%0 Journal Article
%T Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection
%A Marc N Offman
%A Alexander L Tournier
%A Paul A Bates
%J BMC Structural Biology
%D 2008
%I BioMed Central
%R 10.1186/1472-6807-8-34
%X In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed.This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA.Impressive progress in protein structure modelling has been achieved over the last decade; however, improvement between subsequent rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP) is often considered to be modest [1,2]. Given the current accuracy, protein models are useful for qualitative analysis and decision-making in support of a wide range of experimental work. High accuracy modelling is essential for important applications such as, molecular replacement experiments [3-5], function predictions [6] and virtual drug screening [7]. Modelling techniques, however, are still not accurate enough to close the gap between known protein sequences (approximately 5 million non redundant) and solved protein structures (approximately 50,000).Regardless of the current limitations of modelling, two very encouraging observations have been made from the CASP7 results [1,8,9]. First, the gap between the quality of fully automated and manual modelling techniques has narrowed and second, improvement beyond the best template is achieved more frequently.Modern template-based modelling pipelines can be divided into a number of common steps. A typical pipeline starts with template identification and alignment construction. In the next step, models are built using single templates, multiple templates or template fragments. The resulting models are then o
%U http://www.biomedcentral.com/1472-6807/8/34