[HTML][HTML] Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection
MN Offman, AL Tournier, PA Bates - BMC Structural Biology, 2008 - Springer
MN Offman, AL Tournier, PA Bates
BMC Structural Biology, 2008•SpringerBackground Automatic protein modelling pipelines are becoming ever more accurate; this
has come hand in hand with an increasingly complicated interplay between all components
involved. Nevertheless, there are still potential improvements to be made in template
selection, refinement and protein model selection. Results 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 …
has come hand in hand with an increasingly complicated interplay between all components
involved. Nevertheless, there are still potential improvements to be made in template
selection, refinement and protein model selection. Results 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 …
Background
Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.
Results
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.
Conclusion
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.
Springer