Journal of the American Chemical Society · 2011 · 133 citations · 64 references
Life SciencesMolecular BiologyBiomolecular ModelingStructural BiologyTranscriptionMolecular DynamicsNucleic Acid DynamicsNucleic Acid ModelingPolymerase Chain ReactionRna Polymerase FunctionRna Polymerase StructureAll-atom Molecular DynamicsMarkov State ModelsNucleotide Addition CyclePhosphate Release DynamicsRelease Of Rna PolymeraseRna Polymerase DynamicsSecondary ChannelTrigger Loop
Pyrophosphate ion (PP(i)) release after nucleotide incorporation is a necessary step for RNA polymerase II (pol II) to enter the next nucleotide addition cycle during transcription elongation. However, the role of pol II residues in PP(i) release and the mechanistic relationship between PP(i) release and the conformational change of the trigger loop remain unclear. In this study, we constructed a Markov state model (MSM) from extensive all-atom molecular dynamics (MD) simulations in the explicit solvent to simulate the PP(i) release process along the pol II secondary channel. Our results show that the trigger loop has significantly larger intrinsic motion after catalysis and formation of PP(i), which in turn aids PP(i) release mainly through the hydrogen bonding between the trigger loop residue H1085 and the (Mg-PP(i))(2-) group. Once PP(i) leaves the active site, it adopts a hopping model through several highly conserved positively charged residues such as K752 and K619 to release from the pol II pore region of the secondary channel. These positive hopping sites form favorable interactions with PP(i) and generate four kinetically metastable states as identified by our MSM. Furthermore, our single-mutant simulations suggest that H1085 and K752 aid PP(i) exit from the active site after catalysis, whereas K619 facilitates its passage through the secondary channel. Finally, we suggest that PP(i) release could help the opening motion of the trigger loop, even though PP(i) release precedes full opening of the trigger loop due to faster PP(i) dynamics. Our simulations provide predictions to guide future experimental tests.
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