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Proc Natl Acad Sci U S A. Sep 7, 2010; 107(36): 15745–15750.
Published online Aug 23, 2010. doi:  10.1073/pnas.1009898107
PMCID: PMC2936645
Biophysics and Computational Biology

RNA polymerase II trigger loop residues stabilize and position the incoming nucleotide triphosphate in transcription

Abstract

A structurally conserved element, the trigger loop, has been suggested to play a key role in substrate selection and catalysis of RNA polymerase II (pol II) transcription elongation. Recently resolved X-ray structures showed that the trigger loop forms direct interactions with the β-phosphate and base of the matched nucleotide triphosphate (NTP) through residues His1085 and Leu1081, respectively. In order to understand the role of these two critical residues in stabilizing active site conformation in the dynamic complex, we performed all-atom molecular dynamics simulations of the wild-type pol II elongation complex and its mutants in explicit solvent. In the wild-type complex, we found that the trigger loop is stabilized in the “closed” conformation, and His1085 forms a stable interaction with the NTP. Simulations of point mutations of His1085 are shown to affect this interaction; simulations of alternative protonation states, which are inaccessible through experiment, indicate that only the protonated form is able to stabilize the His1085-NTP interaction. Another trigger loop residue, Leu1081, stabilizes the incoming nucleotide position through interaction with the nucleotide base. Our simulations of this Leu mutant suggest a three-component mechanism for correctly positioning the incoming NTP in which (i) hydrophobic contact through Leu1081, (ii) base stacking, and (iii) base pairing work together to minimize the motion of the incoming NTP base. These results complement experimental observations and provide insight into the role of the trigger loop on transcription fidelity.

Keywords: nucleotide selection, transcription fidelity

The process by which gene-encoding DNA is transcribed into a complimentary messenger RNA sequence, termed transcription, is carried out in the eukaryotic cell by complex molecular machinery in which RNA polymerase II (pol II) is the key player (13). Nucleotide addition and translocation proceeds through a proposed four-step cycle (46). A nucleotide triphosphate (NTP) enters the enzyme by the entry site (“E” site). The NTP then rotates into the nucleotide addition site (“A” site) where it is retained if complementary with the template DNA base. Ligation to the 3′ end of the nascent RNA chain results in the pretranslocation state (7). Finally, the enzyme translocates to the i + 1 position on the template DNA, leaving the A site empty in the posttranslocation complex and ready for the next round. However, the mechanism of selection and catalysis is far from understood (4).

A structurally conserved element, the trigger loop, has been suggested to play a key role in both selection of the correctly matched NTP and catalysis of transcription elongation in eukaryotes and prokaryotes (810). Structures of the transcribing complex reported by Wang et al., (8) reveal the trigger loop in a previously unseen “closed” conformation, making a network of interactions with the correctly matched NTP in the A site. Mismatched NTPs are proposed to be destabilizing, so that the trigger loop does not achieve the closed conformation that allows it to trigger catalysis. Mutation of the trigger loop in both eukaryotic pol II and the closely related bacterial enzyme RNAP alters the elongation behavior of the enzyme (1115). However, mutation does not always render the enzyme inactive, even upon deletion of the entire trigger loop (12, 16). Thus, two modes of transcription elongation emerge: a fast, high fidelity, trigger loop-dependent mode and a slow, low fidelity trigger loop-independent mode.

In the closed conformation, the trigger loop directly contacts the β-phosphate and base of the matched NTP through residues His1085 and Leu1081, respectively (8). Specifically, the side chain of His1085 is precisely positioned to directly interact with the β-phosphate oxygen of the incoming NTP, which helps to seal off the active site conformation through electrostatic interactions. Moreover, it is also suggested that the protonated form of His1085 is able to act as an electron-withdrawing group and thereby facilitate the formation of the phosphodiester bond upon nucleophillic attack (8, 17). Thus, the His1085-NTP interaction is hypothesized to couple selection with catalysis, greatly enhancing the rate at which correct NTPs are incorporated over the rate of 2′-deoxy-NTP or incorrectly matched NTP, as the energy of correct base pairing alone cannot account for the enzyme’s remarkable selectivity. Substitution of tyrosine for His1085 by Kaplan et al. (16) (which preserves the ability to hydrogen bond) exhibited severely deficient elongation at saturating concentrations of NTP; the impairment was smaller for the addition of 2′-deoxy-NTP, which indicates that His1085 plays little, if any, role in selection of an incorrect NTP. Substitution of phenylalanine for His1085 was lethal (16).

