bioRxivBiophysics
A benzene-mapping approach for uncovering cryptic pockets in membrane-bound proteins
Abstract
Molecular dynamics (MD) simulations in combination with small organic probes present in the solvent have previously been used as a method to reveal cryptic pockets that may not have been identified in experimental structures. We report such a method implemented within the CHARMM forcefield to effectively explore cryptic pockets on the surfaces of membrane-embedded proteins using benzene as a probe molecule. This relies on modified non-bonded parameters in addition to repulsive potentials between membrane lipids and benzene molecules. The method was tested on part of the outer shell of the dengue virus (DENV), for which research into a safe and effective neutralizing antibody or drug molecule is still ongoing. In particular, the envelope (E) protein, associated with the membrane (M) protein, is a lipid membrane-embedded complex which forms a dimer in the mature viral envelope. Solvent mapping was performed for the full, membrane-embedded EM protein complex and compared with similar calculations performed for the isolated, soluble E protein ectodomain dimer in solvent. Ectodomain-only simulations with benzene exhibited unfolding effects not observed in the more physiologically relevant membrane-associated systems. A cryptic pocket which has been experimentally shown to bind n-octyl-{beta}-D-glucoside detergent was consistently revealed in all benzene-containing simulations. The addition of benzene also enhanced the flexibility and hydrophobic exposure of cryptic pockets at a key, functional interface in the E protein, and revealed a novel, potentially druggable pocket that may be targeted to prevent conformational changes associated with viral entry into the cell. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/025163v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@abe537org.highwire.dtl.DTLVardef@155a741org.highwire.dtl.DTLVardef@57bff6org.highwire.dtl.DTLVardef@fbee3a_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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