Dynamics of pyrophosphate ion release and its coupled trigger loop motion from closed to open state in RNA polymerase II

Higher-order epistasis within Pol II trigger loop haplotypes

RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL for the purpose of understand functional interactions between residues and to understand how individual mutants might alter TL function. We identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating species-specific interactions between otherwise highly conserved TLs and its surroundings. These interactions represent epistasis between TL residues and the rest of Pol II. We sought to understand why certain TL sequences are incompatible with S. cerevisiae Pol II and to dissect the nature of genetic interactions within multiply substituted TLs as a window on higher order epistasis in this system. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.

Bioreaction-Compatible Bivariate Lanthanide MOF Sensor Enables Stimulus-Multiresponsive Platform for ctDNA On-Site Detection

Detection of circulating tumor DNA (ctDNA) in liquid biopsy is of great importance for tumor diagnosis but difficult due to its low amount in bodily fluids. Herein, a novel ctDNA detection platform is established by quantifying DNA amplification by-product pyrophosphate (PPi) using a newly designed bivariable lanthanide metal-organic framework (Ln-MOF), namely, Ce/Eu-DPA MOF (CE-24, DPA = pyridine-2,6-dicarboxylic acid). CE-24 MOF exhibits ultrafast dual-response (fluorescence enhancement and enzyme-activity inhibition) to PPi stimuli by virtue of host-guest interaction. The platform is applied to detecting colon carcinoma-related ctDNA (KARS G12D mutation) combined with the isothermal nucleic acid exponential amplification reaction (EXPAR). ctDNA triggers the generation of a large amount of PPi, and the ctDNA quantification is achieved through the ratio fluorescence/colorimetric dual-mode assay of PPi. The combination of the EXPAR and the dual-mode PPi sensing allows the ctDNA assay method to be low-cost, convenient, bioreaction-compatible (freedom from the interference of bioreaction systems), sensitive (limit of detection down to 101 fM), and suitable for on-site detection. To the best of our knowledge, this work is the first application of Ln-MOF for ctDNA detection, and it provides a novel universal strategy for the rapid detection of nucleic acid biomarkers in point-of-care scenarios.

Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.

Transcription elongation mechanisms of RNA polymerases I, II, and III and their therapeutic implications

Transcription is a tightly regulated, complex, and essential cellular process in all living organisms. Transcription is comprised of three steps, transcription initiation, elongation, and termination. The distinct transcription initiation and termination mechanisms of eukaryotic RNA polymerases I, II, and III (Pols I, II, and III) have long been appreciated. Recent methodological advances have empowered high-resolution investigations of the Pols' transcription elongation mechanisms. Here, we review the kinetic similarities and differences in the individual steps of Pol I-, II-, and III-catalyzed transcription elongation, including NTP binding, bond formation, pyrophosphate release, and translocation. This review serves as an important summation of Saccharomyces cerevisiae (yeast) Pol I, II, and III kinetic investigations which reveal that transcription elongation by the Pols is governed by distinct mechanisms. Further, these studies illustrate how basic, biochemical investigations of the Pols can empower the development of chemotherapeutic compounds.

Revealing the conformational dynamics of UDP-GlcNAc recognition by O-GlcNAc transferase via Markov state model

The O-linked N-acetylglucosamine (O-GlcNAc) glycosylation is a critical post-translational modification and closely linked to various physiological and pathological conditions. The O-GlcNAc transferase (OGT) functions as the only glycosyltransferase of O-GlcNAc glycosylation by transferring GlcNAc from UDP-GlcNAc to serine or threonine residues on protein substrates. The interaction mode of UDP-GlcNAc against OGT has been preliminarily revealed by the crystal structures, yet an atomic-level comprehension for the conformational dynamics of the recognition process remains elusive. Here, we construct the Markov state model based on extensive all-atom molecular dynamics (MD) simulations with an aggregated simulation time of ∼9 μs, and reveal that the UDP-GlcNAc recognition process by OGT encompasses four key metastable states, occurring within an estimated timescale of ∼10 μs. During UDP-GlcNAc recognition process, we find the pyrophosphate moiety (P2O52-) initially anchors to the active pocket via salt bridge and hydrogen bonds, facilitating subsequent binding of the uridine and GlcNAc moieties. Furthermore, the functional roles of K842 involved in the salt bridge with P2O52- were evaluated through extra mutant MD simulations. Overall, our study provides valuable insights into the UDP-GlcNAc recognition mechanism by OGT, which could further aid in mechanistic studies of O-GlcNAc glycosylation and drug development targeting on OGT.

Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes.

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.

Disease mutations and phosphorylation alter the allosteric pathways involved in autoinhibition of protein phosphatase 2A

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms.

Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently

Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov state model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this Perspective, we discuss how including memory (i.e., non-Markovian effects) can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov state models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep-learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov state models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.

Widespread epistasis shapes RNA Polymerase II active site function and evolution

Multi-subunit RNA Polymerases (msRNAPs) are responsible for transcription in all kingdoms of life. At the heart of these msRNAPs is an ultra-conserved active site domain, the trigger loop (TL), coordinating transcription speed and fidelity by critical conformational changes impacting multiple steps in substrate selection, catalysis, and translocation. Previous studies have observed several different types of genetic interactions between eukaryotic RNA polymerase II (Pol II) TL residues, suggesting that the TL's function is shaped by functional interactions of residues within and around the TL. The extent of these interaction networks and how they control msRNAP function and evolution remain to be determined. Here we have dissected the Pol II TL interaction landscape by deep mutational scanning in Saccharomyces cerevisiae Pol II. Through analysis of over 15000 alleles, representing all single mutants, a rationally designed subset of double mutants, and evolutionarily observed TL haplotypes, we identify interaction networks controlling TL function. Substituting residues creates allele-specific networks and propagates epistatic effects across the Pol II active site. Furthermore, the interaction landscape further distinguishes alleles with similar growth phenotypes, suggesting increased resolution over the previously reported single mutant phenotypic landscape. Finally, co-evolutionary analyses reveal groups of co-evolving residues across Pol II converge onto the active site, where evolutionary constraints interface with pervasive epistasis. Our studies provide a powerful system to understand the plasticity of RNA polymerase mechanism and evolution, and provide the first example of pervasive epistatic landscape in a highly conserved and constrained domain within an essential enzyme.

Colorimetry-Based Phosphate Measurement for Polymerase Elongation

DNA detection, which includes the measurement of variants in sequences or the presence of certain genes, is widely used in research and clinical diagnosis. Both require DNA-dependent DNA polymerase-catalyzed strand extension. Currently, these techniques rely heavily on the instruments used to visualize the results. This study introduced a simple and direct colorimetric method to measure polymerase-directed elongation. First, pyrophosphate (PPi), a by-product of strand extension, is converted into phosphate (Pi). Phosphate levels were measured using either Mo-Sb or BIOMOL Green reagent. This study showed that this colorimetry can distinguish single-base variants and detect PCR products in preset stringent conditions, implicating the potential value of this strategy to detect DNA.

Nucleotide addition and cleavage by RNA polymerase II: Coordination of two catalytic reactions using a single active site

RNA polymerase II (Pol II) incorporates complementary ribonucleotides into the growing RNA chain one at a time via the nucleotide addition cycle. The nucleotide addition cycle, however, is prone to misincorporation of noncomplementary nucleotides. Thus, to ensure transcriptional fidelity, Pol II backtracks and then cleaves the misincorporated nucleotides. These two reverse reactions, nucleotide addition and cleavage, are catalyzed in the same active site of Pol II, which is different from DNA polymerases or other endonucleases. Recently, substantial progress has been made to understand how Pol II effectively performs its dual role in the same active site. Our review highlights these recent studies and provides an overall model of the catalytic mechanisms of Pol II. In particular, RNA extension follows the two-metal-ion mechanism, and several Pol II residues play important roles to facilitate the catalysis. In sharp contrast, the cleavage reaction is independent of any Pol II residues. Interestingly, Pol II relies on its residues to recognize the misincorporated nucleotides during the backtracking process, prior to cleavage. In this way, Pol II efficiently compartmentalizes its two distinct catalytic functions using the same active site. Lastly, we also discuss a new perspective on the potential third Mg²⁺ in the nucleotide addition and intrinsic cleavage reactions.

Automated Path Searching Reveals the Mechanism of Hydrolysis Enhancement by T4 Lysozyme Mutants

Bacteriophage T4 lysozyme (T4L) is a glycosidase that is widely applied as a natural antimicrobial agent in the food industry. Due to its wide applications and small size, T4L has been regarded as a model system for understanding protein dynamics and for large-scale protein engineering. Through structural insights from the single conformation of T4L, a series of mutations (L99A,G113A,R119P) have been introduced, which have successfully raised the fractional population of its only hydrolysis-competent excited state to 96%. However, the actual impact of these substitutions on its dynamics remains unclear, largely due to the lack of highly efficient sampling algorithms. Here, using our recently developed travelling-salesman-based automated path searching (TAPS), we located the minimum-free-energy path (MFEP) for the transition of three T4L mutants from their ground states to their excited states. All three mutants share a three-step transition: the flipping of F114, the rearrangement of α0/α1 helices, and final refinement. Remarkably, the MFEP revealed that the effects of the mutations are drastically beyond the expectations of their original design: (a) the G113A substitution not only enhances helicity but also fills the hydrophobic Cavity I and reduces the free energy barrier for flipping F114; (b) R119P barely changes the stability of the ground state but stabilizes the excited state through rarely reported polar contacts S117_OG:N132_ND2, E11_OE1:R145_NH1, and E11_OE2:Q105_NE2; (c) the residue W138 flips into Cavity I and further stabilizes the excited state for the triple mutant L99A,G113A,R119P. These novel insights that were unexpected in the original mutant design indicated the necessity of incorporating path searching into the workflow of rational protein engineering.