Leu1081 forms a hydrophobic contact with the incoming NTP (8), which is also expected to play important roles in stabilizing the active site conformation and positioning the base. In the bacterial RNAP, mutation of the nonpolar residue at the same position of the trigger loop (M932A) increases the intrinsic pausing duration by an order of magnitude (18). In addition, Leu1081 is also suggested to be important in translocation. In a recent cocrystal structure of the pol II elongation complex with the antibiotic α-amanitin in the active site, Leu1081 forms a wedged configuration into the bridge helix, which may eventually trigger the forward translocation of the hybrid (19).

Although experimental studies provide a wealth of information, it is difficult for experiments to provide dynamic information at atomic resolution. Through use of a physics-based force field, all-atom molecular dynamics (MD) simulations can model detailed atomistic interactions and provide dynamic information. Thus, computer simulations may complement structural and biochemical experiments by providing information inaccessible to experiments. In the current study, computer simulation may help address many remaining important questions. For example, the effect of the protonation state change of His1085 induced by the environment is difficult to investigate experimentally. It is hypothesized that His1085 must be protonated in order to act as an electron-withdrawing group, facilitating the SN2 chemical reaction (8). Simulation is also able to provide information on dynamics and flexibility, which experiments do not. Finally, in silico we can design computer experiments to separately investigate different components that contribute to the stability of the pol II active site conformation, for example, separating the interactions contributing to the stability of the incoming NTP base into hydrophobic contact, base-pairing, and base stacking.

In this study we perform a total of 500-ns molecular dynamics simulations of the pol II elongation complex in explicit water (~370,000 atoms) to study two trigger loop residues, His1085 and Leu1081. Our goal is to gain a detailed understanding of the roles of these two critical residues in positioning and stabilizing the incoming NTP in a catalytically competent conformation.

Results

We first investigate if RNA pol II transcription elongation complexes containing a variety of protein and nucleic acids elements (see Fig. S1) are stable in molecular dynamics simulations. As shown in Fig. S2, the rmsd for different elements during the 2.2 ns of the simulation and averaged over 20 runs is always smaller than 4 Å. With more than 3,000 residues and a relatively flexible protein and nucleic acid structure, we consider this to be small. We extend one simulation to 46 ns, and even over this long time period the rmsd for all the atoms is still reasonable (< 6.5 ).

1. Role of His1085 in Transcription Elongation.

His1085 stabilizes the active site conformation compared to point mutations.

Trigger loop residue His1085 has been shown to play an important role in pol II function. In vivo, point mutation to Phe results in total inviability, whereas point mutation to Tyr induces a severe growth defect (16). Further in vitro studies indicated that the defective growth seen in the Tyr mutant is actually caused by defects in pol II elongation (16). However, the molecular mechanisms by which these point mutations alter the transcription elongation rate remain unclear. Therefore, we first perform computer simulations to investigate these two single point His1085 mutations (see Fig. 1).

Fig. 1.
A schematic view of the locations of His1085 and Leu1081 in the RNA pol II active site. RNA is shown in red, template DNA in cyan, and the trigger loop in magenta. Three different protonation states for His1085 are investigated in our simulations: HIP ...

When His1085 is mutated to Phe, the interactions between Phe and NTP are totally lost in our simulations. Fig. 2 shows that the distance between the Pβ atom of NTP and the center of mass of the aromatic ring is generally larger than 10 Å; the percentage of the stable conformations where the minimum distance between any pair of atoms from Phe and GTP is less than 3.5 Å is also negligible (0.2%, see Table S1). The mutation of His1085 to Phe not only causes the loss of hydrogen bonding interactions, but also causes steric repulsion as the six-membered aromatic ring in Phe is bigger than the five-membered imidazole ring in His. Our simulation results explain why this single point mutation can render the enzyme totally inactive (16).

Fig. 2.
(A) Definition of the distance, d, between the Pβ atom in GTP and the center of mass of the imidazole ring in His1085. Histograms of the distance distribution for systems with different protonation states of His1085 and single mutations of His1085 ...

Simulation shows substitution of His1085 to Tyr apparently has higher stability than the mutation to Phe. The distance distribution in Fig. 2 has two peaks, one at ~6  and another at ~8 . The hydrogen bond existence map in Fig. 3 also indicates that a fraction of Tyr conformations is able to hydrogen bond with the NTP. Interestingly, the majority of these hydrogen bonds are formed between Tyr and oxygen (OB1) of the β-phosphate (see Fig. 3). The larger six-membered aromatic ring of Tyr is more confined in the active site and prefers particular orientations that facilitate a hydrogen bond with the oxygen closer to the base of the NTP. Therefore, the Tyr mutant may negatively affect, but not totally shut down, the pol II elongation. This is consistent with experimental observations that this mutant is still active, but decreases the elongation rate by an order of magnitude compared to the wild-type enzyme (16)

Fig. 3.
Mapping of the existence of hydrogen bonds. For each system, the existence of a certain hydrogen bond is plotted for multiple conformations. A total number of 500 conformations were taken for each system; conformations were sampled every 50 ps ...

