Integral Membrane Proteins (IMPs) are an important and scientifically interesting class of protein which span the lipid bilayer surrounding cells, cell compartments and many viruses. Molecular Dynamics (MD) simulation has revealed intimate and often highly specific relationships between membrane lipids and IMPs critical to many metabolic and signalling pathways. Meanwhile, the use of Coarse-Grained (CG) MD techniques has extended capabilities of biomolecular simulation to larger proteins over longer time periods. Several tools and resources for biomolecular simulations of IMPs are presented here, as well as two MD studies of specific IMPs. The previously developed MemProtMD pipeline automates the setup of MD simulations of IMPs; major extensions to this are presented here with the MemProtMD database and web server, automating the analysis of IMP simulations. The results of this can be viewed using the MemProtMD web server, an interactive, searchable online resource containing data from simulations of over 3000 experimentally determined IMP structures in explicit lipid bilayers. Using data from analysis of the entire MemProtMD database, MemProtMetrics has been developed to automate identification and orientation of IMP structures from Protein DataBank (PDB) depositions. This is shown to effectively predict membrane protein orientations seen in MD simulations. A tool for identification and classification of membrane lipids is also described, and used to identify over 500 IMPs structures with resolved lipids. CGMD simulations have also been used to assess dependence on side-chain ionisation state of interactions between lipids and two IMPs observed in mass spectrometry experiments. The simulations reveal similar trends to those seen in experiments. Finally, using multi-scale simulations, and through the development of a novel method for altering membrane composition in MD simulations, lipid-specific scramblase activity was shown for a novel structure of the TMEM16K scramblase IMP.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:740927 |
| Date | January 2017 |
| Creators | Newport, Thomas |
| Contributors | Sansom, Mark S. P. ; Stansfeld, Phillip J. |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:b6dc3047-aaf4-4236-8266-7a885fecb5d9 |
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