DNA opening during transcription initiation by RNA polymerase II in atomic detail

RNA polymerase II (RNAP II) synthesizes RNA by reading the DNA code. During transcription initiation, RNAP II opens the double-stranded DNA to expose the DNA template to the active site. The molecular interactions driving and controlling DNA opening are not well understood. We used all-atom steered molecular dynamics simulations to derive a continuous pathway of DNA opening in human RNAP II, involving a 55 Å DNA strand displacement and a nearly 360° DNA helix rotation. To drive such large-scale transitions, we used a combination of RMSD-based collective variables, a newly designed rotational coordinate, and a path collective variable. The simulations reveal extensive interactions of the DNA with three conserved protein loops near the active site, namely with the rudder, fork loop 1, and fork loop 2. According to the simulations, DNA-protein interactions support DNA opening by a twofold mechanism; they catalyze DNA opening by attacking Watson-Crick hydrogen bonds, and they stabilize the open DNA bubble by the formation of a wide set of DNA-protein salt bridges.

Mechanism of reaction of RNA-dependent RNA polymerase from SARS-CoV-2

We combine molecular dynamics, statistical mechanics, and hybrid quantum mechanics/molecular mechanics simulations to describe mechanistically the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp). Our study analyzes the binding mode of both natural triphosphate substrates as well as remdesivir triphosphate (the active form of drug), which is bound preferentially over ATP by RdRp while being poorly recognized by human RNA polymerase II (RNA Pol II). A comparison of incorporation rates between natural and antiviral nucleotides shows that remdesivir is incorporated more slowly into the nascent RNA compared with ATP, leading to an RNA duplex that is structurally very similar to an unmodified one, arguing against the hypothesis that remdesivir is a competitive inhibitor of ATP. We characterize the entire mechanism of reaction, finding that viral RdRp is highly processive and displays a higher catalytic rate of incorporation than human RNA Pol II. Overall, our study provides the first detailed explanation of the replication mechanism of RdRp.

Computational investigations on target-site searching and recognition mechanisms by thymine DNA glycosylase during DNA repair process

DNA glycosylase, as one member of DNA repair machineries, plays an essential role in correcting mismatched/damaged DNA nucleotides by cleaving the N-glycosidic bond between the sugar and target nucleobase through the base excision repair (BER) pathways. Efficient corrections of these DNA lesions are critical for maintaining genome integrity and preventing premature aging and cancers. The target-site searching/recognition mechanisms and the subsequent conformational dynamics of DNA glycosylase, however, remain challenging to be characterized using experimental techniques. In this review, we summarize our recent studies of sequential structural changes of thymine DNA glycosylase (TDG) during the DNA repair process, achieved mostly by molecular dynamics (MD) simulations. Computational simulations allow us to reveal atomic-level structural dynamics of TDG as it approaches the target-site, and pinpoint the key structural elements responsible for regulating the translocation of TDG along DNA. Subsequently, upon locating the lesions, TDG adopts a base-flipping mechanism to extrude the mispaired nucleobase into the enzyme active-site. The constructed kinetic network model elucidates six metastable states during the base-extrusion process and suggests an active role of TDG in flipping the intrahelical nucleobase. Finally, the molecular mechanism of product release dynamics after catalysis is also summarized. Taken together, we highlight to what extent the computational simulations advance our knowledge and understanding of the molecular mechanism underlying the conformational dynamics of TDG, as well as the limitations of current theoretical work.

RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics

The Markov State Model (MSM) is a powerful tool for modeling long timescale dynamics based on numerous short molecular dynamics (MD) simulation trajectories, which makes it a useful tool for elucidating the conformational changes of biological macromolecules. By partitioning the phase space into discretized states and estimating the probabilities of inter-state transitions based on short MD trajectories, one can construct a kinetic network model that could be used to extrapolate long-timescale kinetics if the Markovian condition is met. However, meeting the Markovian condition often requires hundreds or even thousands of states (microstates), which greatly hinders the comprehension of the conformational dynamics of complex biomolecules. Kinetic lumping algorithms can coarse grain numerous microstates into a handful of metastable states (macrostates), which would greatly facilitate the elucidation of biological mechanisms. In this work, we have developed a reverse-projection-based neural network (RPnet) to lump microstates into macrostates, by making use of a physics-based loss function that is based on the projection operator framework of conformational dynamics. By recognizing that microstate and macrostate transition modes can be related through a projection process, we have developed a reverse-projection scheme to directly compare the microstate and macrostate dynamics. Based on this reverse-projection scheme, we designed a loss function that allows the effective assessment of the quality of a given kinetic lumping. We then make use of a neural network to efficiently minimize this loss function to obtain an optimized set of macrostates. We have demonstrated the power of our RPnet in analyzing the dynamics of a numerical 2D potential, alanine dipeptide, and the clamp opening of an RNA polymerase. In all these systems, we have illustrated that our method could yield comparable or better results than competing methods in terms of state partitioning and reproduction of slow dynamics. We expect that our RPnet holds promise in analyzing the conformational dynamics of biological macromolecules.

Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems.

Conserved Trigger Loop Histidine of RNA Polymerase II Functions as a Positional Catalyst Primarily through Steric Effects

In all domains of life, multisubunit RNA polymerases (RNAPs) catalyze both the extension of mRNA transcripts by nucleotide addition and the hydrolysis of RNA, which enables proofreading by removal of misincorporated nucleotides. A highly conserved catalytic module within RNAPs called the trigger loop (TL) functions as the key controller of these activities. The TL is proposed to act as a positional catalyst of phosphoryl transfer and transcript cleavage via electrostatic and steric contacts with substrates in its folded helical form. The function of a near-universally conserved TL histidine that contacts NTP phosphates is of particular interest. Despite its exceptional conservation, substitutions of the TL His with Gln support efficient catalysis in bacterial and yeast RNAPs. Unlike bacterial TLs, which contain a nearby Arg, the TL His is the only acid-base catalyst candidate in the eukaryotic RNAPII TL. Nonetheless, replacement of the TL His with Leu is reported to support cell growth in yeast, suggesting that even hydrogen bonding and polarity at this position may be dispensable for efficient catalysis by RNAPII. To test how a TL His-to-Leu substitution affects the enzymatic functions of RNAPII, we compared its rates of nucleotide addition, pyrophosphorolysis, and RNA hydrolysis to those of the wild-type RNAPII enzyme. The His-to-Leu substitution slightly reduced rates of phosphoryl transfer with little if any effect on intrinsic transcript cleavage. These findings indicate that the highly conserved TL His is neither an obligate acid-base catalyst nor a polar contact for NTP phosphates but instead functions as a positional catalyst mainly through steric effects.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Highly sensitive fluorescent sensor based on coumarin organic dye for pyrophosphate ion turn-on biosensing in synovial fluid

Highly sensitive fluorescence detection of pyrophosphate ion (PPi) is in urgent demand but remains a great obstacle, ascribing to scarcity of high-performance materials with promising optical property and high affinity. Herein, we report the design and fabrication of a coumarin-based organic dye (DCCH-TPD) containing both hydrazide group and terpyridine moiety for PPi biosensing through Cu²⁺-induced photo-electron transfer (PET) effect and target analyte-switched competitive coordination reaction. Individual DCCH-TPD was found to be highly emissive, and displayed a turn-off response toward Cu²⁺ due to formation of Cu²⁺@DCCH-TPD and PET effect. The recognition of Cu²⁺@DCCH-TPD by PPi leads to generation of Cu²⁺@PPi complex, which greatly reduces the amount of Cu²⁺ coordinated with DCCH-TPD, subsequently decreasing PET effect. Significantly enhanced fluorescence is recorded and the fluorescence intensity is closely relied on PPi concentration. Thus, highly sensitive detection of PPi is achieved, and the detection limit was calculated to be 0.075 μM. Furthermore, the proposed sensor presented good selectivity, and excellent practical ability for application in arthritic fluid.

Transcriptional processing of an unnatural base pair by eukaryotic RNA polymerase II

The development of unnatural base pairs (UBPs) has greatly increased the information storage capacity of DNA, allowing for transcription of unnatural RNA by the heterologously expressed T7 RNA polymerase (RNAP) in Escherichia coli. However, little is known about how UBPs are transcribed by cellular RNA polymerases. Here, we investigated how synthetic unnatural nucleotides, NaM and TPT3, are recognized by eukaryotic RNA polymerase II (Pol II) and found that Pol II is able to selectively recognize UBPs with high fidelity when dTPT3 is in the template strand and rNaMTP acts as the nucleotide substrate. Our structural analysis and molecular dynamics simulation provide structural insights into transcriptional processing of UBPs in a stepwise manner. Intriguingly, we identified a novel 3'-RNA binding site after rNaM addition, termed the swing state. These results may pave the way for future studies in the design of transcription and translation strategies in higher organisms with expanded genetic codes.

