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To compare characteristics of computed tomographic images of hepatic tumors induced by intra-mesenteric venous and intraparenchymal injection of VX2 carcinoma in rabbits. Thirty two New Zealand White rabbit were divided into two groups; group I for metastatic tumor model (n=8) by injection of 0.1 ml tumor homogenate into mesenteric vein, and group II for solitary tumor model (n=24) by direct intraparenchymal injection of 1 mm tumor cubes. Dual-phase computed tomography (CT) was performed on day 10, 17, and 24 post-tumor implantation. On day of each CT follow-up, 2 or 3 rabbits in both groups were sacrificed for histopathologic examinations which were correlated with CT findings. Tumor creation rates were 100% in Group I, and 87.5% in Group II. In group I, arterial phase showed multiple tiny lesions on day 10, and hypodense lesions with peripheral rim enhancement and central hyperdensity (target appearance) on day 17 and 24 post-implantation. Target appearance of group I was more distinctive on portal phase, and was correlated with the viable tumor tissue core surrounded by cystic spaces in histopathologic findings. In group II, arterial phase revealed ill-defined hypodense lesions with peripheral rim enhancement during entire follow-up periods, and rim enhancement was disappeared on portal phase. Viable tumor cells in group II were mainly found in periphery of tumor with apparent central necrosis, and these observations were well correlated with CT findings. This study showed specific characteristics corresponding to each implantation method, and will provide beneficial information for experimental studies using hepatic VX2 carcinoma.

Ethnopharmacological Relevance: Uncaria rhynchophylla (UR) is recognized for its therapeutic applications in treating hypertension and inflammation. However, the specific molecular mechanisms how UR and its bioactive constituents modulate inflammatory pathways remain unknown. This study investigates the effects of UR extract and its constituent, hirsuteine (HST), on TRPV1 channel modulation which is related to hypertension and inflammation., Materials and Methods: Electrophysiological recordings and calcium imaging experiments were conducted to assess TRPV1 activation by UR extract and HST in HEK293T cells and sensory neurons., Results: UR extract and HST activated TRPV1 in HEK293T cells, with repeated applications causing channel desensitization. HST application on TRPV1-positive sensory neurons significantly reduced electrical activity compared to capsaicin., Conclusion: This study demonstrated UR extract and HST are a novel TRPV1 agonists inducing channel desensitization and a potent agent for treatment of TRPV1 dependent pain relief., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 KIST. Published by Elsevier B.V. All rights reserved.)

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states; yet how these molecular machines orchestrate such biochemical sequences has remained elusive. Here, using single-molecule Förster resonance energy transfer, we show that the A-domain of gramicidin S synthetase I adopts structurally extended and functionally obligatory conformations for alternating between adenylation and thioester-formation structures during enzymatic cycles. Complementary biochemical, computational and small-angle X-ray scattering studies reveal interconversion among these three conformations as intrinsic and hierarchical where intra-A-domain organizations propagate to remodel inter-A-PCP didomain configurations during catalysis. The tight kinetic coupling between structural transitions and enzymatic transformations is quantified, and how the gramicidin S synthetase I A-domain utilizes its inherent conformational dynamics to drive directional biosynthesis with a flexibly linked PCP domain is revealed., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D ). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

