Transcription is a central step in gene expression, in which the DNA template is processively read by RNA polymerase II (Pol II), synthesizing a complementary messenger RNA transcript. At each cycle, Pol II moves exactly one register along the DNA, a process known as translocation. Although X-ray crystal structures have greatly enhanced our understanding of the transcription process, the underlying molecular mechanisms of translocation remain unclear. Here we use sophisticated simulation techniques to observe Pol II translocation on a millisecond timescale and at atomistic resolution. We observe multiple cycles of forward and backward translocation and identify two previously unidentified intermediate states. We show that the bridge helix (BH) plays a key role accelerating the translocation of both the RNA:DNA hybrid and transition nucleotide by directly interacting with them. The conserved BH residues, Thr831 and Tyr836, mediate these interactions. To date, this study delivers the most detailed picture of the mechanism of Pol II translocation at atomic level.Markov state model | molecular dynamics | trigger loop T he RNA polymerase is the central component of gene expression in all living organisms, transferring genetic information from DNA to RNA. In eukaryotes, the RNA polymerase II (Pol II) enzyme is responsible for transcribing DNA into messenger RNA. In the past decade, a number of X-ray crystallographic structures of Pol II have been obtained at different stages of the transcription process, providing a static picture of how this complex machine performs its function (1, 2). Transcription is a multistep process consisting of initiation, elongation, and termination, where elongation is composed of consecutive nucleotide addition cycles (NACs). In each NAC, the NTP substrate first diffuses into Pol II active site through the secondary channel (3-5) or alternatively the main channel (6). Upon correct NTP binding to the Pol II active site, the trigger loop (TL) conformation switches from an inactive open state to an active closed state (7). The closure of the active site subsequently facilitates the catalysis of the nucleotide addition reaction (7), followed by release of the pyrophosphate ion (PPi). To proceed to the next NAC, Pol II must translocate from a pretranslocation state, in which the active site is still occupied by the newly added nucleotide at 3′-RNA, to a posttranslocation state. During translocation, the template DNA and RNA must move by exactly one register, once again creating a free insertion site (i site) (1,2,5,(8)(9)(10)(11)(12)(13).Although static snapshots of X-ray structures of Pol II pretranslocation and posttranslocation states are valuable, the dynamics underlying the fundamental RNA polymerase translocation mechanism remain poorly understood (14). Two models of translocation have been proposed based on structural, biochemical, and genetic approaches. On one hand, for single subunit T7 RNAP, PPi release is suggested to be mechanically coupled to the opening motion of the O-helix (co...

G-protein-coupled receptors (GPCRs) are key signaling molecules and are intensely studied. Whereas GPCRs recognizing small-molecules have been successfully targeted for drug discovery, proteinrecognizing GPCRs, such as the chemokine receptors, claim few drugs or even useful small molecule reagents. This reflects both the difficulties that attend protein-protein interface inhibitor discovery, and the lack of structures for these targets. Imminent structure determination of chemokine receptor CXCR4 motivated docking screens for new ligands against a homology model and subsequently the crystal structure. More than 3 million molecules were docked against the model and then against the crystal structure; 24 and 23 high-scoring compounds from the respective screens were tested experimentally. Docking against the model yielded only one antagonist, which resembled known ligands and lacked specificity, whereas the crystal structure docking yielded four that were dissimilar to previously known scaffolds and apparently specific. Intriguingly, several were potent and relatively small, with IC 50 values as low as 306 nM, ligand efficiencies as high as 0.36, and with efficacy in cellular chemotaxis. The potency and efficiency of these molecules has few precedents among protein-protein interface inhibitors, and supports structure-based efforts to discover leads for chemokine GPCRs.drug design | virtual screening | promiscuous aggregation G -protein-coupled receptors (GPCRs) play a central role in many normal physiological pathways and altered diseased states, and are the targets of approximately 30% of marketed drugs (1). Ligand discovery against small-molecule GPCRs such as the bioamine receptors has been particularly productive, as have structure-based screens against their crystal structures (2-5). Targeting larger-molecule-recognizing GPCRs has been more difficult. Although multiple reagents are available for lipid and peptidergic GPCRs, their molecular weights are substantially higher than those typical for bioamine receptors, and they are less ligand efficient. This reflects the challenges faced in ligand discovery against peptide-protein and lipid-protein interfaces. These difficulties are still more acute against chemokine GPCRs, which recognize folded proteins of ∼100 amino acids in length and are thus protein-protein interface (PPI) targets (6). Although there are several example drugs in this class, such as maraviroc, plerixafor, and vorapaxar, finding organic molecules with good affinity and the physical properties of oral drugs is notoriously difficult for PPI targets, as reflected in the high molecular weight and hydrophobicity of the few PPI drugs (7).A public competition to predict ligand complexes with the structure of C-X-C chemokine receptor 4 (CXCR4) inspired us to bring structure-based discovery to bear against a key member of the chemokine family (8). CXCR4 natively recognizes the CXCL12 chemokine, an 8-kDa protein. Like many other PPI targets, CXCR4 plays a key signaling role: it is constitutively expres...

Movement is crucial to the biological function of many proteins, yet crystallographic structures of proteins can give us only a static snapshot. The protein dynamics that are important to biological function often happen on a timescale that is unattainable through detailed simulation methods such as molecular dynamics as they often involve crossing high-energy barriers. To address this coarse-grained motion, several methods have been implemented as web servers in which a set of coordinates is usually linearly interpolated from an initial crystallographic structure to a final crystallographic structure. We present a new morphing method that does not extrapolate linearly and can therefore go around high-energy barriers and which can produce different trajectories between the same two starting points. In this work, we evaluate our method and other established coarse-grained methods according to an objective measure: how close a coarse-grained dynamics method comes to a crystallographically determined intermediate structure when calculating a trajectory between the initial and final crystal protein structure. We test this with a set of five proteins with at least three crystallographically determined on-pathway high-resolution intermediate structures from the Protein Data Bank. For simple hinging motions involving a small conformational change, segmentation of the protein into two rigid sections outperforms other more computationally involved methods. However, large-scale conformational change is best addressed using a nonlinear approach and we suggest that there is merit in further developing such methods.

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

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 wildtype 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. nucleotide selection | transcription fidelity