The protonation state of His1085 plays an important role in active site stability.

Wang et al. (8) suggested that the protonated imidazole group of His1085 plays a crucial role in the phosphodiester bond formation of RNA synthesis. The protonated histidine can withdraw electron density from the phosphate and facilitate SN2 attack of the RNA 3′-terminal OH group. Furthermore, the interactions between His1085 and NTP can also stabilize the transition state for the SN2 reaction. Similar mechanisms have been observed in many nucleic acid polymerases (17) as well as other biological systems such as serine/threonine metallo-phosphotases (20, 21).

Understanding the nature of the interactions between His1085 and NTP is thus important for understanding the role of His1085 in catalysis as well as in stabilizing the closed conformation. His1085 interacts with the phosphate of NTP through two types of interactions: hydrogen bonds and salt bridges. Hydrogen bonding interactions exist when His1085 takes either the protonated form or one of the unprotonated forms with hydrogen connected toNε (see Fig. 1); salt bridge interactions exist only when the histidine side chain is protonated.

The effect of the histidine protonation state on the His1085-NTP interactions is important, but difficult to investigate experimentally. By designing artificial systems, computer simulations can study the independent contributions from hydrogen bonds and salt bridges to the interaction between His1085 and NTP. Specially, we simulate three protonation states of His1085 as shown in Fig. 1: HIP (protonated Nδ and Nε), HIE (protonated Nε), and HID (protonated Nδ). In addition, we construct an artificially charged state by assigning a positive charge to the Nε atom of HID (HID+).

The protonated form (HIP) is indeed the most stable system in our simulations because it can interact with the phosphate group of NTP through both hydrogen bonding and salt bridge interactions. HIP is stable in most of the conformations (88.8%, Table S1), with the Nε-Hε group generally hydrogen bonding with either OB1 or OG1 atoms on the phosphate group of NTP (see Fig. 3). Significantly less stable interaction between histidine and the phosphate group of NTP is found in the unprotonated form, HIE (39.9%), which is able to form only hydrogen bonds but not salt bridges. The distance distribution for this system as displayed in Fig. 2 is bimodal. Although the major peak is still at ~5 , corresponding to stable interactions, the existence of peaks at larger distances indicates that His1085 is less stable without the salt bridge interactions. These distance distributions, generated from 20 trajectories each, have been shown to reach convergence (see Fig. S3). The comparison between HIE and Tyr is interesting, because both of them form only hydrogen bonds. HIE (39.9% stable conformations) is slightly more stable than Tyr (25.1% stable conformations). This difference may be due to the steric effects caused by the larger size of the aromatic ring in the Tyr side chain. The other His protonation form, HID, behaves like the His1085 to Phe mutation that can form neither hydrogen bonding nor salt bridge interactions with NTP. For this system, only a very small fraction (4.0%) is observed to be stable and the distance distribution peaks around 10 Å (see Fig. 2). Among all the protonation forms, the protonated histidine (HIP) is the only form that can stabilize the closed conformations (more than 50% of the conformations are stable).

In order to compare the contribution of hydrogen bonds and salt bridges to the stability, we designed the artificially protonated form, HID+, which can form salt bridges but not hydrogen bonds (see Fig. 1). As shown in Fig. 2, there is only a single peak at ~5  in the distance probability distribution for HID+. In addition, a larger fraction of conformations (59.8%) is stable for HID+ compared to HIE where only hydrogen bonding interaction is present. These results suggest that the salt bridge contributes more than hydrogen bonding to His1085-NTP interactions.