In various autoimmune diseases, dysfunctional TREX1 (Three prime Repair Exonuclease 1) leads to accumulation of endogenous single-stranded DNA (ssDNA), double-stranded DNA (dsDNA) and DNA/RNA hybrids in the cytoplasm and triggers immune activation through the cGAS-STING pathway. Although inhibition of TREX1 could be a useful strategy for cancer immunotherapy, profiling cellular functions in terms of its potential substrates is a key step. Particularly important is the functionality of processing DNA/RNA hybrids and RNA substrates. The exonuclease activity measurements conducted here establish that TREX1 can digest both ssRNA and DNA/RNA hybrids but not dsRNA. The newly solved structures of TREX1-RNA product and TREX1-nucleotide complexes show that 2'-OH does not impose steric hindrance or specific interactions for the recognition of RNA. Through all-atom molecular dynamics simulations, we illustrate that the 2'-OH-mediated intra-chain hydrogen bonding in RNA would affect the binding with TREX1 and thereby reduce the exonuclease activity. This notion of higher conformational rigidity in RNA leading TREX1 to exhibit weaker catalytic cleavage is further validated by the binding affinity measurements with various synthetic DNA-RNA junctions. The results of this work thus provide new insights into the mechanism by which TREX1 processes RNA and DNA/RNA hybrids and contribute to the molecular-level understanding of the complex cellular functions of TREX1 as an exonuclease., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

In gene transcription, certain sequences of double-stranded (ds)DNA play a vital role in nucleosome positioning and expression initiation. That dsDNA is deformed to various extents in these processes leads us to ask: Could the genomic DNA also have sequence specificity in its chemical-scale mechanical properties? We approach this question using statistical machine learning to determine the rigidity between DNA chemical moieties. What emerges for the polyA, polyG, TpA, and CpG sequences studied here is a unique trigram that contains the quantitative mechanical strengths between bases and along the backbone. In a way, such a sequence-dependent trigram could be viewed as a DNA mechanical code. Interestingly, we discover a compensatory competition between the axial base-stacking interaction and the transverse base-pairing interaction, and such a reciprocal relationship constitutes the most discriminating feature of the mechanical code. Our results also provide chemical-scale understanding for experimental observables. For example, the long polyA persistence length is shown to have strong base stacking while its complement (polyA c ) exhibits high backbone rigidity. The mechanical code concept enables a direct reading of the physical interactions encoded in the sequence which, with further development, is expected to shed new light on DNA allostery and DNA-binding drugs., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Secondary structures formed by expanded CUG RNA are involved in the pathobiology of myotonic dystrophy type 1. Understanding the molecular basis of toxic RNA structures can provide insights into the mechanism of disease pathogenesis and accelerate the drug discovery process. Here, we report the crystal structure of CUG repeat RNA containing three U-U mismatches between C-G and G-C base pairs. The CUG RNA crystallizes as an A-form duplex, with the first and third U-U mismatches adopting a water-mediated asymmetric mirror isoform geometry. We found for the first time that a symmetric, water-bridged U-H 2 O-U mismatch is well tolerated within the CUG RNA duplex, which was previously suspected but not observed. The new water-bridged U-U mismatch resulted in high base-pair opening and single-sided cross-strand stacking interactions, which in turn dominate the CUG RNA structure. Furthermore, we performed molecular dynamics simulations that complemented the structural findings and proposed that the first and third U-U mismatches are interchangeable conformations, while the central water-bridged U-U mismatch represents an intermediate state that modulates the RNA duplex conformation. Collectively, the new structural features provided in this work are important for understanding the recognition of U-U mismatches in CUG repeats by external ligands such as proteins or small molecules., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease., Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC 50 = 0.4 μM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms., Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals., Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Chang, Pathak, Tzou, Luo, Hsu, Li, Lee, Chen, Huang, Yang, Hsu, Tsai, Chang, Hsu, Liu, Chin, Lin, Yang, Wu, Lee, Hsu, Liu, Chu, Wang, Wang, Huang, Lin, Hsieh, Wu Lee, Hung and Yang.)

