Bridge helix bending promotes RNA polymerase II backtracking through a critical and conserved threonine residue.

The dynamics of the RNA polymerase II (Pol II) backtracking process is poorly understood. We built a Markov State Model from extensive molecular dynamics simulations to identify metastable intermediate states and the dynamics of backtracking at atomistic detail. Our results reveal that Pol II backtracking occurs in a stepwise mode where two intermediate states are involved. We find that the continuous bending motion of the Bridge helix (BH) serves as a critical checkpoint, using the highly conserved BH residue T831 as a sensing probe for the 3'-terminal base paring of RNA:DNA hybrid. If the base pair is mismatched, BH bending can promote the RNA 3'-end nucleotide into a frayed state that further leads to the backtracked state. These computational observations are validated by site-directed mutagenesis and transcript cleavage assays, and provide insights into the key factors that regulate the preferences of the backward translocation

Constructing kinetic models to elucidate structural dynamics of a complete RNA polymerase II elongation cycle.

The RNA polymerase II elongation is central in eukaryotic transcription. Although multiple intermediates of the elongation complex have been identified, the dynamical mechanisms remain elusive or controversial. Here we build a structure-based kinetic model of a full elongation cycle of polymerase II, taking into account transition rates and conformational changes characterized from both single molecule experimental studies and computational simulations at atomistic scale. Our model suggests a force-dependent slow transition detected in the single molecule experiments corresponds to an essential conformational change of a trigger loop (TL) opening prior to the polymerase translocation. The analyses on mutant study of E1103G and on potential sequence effects of the translocation substantiate this proposal. Our model also investigates another slow transition detected in the transcription elongation cycle which is independent of mechanical force. If this force-independent slow transition happens as the TL gradually closes upon NTP binding, the analyses indicate that the binding affinity of NTP to the polymerase has to be sufficiently high. Otherwise, one infers that the slow transition happens pre-catalytically but after the TL closing. Accordingly, accurate determination of intrinsic properties of NTP binding is demanded for an improved characterization of the polymerase elongation. Overall, the study provides a working model of the polymerase II elongation under a generic Brownian ratchet mechanism, with most essential structural transition and functional kinetics elucidated

Constructing kinetic models to elucidate structural dynamics of a complete RNA polymerase II elongation cycle.

The RNA polymerase II elongation is central in eukaryotic transcription. Although multiple intermediates of the elongation complex have been identified, the dynamical mechanisms remain elusive or controversial. Here we build a structure-based kinetic model of a full elongation cycle of polymerase II, taking into account transition rates and conformational changes characterized from both single molecule experimental studies and computational simulations at atomistic scale. Our model suggests a force-dependent slow transition detected in the single molecule experiments corresponds to an essential conformational change of a trigger loop (TL) opening prior to the polymerase translocation. The analyses on mutant study of E1103G and on potential sequence effects of the translocation substantiate this proposal. Our model also investigates another slow transition detected in the transcription elongation cycle which is independent of mechanical force. If this force-independent slow transition happens as the TL gradually closes upon NTP binding, the analyses indicate that the binding affinity of NTP to the polymerase has to be sufficiently high. Otherwise, one infers that the slow transition happens pre-catalytically but after the TL closing. Accordingly, accurate determination of intrinsic properties of NTP binding is demanded for an improved characterization of the polymerase elongation. Overall, the study provides a working model of the polymerase II elongation under a generic Brownian ratchet mechanism, with most essential structural transition and functional kinetics elucidated

A Two-State Model for the Dynamics of the Pyrophosphate Ion Release in Bacterial RNA Polymerase

The dynamics of the PPi release during the transcription elongation of bacterial RNA polymerase and its effects on the Trigger Loop (TL) opening motion are still elusive. Here, we built a Markov State Model (MSM) from extensive all-atom molecular dynamics (MD) simulations to investigate the mechanism of the PPi release. Our MSM has identified a simple two-state mechanism for the PPi release instead of a more complex four-state mechanism observed in RNA polymerase II (Pol II). We observed that the PPi release in bacterial RNA polymerase occurs at sub-microsecond timescale, which is similar to 3-fold faster than that in Pol II. After escaping from the active site, the (Mg-PPi)(2-) group passes through a single elongated metastable region where several positively charged residues on the secondary channel provide favorable interactions. Surprisingly, we found that the PPi release is not coupled with the TL unfolding but correlates tightly with the side-chain rotation of the TL residue R1239. Our work sheds light on the dynamics underlying the transcription elongation of the bacterial RNA polymerase

Dynamics of Pyrophosphate Ion Release and Its Coupled Trigger Loop Motion from Closed to Open State in RNA Polymerase II

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

The neuronal responses to repetitive acoustic pulses in different fields of the auditory cortex of awake rats.

