Molecular docking algorithms are computational methods that predict the binding site and docking pose of specified ligands with a protein target. They have proliferated in recent years, due to the explosion of structural data in biology. Oxdock is an algorithm that uses various techniques to simplify this complex task, the most significant being the use of a multiscale approach to analyse the problem using a simple representation in the early stages. Oxdock is shown to be a very useful tool in computational biology, as exemplified by two cases. The first case is the analysis of the NMDA subclass of neuronal glutamate receptors and the subsequent elucidation of their function. The second is the investigation of the newly discovered plant glutamate receptors and the clarification of their natural ligands. The results in both instances open new areas of research into exciting areas of biology. Despite its effectiveness in solving many problems, Oxdock does fail in a number of circumstances. It is thus important to devise a new and improved method for molecular docking. This is achieved by combining the speed of the multiscale approach with the optimising ability of Evolutionary Programming. This yields an algorithm that is shown to be precise, accurate and specific. The new algorithm, Eve, is then modified to illustrate its potential in both lead optimisation and de novo drug design. These capacities, combined with its ability to predict the location of binding sites and the docking pose of a ligand, highlight the promise of computational methods in solving problems in many areas of biological chemistry.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:419311 |
| Date | January 2005 |
| Creators | Huggins, David John |
| Contributors | Grant, Guy H. : Richards, William Graham |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:f166d5ec-5085-48b9-838a-626f754f73fb |
Page generated in 0.0019 seconds