The effects of inter-residue interactions on protein collective motions are analyzed by comparing two elastic network models (ENM)-structural contact ENM (SC-ENM) and molecular dynamics (MD)-ENM-with the edge weights computed from an all-atom MD trajectory by structure-mechanics statistical learning. A theoretical framework is devised to decompose the eigenvalues of ENM Hessian into contributions from individual springs and to compute the sensitivities of positional fluctuations and covariances to spring constant variation. Our linear perturbation approach quantifies the response mechanisms as softness modulation and orientation shift. All contacts of C α positions in SC-ENM have an identical spring constant by fitting the profile of root-of-mean-squared-fluctuation calculated from an all-atom MD simulation, and the same trajectory data are also used to compute the specific spring constant of each contact as an MD-ENM edge weight. We illustrate that the soft-mode reorganization can be understood in terms of gaining weights along the structural contacts of low elastic strengths and loosing magnitude along those of high rigidities. With the diverse mechanical strengths encoded in protein dynamics, MD-ENM is found to have more pronounced long-range couplings and sensitivity responses with orientation shift identified as a key player in driving the specific residues to have high sensitivities. Furthermore, the responses of perturbing the springs of different residues are found to have asymmetry in the action-reaction relationship. In understanding the mutation effects on protein functional properties, such as long-range communications, our results point in the directions of collective motions as a major effector.

Background: Frontal asymmetry plays a major role in depression. However, patients with treatment-resistant depression (TRD) have widespread hypofrontality. We investigated whether patients with TRD have a characteristic frontal activation pattern in functional near-infrared spectroscopy (fNIRS) findings and how the frontal cortex responds to different levels of cognitive tasks., Methods: We enrolled 27 right-handed patients with TRD, 27 patients without TRD and 27 healthy controls. We used multichannel fNIRS to evaluate activation of the bilateral dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC) and left motor area in response to 3 tasks: finger tapping, a low cognitive-load motor task; verbal fluency, a moderate cognitive-load task; and a dual task involving simultaneous finger tapping and verbal fluency, a high cognitive-load task., Results: We found significant between-group differences in left DLPFC activation for all 3 tasks. The healthy controls had cortical activation in the left motor area during finger tapping and the bilateral frontal cortex during the dual task. However, patients without TRD had right VLPFC activation during finger tapping and left DLPFC activation during the dual task. Patients with TRD had bilateral DLPFC activation during finger tapping but exhibited increased bilateral VLPFC and left motor area activation during verbal fluency and increased left motor area activation during the dual task. In healthy controls and patients without TRD, we found that the right VLPFC was positively correlated with depression severity., Limitations: Our cohort included only patients with late-onset depression., Conclusion: We found different patterns of abnormal frontal activation between patients with and without TRD. In patients without TRD, the right prefrontal cortex (PFC) was recruited during simple motor tasks. However, in patients with TRD, the bilateral PFC was recruited during simple tasks and motor cortical resources were used compensatorily during PFC-demanding complex cognitive tasks., Competing Interests: Competing interests: None declared., (© 2022 CMA Impact Inc. or its licensors.)

A protein's adaptive response to its substrates is one of the key questions driving molecular physics and physical chemistry. This work employs the recently developed structure-mechanics statistical learning method to establish a mechanical perspective. Specifically, by mapping all-atom molecular dynamics simulations onto the spring parameters of a backbone-side-chain elastic network model, the chemical moiety specific force constants (or mechanical rigidity) are used to assemble the rigidity graph, which is the matrix of inter-residue coupling strength. Using the S1A protease and the PDZ3 signaling domain as examples, chains of spatially contiguous residues are found to exhibit prominent changes in their mechanical rigidity upon substrate binding or dissociation. Such a mechanical-relay picture thus provides a mechanistic underpinning for conformational changes, long-range communication, and inter-domain allostery in both proteins, where the responsive mechanical hotspots are mostly residues having important biological functions or significant mutation sensitivity., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