A comprehensive mechanism for 5-carboxylcytosine-induced transcriptional pausing revealed by Markov state models

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model

To 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.

ATP-Magnesium Coordination: Protein Structure-Based Force Field Evaluation and Corrections

In the numerous molecular recognition and catalytic processes across biochemistry involving adenosine triphosphate (ATP), the common bioactive form is its magnesium chelate, ATP·Mg2+. In aqueous solution, two chelation geometries predominate, distinguished by bidentate and tridentate Mg2+-phosphate coordination. These are approximately isoenergetic but separated by a high energy barrier. Force field-based atomistic simulation studies of this complex require an accurate representation of its structure and energetics. Here we focused on the energetics of ATP·Mg2+ coordination. Applying an enhanced sampling scheme to circumvent prohibitively slow sampling of transitions between coordination modes, we observed striking contradictions between Amber and CHARMM force field descriptions, most prominently in opposing predictions of the favored coordination mode. Through further configurational free energy calculations, conducted against a diverse set of ATP·Mg2+-protein complex structures to supplement otherwise limited experimental data, we quantified systematic biases for each force field. The force field calculations were strongly predictive of experimentally observed coordination modes, enabling additive corrections to the coordination free energy that deliver close agreement with experiment. We reassessed the applicability of the thus corrected force field descriptions of ATP·Mg2+ for biomolecular simulation and observed that, while the CHARMM parameters display an erroneous preference for overextended triphosphate configurations that will affect many common biomolecular simulation applications involving ATP, the force field energy landscapes broadly agree with experimental measurements of solution geometry and the distribution of ATP·Mg2+ structures found in the Protein Data Bank. Our force field evaluation and correction approach, based on maximizing consistency with the large and heterogeneous collection of structural information encoded in the PDB, should be broadly applicable to many other systems.

Atomic resolution of short-range sliding dynamics of thymine DNA glycosylase along DNA minor-groove for lesion recognition

Thymine DNA glycosylase (TDG), as a repair enzyme, plays essential roles in maintaining the genome integrity by correcting several mismatched/damaged nucleobases. TDG acquires an efficient strategy to search for the lesions among a vast number of cognate base pairs. Currently, atomic-level details of how TDG translocates along DNA as it approaches the lesion site and the molecular mechanisms of the interplay between TDG and DNA are still elusive. Here, by constructing the Markov state model based on hundreds of molecular dynamics simulations with an integrated simulation time of ∼25 μs, we reveal the rotation-coupled sliding dynamics of TDG along a 9 bp DNA segment containing one G·T mispair. We find that TDG translocates along DNA at a relatively faster rate when distant from the lesion site, but slows down as it approaches the target, accompanied by deeply penetrating into the minor-groove, opening up the mismatched base pair and significantly sculpturing the DNA shape. Moreover, the electrostatic interactions between TDG and DNA are found to be critical for mediating the TDG translocation. Notably, several uncharacterized TDG residues are identified to take part in regulating the conformational switches of TDG occurred in the site-transfer process, which warrants further experimental validations.

On the advantages of exploiting memory in Markov state models for biomolecular dynamics

Biomolecular dynamics play an important role in numerous biological processes. Markov State Models (MSMs) provide a powerful approach to study these dynamic processes by predicting long time scale dynamics based on many short molecular dynamics (MD) simulations. In an MSM, protein dynamics are modeled as a kinetic process consisting of a series of Markovian transitions between different conformational states at discrete time intervals (called "lag time"). To achieve this, a master equation must be constructed with a sufficiently long lag time to allow interstate transitions to become truly Markovian. This imposes a major challenge for MSM studies of proteins since the lag time is bound by the length of relatively short MD simulations available to estimate the frequency of transitions. Here, we show how one can employ the generalized master equation formalism to obtain an exact description of protein conformational dynamics both at short and long time scales without the time resolution restrictions imposed by the MSM lag time. Using a simple kinetic model, alanine dipeptide, and WW domain, we demonstrate that it is possible to construct these quasi-Markov State Models (qMSMs) using MD simulations that are 5-10 times shorter than those required by MSMs. These qMSMs only contain a handful of metastable states and, thus, can greatly facilitate the interpretation of mechanisms associated with protein dynamics. A qMSM opens the door to the study of conformational changes of complex biomolecules where a Markovian model with a few states is often difficult to construct due to the limited length of available MD simulations.