Cortical representation of time-varying features of acoustic signals is a fundamental issue of acoustic processing remaining unresolved. The rat is a widely used animal model for auditory cortical processing. Though some electrophysiological studies have investigated the neural responses to temporal repetitive sounds in the auditory cortex (AC) of rats, most of them were conducted under anesthetized condition. Recently, it has been shown that anesthesia could significantly alter the temporal patterns of neural response. For this reason, we systematically examined the single-unit neural responses to click-trains in the core region of rat AC under awake condition. Consistent with the reports on anesthetized rats, we confirmed the existence of characteristic tonotopic organizations, which were used to divide the AC into anterior auditory field (AAF), primary auditory cortex (A1) and posterior auditory field (PAF). We further found that the neuron's capability to synchronize to the temporal repetitive stimuli progressively decreased along the anterior-to-posterior direction of AC. The median of maximum synchronization rate was 64, 32 and 16 Hz in AAF, A1 and PAF, respectively. On the other hand, the percentage of neurons, which showed non-synchronized responses and could represent the stimulus repetition rate by the mean firing rate, increased from 7% in AAF and A1 to 20% in PAF. These results suggest that the temporal resolution of acoustic processing gradually increases from the anterior to posterior part of AC, and thus there may be a hierarchical stream along this direction of rat AC

Application of Markov State Models to simulate long timescale dynamics of biological macromolecules

Conformational changes of proteins are an*Author contributed equally with all other contributors. essential part of many biological processes such as: protein folding, ligand binding, signal transduction, allostery, and enzymatic catalysis. Molecular dynamics (MD) simulations can describe the dynamics of molecules at atomic detail, therefore providing a much higher temporal and spatial resolution than most experimental techniques. Although MD simulations have been widely applied to study protein dynamics, the timescales accessible by conventional MD methods are usually limited to timescales that are orders of magnitude shorter than the conformational changes relevant for most biological functions. During the past decades great effort has been devoted to the development of theoretical methods that may enhance the conformational sampling. In recent years, it has been shown that the statistical mechanics framework provided by discrete-state and -time Markov State Models (MSMs) can predict long timescale dynamics from a pool of short MD simulations. In this chapter we provide the readers an account of the basic theory and selected applications of MSMs. We will first introduce the general concepts behind MSMs, and then describe the existing procedures for the construction of MSMs. This will be followed by the discussions of the challenges of constructing and validating MSMs, Finally, we will employ two biologically-relevant systems, the RNA polymerase and the LAO-protein, to illustrate the application of Markov State Models to elucidate the molecular mechanisms of complex conformational changes at biologically relevant timescales

A two-state mechanism for the PP_i release in RNAP revealed by the MSM.

<p>(A) Two metastable states (S1 and S2) are identified. 500 randomly selected conformations from each metastable state are superimposed and represented with cyan and green spheres for S1 and S2 respectively. Each sphere indicates the coordinate of the center of mass of the PP_i group. (B) The two metastable states are displayed as two circles, and the size of these circles is proportional to the equilibrium populations of the S1 (12.6%±0.02%) and S2 (87.4%±0.02%) state, (C) Key interactions between (Mg-PP_i)²⁻ group and RNAP in each state are displayed. (D) Conservation analysis of the positively charged residues that interact with the (Mg-PP_i)²⁻ group among different species. The sequence alignment was performed using the online software ClustalW2 (<a href="http://www.ebi.ac.uk/Tools/msa/clustalw2/" target="_blank">http://www.ebi.ac.uk/Tools/msa/clustalw2/</a>).</p