For using targeted covalent inhibitors (TCIs) as anticancer and antiviral drugs, we establish that the model compounds PCMPS ( p -chloromercuriphenyl sulfate) and PCMB ( p -chloromercuribenzoate) are inhibitors of the DEDDh family of exonucleases. The underlying mechanism is analyzed by X-ray crystallography, activity/nucleic acid-binding assays, and all-atom molecular dynamics (MD) simulations. The first TCI-complexed structures of a DEDDh enzyme, the Lassa fever virus NP exonuclease (NPexo), are resolved to elucidate that the Cys409 binding site is away from the active site and the RNA-binding lid. The NPexo C409A structures indicate Cys461 as the alternative distal site for obstructing the equally active mutant. All-atom MD simulations of the wild type and mutant NPexos in explicit solvent uncover an allosteric inhibition mechanism that the local perturbation induced by PCMPS sulfonate propagates to impact the RNA-binding lid conformation. Binding assay studies confirm that PCMPS does affect the RNA binding of NPexo. The predicted relative potency between PCMPS and PCMB is also in line with experiments. The structural data and inhibition mechanism established in this work provide an important molecular basis for the drug development of TCIs., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 μs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Mammalian apurinic/apyrimidinic (AP) endonuclease 1 (APE1) has versatile enzymatic functions, including redox, endonuclease, and exonuclease activities. APE1 is thus broadly associated with pathways in DNA repair, cancer cell growth, and drug resistance. Unlike its AP site-specific endonuclease activity in Base excision repair (BER), the 3'-5' exonucleolytic cleavage of APE1 using the same active site exhibits complex substrate selection patterns, which are key to the biological functions. This work aims to integrate molecular structural information and biocatalytic properties to deduce the substrate recognition mechanism of APE1 as an exonuclease and make connection to its diverse functionalities in the cell. In particular, an induced space-filling model emerges in which a bridge-like structure is formed by Arg177 and Met270 (RM bridge) upon substrate binding, causing the active site to adopt a long and narrow product pocket for hosting the leaving group of an AP site or the 3'-end nucleotide. Rather than distinguishing bases as other exonucleases, the hydrophobicity and steric hindrance due to the APE1 product pocket provides selectivity for substrate structures, such as matched or mismatched blunt-ended dsDNA, recessed dsDNA, gapped dsDNA, and nicked dsDNA with 3'-end overhang shorter than 2 nucleotides. These dsDNAs are similar to the native substrates in BER proofreading, BER for trinucleotide repeats (TNR), Nucleotide incision repair (NIR), DNA single-strand breaks (SSB), SSB with damaged bases, and apoptosis. Integration of in vivo studies, in vitro biochemical assays, and structural analysis is thus essential for linking the APE1 exonuclease activity to the specific roles in cellular functions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

The exonuclease activity of Apurinic/apyrimidinic endonuclease 1 (APE1) is responsible for processing matched/mismatched terminus in various DNA repair pathways and for removing nucleoside analogs associated with drug resistance. To fill in the gap of structural basis for exonucleolytic cleavage, we determine the APE1-dsDNA complex structures displaying end-binding. As an exonuclease, APE1 does not show base preference but can distinguish dsDNAs with different structural features. Integration with assaying enzyme activity and binding affinity for a variety of substrates reveals for the first time that both endonucleolytic and exonucleolytic cleavage can be understood by an induced space-filling model. Binding dsDNA induces RM (Arg176 and Met269) bridge that defines a long and narrow product pocket for exquisite machinery of substrate selection. Our study paves the way to comprehend end-processing of dsDNA in the cell and the drug resistance relating to APE1.

The circadian clock is a complex system that plays many important roles in most organisms. Previously, many mathematical models have been used to sharpen our understanding of the Arabidopsis clock, which brought to light the roles of each transcriptional and post-translational regulations. However, the presence of both regulations, instead of either transcription or post-translation, raised curiosity of whether the combination of these two regulations is important for the clock's system. In this study, we built a series of simplified oscillators with different regulations to study the importance of post-translational regulation (specifically, 26S proteasome degradation) in the clock system. We found that a simple transcriptional-based oscillator can already generate sustained oscillation, but the oscillation can be easily destroyed in the presence of transcriptional leakage. Coupling post-translational control with transcriptional-based oscillator in a feed-forward loop will greatly improve the robustness of the oscillator in the presence of basal leakage. Using these general models, we were able to replicate the increased variability observed in the E3 ligase mutant for both plant and mammalian clocks. With this insight, we also predict a plausible regulator of several E3 ligase genes in the plant's clock. Thus, our results provide insights into and the plausible importance in coupling transcription and post-translation controls in the clock system., Competing Interests: The authors have declared that no competing interests exist.

