Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model.
AuthorsUnarta, Ilona Christy
Cheung, Peter Pak-Hang
KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Embargo End Date2021-10-22
Permanent link to this recordhttp://hdl.handle.net/10754/668915
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AbstractTo initiate transcription, the holoenzyme (RNA polymerase [RNAP] in complex with σ factor) loads the promoter DNA via the flexible loading gate created by the clamp and β-lobe, yet their roles in DNA loading have not been characterized. We used a quasi-Markov State Model (qMSM) built from extensive molecular dynamics simulations to elucidate the dynamics of Thermus aquaticus holoenzyme’s gate opening. We showed that during gate opening, β-lobe oscillates four orders of magnitude faster than the clamp, whose opening depends on the Switch 2’s structure. Myxopyronin, an antibiotic that binds to Switch 2, was shown to undergo a conformational selection mechanism to inhibit clamp opening. Importantly, we reveal a critical but undiscovered role of β-lobe, whose opening is sufficient for DNA loading even when the clamp is partially closed. These findings open the opportunity for the development of antibiotics targeting β-lobe of RNAP. Finally, we have shown that our qMSMs, which encode non-Markovian dynamics based on the generalized master equation formalism, hold great potential to be widely applied to study biomolecular dynamics.
CitationUnarta, I. C., Cao, S., Kubo, S., Wang, W., Cheung, P. P.-H., Gao, X., … Huang, X. (2021). Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model. Proceedings of the National Academy of Sciences, 118(17), e2024324118. doi:10.1073/pnas.2024324118
SponsorsX.H. was supported by the Hong Kong Research Grant Council (16303919, 16307718, AoE/P-705/16, AoE/M-09/12, and T13-605/18-W) and the Hong Kong Innovation and Technology Commission (ITCPD/17-9 and ITC-CNERC14SC01). X.G. was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under Awards FCC/1/1976-23, FCC/1/1976-26, URF/1/4098-01-01, and REI/1/0018-01-01. This research made use of the computing resources of the Supercomputing Laboratory at KAUST and the X-GPU cluster supported by the Hong Kong Research Grant Council Collaborative Research Fund C6021-19EF.
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