Elucidating molecular mechanisms of functional conformational changes of proteins via Markov state models

Functional conformational changes of proteins can facilitate numerous biological events in cells. The Markov state model (MSM) built from molecular dynamics simulations provide a powerful approach to study them. We here introduce a protocol that is tailor-made for constructing MSMs to study the functional conformational changes of proteins. In this protocol, one of the important steps is to select proper molecular features that can collectively describe the slowest timescales of conformational changes of interest. We recommend spectral oASIS, the modified version of oASIS, as a promising approach for automatic feature selection. Recently developed deep learning methods could also serve efficient approaches for selecting features and finding collective variables. Using DNA repair enzymes and RNA polymerases as examples, we review recent applications of MSMs to elucidate molecular mechanisms of functional conformational changes. Finally, we discuss remaining challenges and future perspectives for constructing MSMs to study functional conformational changes of proteins.

Nucleotide Loading Modes of Human RNA Polymerase II as Deciphered by Molecular Simulations

Mapping the route of nucleoside triphosphate (NTP) entry into the sequestered active site of RNA polymerase (RNAP) has major implications for elucidating the complete nucleotide addition cycle. Constituting a dichotomy that remains to be resolved, two alternatives, direct NTP delivery via the secondary channel (CH2) or selection to downstream sites in the main channel (CH1) prior to catalysis, have been proposed. In this study, accelerated molecular dynamics simulations of freely diffusing NTPs about RNAPII were applied to refine the CH2 model and uncover atomic details on the CH1 model that previously lacked a persuasive structural framework to illustrate its mechanism of action. Diffusion and binding of NTPs to downstream DNA, and the transfer of a preselected NTP to the active site, are simulated for the first time. All-atom simulations further support that CH1 loading is transcription factor IIF (TFIIF) dependent and impacts catalytic isomerization. Altogether, the alternative nucleotide loading systems may allow distinct transcriptional landscapes to be expressed.

Quantification of cyclic DNA polymerization with lanthanide coordination nanomaterials for liquid biopsy

Quantification of circulating tumor DNA (ctDNA) is of great importance in liquid biopsy but difficult due to its low amount in bodily fluids. To meet this high demand, a novel method for ctDNA detection is established by quantifying cyclic DNA polymerization using lanthanide coordination polymers (Ln-CPs). Relying on the coordination between the pyrophosphate ion (PPi) and trivalent cerium ion (Ce3+), organic ligand-free PPi-Ce coordination polymer networks (PPi-Ce CPNs) with enhanced fluorescence are prepared for the first time. By surveying the optical properties of PPi-Ce CPNs, it is found that PPi regulates electric-dipole transition of Ce3+ to the lowest excited state, thus facilitating the emission of fluorescence. Therefore, fluorescence enhancement of PPi-Ce CPNs originates from the ligand field effect rather than the normal antenna effect. Moreover, a new strategy to quantify DNA polymerization is developed based on PPi-Ce CPNs. By introducing multifold cyclic DNA polymerization, a small amount of ctDNA triggers the exponential generation of PPi to form plenty of PPi-Ce CPNs. Accordingly, a biosensor is constructed for sensitive ctDNA detection by measuring the intense fluorescence of PPi-Ce CPNs. The biosensor is capable of sensing ctDNA at the sub-femtomolar level, which is far better than the analytical performances of commercial dyes. Besides, the analytical method is able to detect single nucleotide polymorphism and determine ctDNA in real samples. Considering that DNA polymerization is widely used in bio-recognition, bio-assembly and biomineralization, the work provides a versatile quantitative strategy of making relevant processes precise and controllable.

Opening dynamics of HIV-1 gp120 upon receptor binding is dictated by a key hydrophobic core

HIV-1 entry is mediated firstly by the molecular recognition between the viral glycoprotein gp120 and its receptor CD4 on host T-cells. As a key antigen that can be targeted by neutralizing antibodies, gp120 has been a focus for extensive studies with efforts to understand its structural properties and conformational dynamics upon receptor binding. An atomistic-level revelation of gp120 opening dynamics activated by CD4, however, is still unknown. Here, by constructing a Markov State Model (MSM) based on hundreds of Molecular Dynamics (MD) simulations with an aggregated simulation time of ∼20 microseconds (μs), we identify the key metastable states of gp120 during its opening dynamics upon CD4 binding. The MSM provides a clear dynamic model whereby the identified metastable states coexist and can reach an equilibrium. More importantly, a hydrophobic core flanked by variable loops (V1V2 and V3) and the β20/21 region plays an essential role in triggering the gp120 opening. Any destabilizing effects introduced into the hydrophobic core, therefore, can be expected to promote transition of gp120 to an open state. Moreover, the variable loops demonstrate high flexibilities in fully open gp120. In particular, the V3 region is capable of exploring both closed and open conformations, even with the V1/V2 loops largely adopting an open form. In addition, the bridging sheet formation in gp120 is likely induced by the incoming co-receptor/antibody recognitions, since the V1/V2 structure is highly heterogeneous so that the bridging-sheet formed conformation is not the most populated state. Our studies provide deep insights into the dynamic features of gp120 and its molecular recognitions to the broadly neutralizing antibodies, which guides future attempts to design more effective gp120 immunogens.