The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression, but tracing their molecular origin has been difficult due to the structural and chemical complexity. We posit that concepts from machine learning can help to tackle this long-standing challenge. Here, we demonstrate the feasibility and advantage of this strategy through developing a structure-mechanics statistical learning scheme to elucidate how local rigidity in double-stranded (ds)DNA and dsRNA may lead to their global flexibility in bend, stretch, and twist. Specifically, the mechanical parameters in a heavy-atom elastic network model are computed from the trajectory data of all-atom molecular dynamics simulation. The results show that the inter-atomic springs for backbone and ribose puckering in dsRNA are stronger than those in dsDNA, but are similar in strengths for base-stacking and base-pairing. Our analysis shows that the experimental observation of dsDNA being easier to bend but harder to stretch than dsRNA comes mostly from the respective B- and A-form topologies. The computationally resolved composition of local rigidity indicates that the flexibility of both nucleic acids is mostly due to base-stacking. But for properties like twist-stretch coupling, backbone springs are shown to play a major role instead. The quantitative connection between local rigidity and global flexibility sets foundation for understanding how local binding and chemical modification of genetic materials effectuate longer-ranged regulatory signals., Competing Interests: There are no conflicts to declare statement., (This journal is © The Royal Society of Chemistry.)

The retinoid acid-related orphan receptor α (RORα), a member of the orphan nuclear receptor superfamily, functions as an unknown ligand-dependent transcription factor. RORα was shown to regulate a broad array of physiological processes such as Purkinje cell development in the cerebellum, circadian rhythm, lipid and bone metabolism, inhibition of inflammation, and anti-apoptosis. The human RORα gene encodes at least four distinct isoforms (RORα1, -2, -3, -4), which differ only in their N-terminal domain (NTD). Two isoforms, RORα2 and 3, are not expressed in mice, whereas RORα1 and 4 are expressed both in mice and humans. In the present study, we identified the specific NTD of RORα2 that enhances prostate tumor progression and proliferation via lysine methylation-mediated recruitment of coactivator complex pontin/Tip60. Upregulation of the RORα2 isoform in prostate cancers putatively promotes tumor formation and progression. Furthermore, binding between coactivator complex and RORα2 is increased by lysine methylation of RORα2 because methylation permits subsequent interaction with binding partners. This methylation-dependent activation is performed by SET domain containing 7 (SETD7) methyltransferase, inducing the oncogenic potential of RORα2. Thus, post-translational lysine methylation of RORα2 modulates oncogenic function of RORα2 in prostate cancer. Exploration of the post-translational modifications of RORα2 provides new avenues for the development of tumor-suppressive therapeutic agents through modulating the human isoform-specific tumorigenic role of RORα2.

Protein tyrosine phosphatase B (PtpB) from Mycobacterium tuberculosis (Mtb) extends the bacteria's survival in hosts and hence is a potential target for Mtb-specific drugs. To study how Mtb-specific sequence insertions in PtpB may regulate access to its active site through large-amplitude conformational changes, we performed free-energy calculations using an all-atom explicit solvent model. Corroborated by biochemical assays, the results show that PtpB's active site is controlled via an "either/or" compound conformational gating mechanism, an unexpected discovery that Mtb has evolved to bestow a single enzyme with such intricate logical operations. In addition to providing unprecedented insights for its active-site surroundings, the findings also suggest new ways of inactivating PtpB.