2. Leu1081 Positions the Incoming Nucleotide Base.

The trigger loop residue, Leu1081, makes hydrophobic contact with the incoming NTP base in the closed conformation (8). Our simulation results show that Leu1081 is actually the least flexible residue of the trigger loop, as measured by root mean square fluctuation of Cα atoms (see Fig. S4). This indicates a strong and stable interaction between Leu1081 and the NTP base. In order to further study this interaction, we investigate the dynamic profile of the incoming NTP using two vertical (up or down the nascent nucleic acid double helix) and one lateral distance (perpendicular to this helix axis in the base-pairing direction) as shown in Fig. 4. Furthermore, we compare the results of the wild-type with two single point mutations: Leu1081Ala and Leu1081Gly, where the hydrophobic interactions between residue 1081 and the incoming NTP base are eliminated. We measure the distance between the Cα atom of the Leu1081 and the center of mass (c.o.m.) of the GTP base as an indicator of the vertical motion between the trigger loop and GTP. Mutation of Leu1081 to Ala alters this distance distribution and induces a larger fluctuation (Fig. 4B). The distance between the c.o.m. of the GTP base and that of the nucleotide base in the -2 position of the nascent RNA chain (the second vertical motion; see Fig. 4C) is unchanged. Lateral motion between the DNA template and the incoming GTP is also unaffected (see Fig. 4A). Mutation to Gly has an even larger effect on the trigger loop to NTP vertical motion, but might also disrupt the trigger loop backbone stability. These results indicate that Leu1081 maintains the correct position of the base by minimizing the vertical motion between the trigger loop and the incoming NTP.

Fig. 4.
(A) Distance distributions as a function of time and distance probability distributions in the wild-type, ALA (Leu1081Ala), GLY (Leu1081Gly), Abasic, and Abasic (3′ RNA) systems are shown in black, red, green, blue, and brown, respectively. Distance ...

To fully understand the nucleotide base positioning, we also investigate the effect of two additional components contributing to the base stability: base pairing and base stacking. In order to separately study these two components, we designed the system with an abasic mutation of either the 3′ terminal RNA nucleotide or the DNA nucleotide at +1 position, which eliminates the base stacking and base pairing, respectively (see Fig. 1). In the wild type, when base pairing is present, there is no lateral motion. Without base pairing, as in the abasic DNA mutant, there is increased lateral motion, greater distance distribution, and higher fluctuations. This also explains the slight increase in the fluctuation of the vertical motion to the trigger loop. A second system, in which the RNA 3′ nucleotide is mutated to abasic, has no base stacking with the incoming NTP. In this system, the lateral distance is unaffected. So, too, is the vertical distance to the trigger loop. However, the other vertical distance between the GTP base and the nucleotide of the nascent RNA chain is greatly increased.

Discussion

In this study, we use a total of 500-ns all-atom molecular dynamics simulations to investigate the role of two important trigger loop residues in the RNA pol II transcription elongation complex. Although this system including as it does explicit solvent is large (~370,000 atoms), it proved to be stable on the nanosecond time scale. This allowed molecular dynamics to provide data that are inaccessible through experimental observation.

We found that the wild-type complex, when simulated in the most likely His1085 protonation state (HIP), forms the most stable interaction with the GTP phosphate group and the trigger loop is stabilized in the closed conformation. Simulations of point mutations of this residue are shown to adversely affect this interaction, which is consistent with experimental data. We found both hydrogen bond interactions and charge–charge interactions contribute to the stabilization of the His1085-NTP interaction. The protonated form of His (HIP) may be the major form in the presence of NTP due to the pKa shift of the imidazole to a higher value due to a negative charge group nearby (in this case, the triphosphate group). This information is very difficult to obtain experimentally, but important for understanding the catalytic reactions. The stabilization of the active site conformation is suggested to have catalytic significance: The closed conformation formed by the trigger loop upon entry of a correctly matched NTP is necessary for the proposed SN2 reaction leading to phosphodiester bond formation (8).

We propose a three-component mechanism for correctly positioning the incoming NTP for selection and catalysis, as shown in Fig. 5. While His1085 stabilizes a catalytically competent trigger loop conformation through interactions with the NTP phosphate group, Leu1081 also stabilizes the incoming nucleotide position through interaction with the base. Leu1081 positions the base by minimizing the vertical motion between the trigger loop and the incoming NTP. This hydrophobic interaction is crucial and thus conserved across species, as in bacterial RNAP, where a methionine residue plays a similar role as leucine in pol II. Eliminating this interaction greatly increases the intrinsic pausing duration (18). Furthermore, we predict that mutation of Leu1081 to Ile or Val, which preserves the hydrophobic contact, may still function properly in transcription elongation. In addition, the base stacking from the 3′ RNA terminal nucleotide, sandwiching with Leu1081, controls the positioning of incoming NTP in the vertical direction. Finally, the template base was found to minimize the lateral motion between the incoming NTP and the template DNA. Therefore, the template base may play important dual roles in transcription: It serves not only as a template for correct base selection, but also contributes to correct nucleotide positioning. The role of base pairing and stacking in positioning the incoming NTP has long been recognized (22). It is found experimentally that a single abasic site to eliminate base pairing serves as a strong block for pol II elongation (23). However, the role of Leu in positioning the incoming NTP has only been recognized recently (8). Our mechanism explains how these three components work together to stabilize and correctly position the incoming NTPs.