Pyrophosphate hydrolysis is an intrinsic and critical step of the DNA synthesis reaction

DNA synthesis by DNA polymerases (dPols) is central to duplication and maintenance of the genome in all living organisms. dPols catalyze the formation of a phosphodiester bond between the incoming deoxynucleoside triphosphate and the terminal primer nucleotide with the release of a pyrophosphate (PPi) group. It is believed that formation of the phosphodiester bond is an endergonic reaction and PPi has to be hydrolyzed by accompanying pyrophosphatase enzymes to ensure that the free energy change of the DNA synthesis reaction is negative and it can proceed in the forward direction. The fact that DNA synthesis proceeds in vitro in the absence of pyrophosphatases represents a long-standing conundrum regarding the thermodynamics of the DNA synthesis reaction. Using time-resolved crystallography, we show that hydrolysis of PPi is an intrinsic and critical step of the DNA synthesis reaction catalyzed by dPols. The hydrolysis of PPi occurs after the formation of the phosphodiester bond and ensures that the DNA synthesis reaction is energetically favorable without the need for additional enzymes. Also, we observe that DNA synthesis is a two Mg2+ ion assisted stepwise associative SN2 reaction. Overall, this study provides deep temporal insight regarding the primary enzymatic reaction responsible for genome duplication.

Dynamics of the excised base release in thymine DNA glycosylase during DNA repair process

Thymine DNA glycosylase (TDG) initiates base excision repair by cleaving the N-glycosidic bond between the sugar and target base. After catalysis, the release of excised base is a requisite step to terminate the catalytic cycle and liberate the TDG for the following enzymatic reactions. However, an atomistic-level understanding of the dynamics of the product release process in TDG remains unknown. Here, by employing molecular dynamics simulations combined with the Markov State Model, we reveal the dynamics of the thymine release after the excision at microseconds timescale and all-atom resolution. We identify several key metastable states of the thymine and its dominant releasing pathway. Notably, after replacing the TDG residue Gly142 with tyrosine, the thymine release is delayed compared to the wild-type (wt) TDG, as supported by our potential of mean force (PMF) calculations. These findings warrant further experimental tests to potentially trap the excised base in the active site of TDG after the catalysis, which had been unsuccessful by previous attempts. Finally, we extended our studies to other TDG products, including the uracil, 5hmU, 5fC and 5caC bases in order to compare the product release for different targeting bases in the TDG–DNA complex.

The Mechanisms of Substrate Selection, Catalysis, and Translocation by the Elongating RNA Polymerase

Multi-subunit DNA-dependent RNA polymerases synthesize all classes of cellular RNAs, ranging from short regulatory transcripts to gigantic messenger RNAs. RNA polymerase has to make each RNA product in just one try, even if it takes millions of successive nucleotide addition steps. During each step, RNA polymerase selects a correct substrate, adds it to a growing chain, and moves one nucleotide forward before repeating the cycle. However, RNA synthesis is anything but monotonous: RNA polymerase frequently pauses upon encountering mechanical, chemical and torsional barriers, sometimes stepping back and cleaving off nucleotides from the growing RNA chain. A picture in which these intermittent dynamics enable processive, accurate, and controllable RNA synthesis is emerging from complementary structural, biochemical, computational, and single-molecule studies. Here, we summarize our current understanding of the mechanism and regulation of the on-pathway transcription elongation. We review the details of substrate selection, catalysis, proofreading, and translocation, focusing on rate-limiting steps, structural elements that modulate them, and accessory proteins that appear to control RNA polymerase translocation.

C, Da LT, Yu J. A Viral T7 RNA Polymerase Ratcheting Along DNA With Fidelity Control

RNA polymerase (RNAP) from bacteriophage T7 is a representative single-subunit viral RNAP that can transcribe with high promoter activities without assistances from transcription factors. We accordingly studied this small transcription machine computationally as a model system to understand underlying mechanisms of mechano-chemical coupling and fidelity control in the RNAP transcription elongation. Here we summarize our computational work from several recent publications to demonstrate first how T7 RNAP translocates via Brownian alike motions along DNA right after the catalytic product release. Then we show how the backward translocation motions are prevented at post-translocation upon successful nucleotide incorporation, which is also subject to stepwise nucleotide selection and acts as a pawl for "selective ratcheting". The structural dynamics and energetics features revealed from our atomistic molecular dynamics (MD) simulations and related analyses on the single-subunit T7 RNAP thus provided detailed and quantitative characterizations on the Brownian-ratchet working scenario of a prototypical transcription machine with sophisticated nucleotide selectivity for fidelity control. The presented mechanisms can be more or less general for structurally similar viral or mitochondrial RNAPs and some of DNA polymerases, or even for the RNAP engine of the more complicated transcription machinery in higher organisms.

