compensate for the absence of the other. This led to the prediction that in the case of a receptor which is not degraded, such as the insulin receptor, the negative feedback loop to SOS will actually be essential for a transient response to be achieved. Overall, the results shed new light on the role of negative feedback in EGF receptor signalling and suggest that different receptors are dependent on different features within the ERK pathway when relaying their signals.

Protein structure topological comparison, discovery and matching service.

UNLABELLED: We describe a fold level fast protein comparison and motif matching facility based on the TOPS representation of structure. This provides an update to a previous service at the EBI, with a better graph matching with faster results and visualization of both the structures being compared against and the common pattern of each with the target domain. AVAILABILITY: Web service at http://balabio.dcs.gla.ac.uk/tops or via the main TOPS site at http://www.tops.leeds.ac.uk. Software is also available for download from these sites.

Multiple structural alignment for distantly related all beta structures using TOPS pattern discovery and simulated annealing.

Topsalign is a method that will structurally align diverse protein structures, for example, structural alignment of protein superfolds. All proteins within a superfold share the same fold but often have very low sequence identity and different biological and biochemical functions. There is often significant structural diversity around the common scaffold of secondary structure elements of the fold. Topsalign uses topological descriptions of proteins. A pattern discovery algorithm identifies equivalent secondary structure elements between a set of proteins and these are used to produce an initial multiple structure alignment. Simulated annealing is used to optimize the alignment. The output of Topsalign is a multiple structure-based sequence alignment and a 3D superposition of the structures. This method has been tested on three superfolds: the beta jelly roll, TIM (alpha/beta) barrel and the OB fold. Topsalign outperforms established methods on very diverse structures. Despite the pattern discovery working only on beta strand secondary structure elements, Topsalign is shown to align TIM (alpha/beta) barrel superfamilies, which contain both alpha helices and beta strands.

Interactive visualization and exploration of relationships between biological objects.

Genome sequencing and microarray technology produce ever-increasing amounts of complex data that need analysis. Visualization is an effective analytical technique that exploits the ability of the human brain to process large amounts of data. Here, we review traditional visualization methods based on clustering and tree representation, and also describe an alternative approach that involves projecting objects onto a Euclidean space in a way that reflects their structural or functional distances. Data are visualized without preclustering and can be dynamically explored by the user using 'virtual-reality'. We illustrate this approach with two case studies from protein topology and gene expression.