Fig. 5.
A proposed three-component mechanism for the base recognition of Pol II. The base stacking from the 3′ RNA terminal nucleotide, sandwiching with Leu1081, maintains the correct positioning of incoming NTP base in the vertical direction. The template ...

Although this is a set of atomic resolved simulations of the whole transcription complex in explicit water, it has some limitations. Because of the size of the system, we cannot sample to convergence, making it impossible to calculate an accurate free energy difference between the different systems studied. We circumvent this problem by focusing on the distribution of distances that are related to experimental observation, by running many short simulations and by estimating the expected errors in all calculated results by bootstrapping with replacement. Our results do qualitatively reflect the relative stabilities of the different systems and do indicate the role of His1085 and Leu1081 in the active site stability. We suggest that there could be an important link between the active site stability and the catalysis and NTP selection rate. The simulation system we set up may be used to investigate other key residues in pol II transcription. The same methodology could be used in targeted studies of other large biologically important protein complexes.

System and Method

Molecular dynamics simulations for the RNA pol II transcription elongation complex were carried out with an all-atom model. The whole transcription elongation complex consists of RNA Pol II, a DNA template, the RNA nascent chain, and GTP. The crystal structure (PDB ID code 2E2H) reported by Wang et al. (8) is used as our initial configuration. We follow a similar procedure to set up the simulation as in our previous study of the RNA pol II backtracked complex (24). Missing residues in the middle of each chain of pol II are filled in by Segmod (25). The pol II elongation complex is solvated in a water box, with nonperiodic water layers at least 7 Å from the pol II surface. The solvated configuration is shown in Fig. S1. Two Mg2+ ions in the catalytic site from the crystal structure (8) are included and 100 Na+ counterions are added to make the HIP system electrically neutral. The solvated system for HIP has 368,799 atoms (other systems have a slight variation in the number of atoms). The ENCAD program (26) is first used for minimization to eliminate bad contacts, and subsequently the GROMACS simulation package (27), renowned for its speed, is used to perform molecular dynamics simulations for this large system. The AMBER03 force field (28) is used for the protein, RNA, DNA, and ions; AMBER03 force field parameters (28) for the phosphate and sugar are also used for the abasic residue with minor modifications; polyphosphate parameters developed by Meagher et al. (29) are used for GTP; and the TIP3P (30) water model is used for the explicit solvent. For the long-range electrostatic interactions, the particle-mesh Ewald method (31) is used with the short-range cutoff of 10 Å. For the van der Waals interactions, a typical 9-Å cutoff is used, and a switch function is applied to make the functions smoothly go to zero at 8 Å. A 10-Å neighbor list is updated every 10 time steps. A time step of 2.0 fs is used, and the lengths of the bonds connecting to hydrogen atoms are constrained. The Nose–Hoover thermostat (32) is used for temperature coupling.

We follow a standard equilibration procedure that includes conjugate gradient minimization and 200-ps constant number of atoms, volume, and temperature MD simulation with position restraints on the solute. The final configurations from equilibration are then used for production simulations. Twenty 2.2-ns simulations with different initial velocities are performed for each of the 10 systems: three protonation states of His1085 (HIP, HIE, and HID), an artificial system with an extra position charge placed on Nδ (HID+), two single point mutations (Phe and Tyr) for His1085, two single point mutations (Gly and Ala) for Leu1081, and two other changes making RNA terminal nucleotide and DNA nucleotide at +1 position abasic. The systems are detailed in Fig. 1. During the first 200 ps of the simulations, we gradually increase the temperature from 50 K to 300 K, and the remaining 2-ns simulations are run at 300 K. We extended one of the molecular dynamics simulations for HIP to 46 ns. Thus, the total simulation time is about 500 ns, and each simulation is run in parallel with 56 cores (Dell PowerEdge 1950 compute nodes) on Stanford’s Bio-X2 supercomputing cluster.

Supplementary Material

Supporting Information:

Acknowledgments.

X.H. acknowledges Hong Kong Research Grants Council DAG09/10.SC03 and University Grants Committee RPC10SC03. M.L, X.H., and D.R.W. acknowledge National Institutes of Health (NIH) Roadmap for Medical Research Grant U54 GM072970 and NIH Grant GM041455 (to M.L). D.W. acknowledges NIH Pathway to Independence Award GM085136 and start-up fund from Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego. R.D.K. acknowledges NIH Grant GM49985. Computing resources were provided by National Science Foundation award CNS-0619926.

Footnotes

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1009898107/-/DCSupplemental.

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