8-Oxo-guanine DNA damage induces transcription errors by escaping two distinct fidelity control checkpoints of RNA polymerase II

RNA polymerase II (Pol II) has an intrinsic fidelity control mechanism to maintain faithful genetic information transfer during transcription. 8-Oxo-guanine (8OG), a commonly occurring damaged guanine base, promotes misincorporation of adenine into the RNA strand. Recent structural work has shown that adenine can pair with the syn conformation of 8OG directly upstream of the Pol II active site. However, it remains unknown how 8OG is accommodated in the active site as a template base for the incoming ATP. Here, we used molecular dynamics (MD) simulations to investigate two consecutive steps that may contribute to the adenine misincorporation by Pol II. First, the mismatch is located in the active site, contributing to initial incorporation of adenine. Second, the mismatch is in the adjacent upstream position, contributing to extension from the mismatched bp. These results are supported by an in vitro transcription assay, confirming that 8OG can induce adenine misincorporation. Our simulations further suggest that 8OG forms a stable bp with the mismatched adenine in both the active site and the adjacent upstream position. This stability predominantly originates from hydrogen bonding between the mismatched adenine and 8OG in a noncanonical syn conformation. Interestingly, we also found that an unstable bp present directly upstream of the active site, such as adenine paired with 8OG in the canonical anti conformation, largely disrupts the stability of the active site. Our findings have uncovered two main factors contributing to how 8OG induces transcriptional errors and escapes Pol II transcriptional fidelity control checkpoints.

Mechanism of Mg2+-Accompanied Product Release in Sugar Nucleotidyltransferases

The nucleotidyl transfer reaction, catalyzed by sugar nucleotidyltransferases (SNTs), is assisted by two active site Mg²⁺ ions. While studying this reaction using X-ray crystallography, we captured snapshots of the pyrophosphate (product) as it exits along a pocket. Surprisingly, one of the active site Mg²⁺ ions remains coordinated to the exiting pyrophosphate. This hints at the participation of Mg²⁺ in the process of product release, besides its role in catalyzing nucleotidyl transfer. These observations are further supported by enhanced sampling molecular dynamics simulations. Free energy computations suggest that the product release is likely to be rate limiting in SNTs, and the origin of the high free energy barrier for product release could be traced back to the "slow" conformational change of an Arg residue at the exit end of the pocket. These results establish a dual role for Mg²⁺, and propose a general mechanism of product release during the nucleotidyl transfer by SNTs.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models

Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein-ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.

Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models

Transcription elongation cycle (TEC) of RNA polymerase II (Pol II) is a process of adding a nucleoside triphosphate to the growing messenger RNA chain. Due to the long timescale events in Pol II TEC, an advanced computational technique, such as Markov State Model (MSM), is needed to provide atomistic mechanism and reaction rates. The combination of MSM and experimental results can be used to build a kinetic network model (KNM) of the whole TEC. This review provides a brief protocol to build MSM and KNM of the whole TEC, along with the latest findings of MSM and other computational studies of Pol II TEC. Lastly, we offer a perspective on potentially using a sequence dependent KNM to predict genome-wide transcription error.

Pyrophosphate Release in the Protein HIV Reverse Transcriptase

Enzymatic reactions usually occur in several steps: a step of substrate binding to the surface of the protein, a step of protein reorganization around the substrate and conduction of a chemical reaction, and a step of product release. The release of inorganic phosphate-PPi-from the matrix of the protein HIV reverse transcriptase is investigated computationally. Atomically detailed simulations with explicit solvent are analyzed to obtain the free energy profile, mean first passage time, and detailed molecular mechanisms of PPi escape. A challenge for the computations is of time scales. The experimental time scale of the process of interest is in milliseconds, and straightforward molecular dynamics simulations are in sub-microseconds. To overcome the time scale gap, we use the algorithm of Milestoning along a reaction coordinate to compute the overall free energy profile and rate. The methods of locally enhanced sampling and steered molecular dynamics determine plausible reaction coordinates. The observed molecular mechanism couples the transfer of the PPi to positively charged lysine side chains that are found on the exit pathway and to an exiting magnesium ion. In accord with experimental findings, the release rate is comparable to the chemical step, allowing for variations in substrate (DNA or RNA template) in which the release becomes rate determining.