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|Title:||Mixed-probe simulation and probe-derived surface topography map analysis for ligand binding site identification||Authors:||Sayyed-Ahmad, Abdallah
Gorfe, Alemayehu A.
|Issue Date:||Mar-2017||Publisher:||ACS||Source:||Sayyed-Ahmad A and Gorfe AA “Mixed-probe simulation and probe-derived surface topography map analysis for ligand binding site identification”, Journal of chemical theory and computation, 13(4), 1851-1861, 2017.||Abstract:||Membrane proteins represent a considerable fraction of pharmaceutical drug targets. A computational technique to identify ligand binding pockets in these proteins is therefore of great importance. We recently reported such a technique called pMD-membrane that utilizes small molecule probes to detect ligand binding sites and surface hotspots on membrane proteins based on probe-based molecular dynamics simulation. The current work extends pMD-membrane to a diverse set of small organic molecular species that can be used as cosolvents during simulation of membrane proteins. We also describe a projection technique for globally quantifying probe densities on the protein surface and introduce a technique to construct surface topography maps directly from the probe-binding propensity of surface residues. The map reveals surface patterns and geometric features that aid in filtering out high probe density hotspots lacking pocketlike characteristics. We demonstrate the applicability of the extended pMD-membrane and the new analysis tool by exploring the druggability of full-length G12D, G12V, and G13D oncogenic K-Ras mutants bound to a negatively charged lipid bilayer. Using data from 30 pMD-membrane runs conducted in the presence of a 2.8 M cosolvent made up of an equal proportion of seven small organic molecules, we show that our approach robustly identifies known allosteric ligand binding sites and other reactive regions on K-Ras. Our results also show that accessibility of some pockets is modulated by differential membrane interactions.||Description:||An article publishe in : Journal of Chemical Theory and Computation, 2017, 13, pp. 1851−1861||URI:||http://hdl.handle.net/20.500.11889/5435|
|Appears in Collections:||Fulltext Publications|
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