In higher plants (e.g., Arabidopsis thaliana), the core structure of the circadian clock is mostly governed by a repression process with very few direct activators. With a series of simplified models, we studied the underlying mechanism and found that the Arabidopsis clock consists of type-2 incoherent feed-forward loops (IFFLs), one of them creating a pulse-like expression in PRR9/7. The double-negative feedback loop between CCA1/LHY and PRR5/TOC1 generates a bistable, hysteretic behavior in the Arabidopsis circadian clock. We found that the IFFL involving PRR9/7 breaks the bistability and moves the system forward with a rapid pulse in the daytime, and the evening complex (EC) breaks it in the evening. With this illustration, we can intuitively explain the behavior of the clock under mutant conditions. Thus, our results provide new insights into the underlying network structures of the Arabidopsis core oscillator.

Three prime repair exonuclease 1 (TREX1) is an essential exonuclease in mammalian cells, and numerous in vivo and in vitro data evidenced its participation in immunity regulation and in genotoxicity remediation. In these very complicated cellular functions, the molecular mechanisms by which duplex DNA substrates are processed are mostly elusive because of the lack of structure information. Here, we report multiple crystal structures of TREX1 complexed with various substrates to provide the structure basis for overhang excision and terminal unwinding of DNA duplexes. The substrates were designed to mimic the intermediate structural DNAs involved in various repair pathways. The results showed that the Leu24-Pro25-Ser26 cluster of TREX1 served to cap the nonscissile 5'-end of the DNA for precise removal of the short 3'-overhang in L- and Y-structural DNA or to wedge into the double-stranded region for further digestion along the duplex. Biochemical assays were also conducted to demonstrate that TREX1 can indeed degrade double-stranded DNA (dsDNA) to a full extent. Overall, this study provided unprecedented knowledge at the molecular level on the enzymatic substrate processing involved in prevention of immune activation and in responses to genotoxic stresses. For example, Arg128, whose mutation in TREX1 was linked to a disease state, were shown to exhibit consistent interaction patterns with the nonscissile strand in all of the structures we solved. Such structure basis is expected to play an indispensable role in elucidating the functional activities of TREX1 at the cellular level and in vivo., Competing Interests: The authors have declared that no competing interests exist.

Background: In patients with type 2 diabetes (T2D) with poor glycemic control, there is an unmet need for treatment optimization involving the initiation and/or intensification of insulin therapy, which is often delayed because of clinical inertia. Educational initiatives that target patients and physicians might be one way to address this need., Objective: To evaluate the effectiveness of educational materials mailed to physicians and their patients in affecting initiation of insulin therapy and other health care outcomes., Methods: This study, named PIVOTs (Personalized care and the role of Insulin as a Vehicle to Optimizing Treatments), used integrated medical and pharmacy claims data from the U.S.-based HealthCore Integrated Research Database between January 1, 2006, and April 4, 2014, to identify patients who were potential candidates for insulin therapy. Eligible patients were aged 18-75 years, currently enrolled in a commercial or Medicare Advantage health plan, with T2D diagnosis codes. Patients selected for insulin treatment education had glycated hemoglobin A1c (A1c) > 10%, irrespective of the number of noninsulin antihyperglycemic drugs used, or A1c > 8.0% and ≤ 10% while receiving ≥ 2 noninsulin antihyperglycemic drugs. For each identified patient, a corresponding treating physician was identified on a hierarchical basis. Physician-level randomization was conducted to assign physicians and their linked patients to the following 4 cohorts: (1) a cross-sectional cohort in which educational materials were sent to patients and physicians on a single outreach date; (2) a longitudinal cohort in which educational materials were sent to patients and physicians on 2 occasions, 3 months apart; (3) an enhanced cohort in which patients and physicians received the same mailings as the longitudinal cohort, plus physicians were invited to attend a 1:1 video conference academic detailing session; and (4) a control cohort in which patients and physicians did not receive any educational materials. Insulin initiation rates, A1c levels, and medical and pharmacy costs were assessed from claims over 6 and 12 months follow-up within each cohort., Results: Mean insulin initiation rates at 12 months ranged from 9.2%-10.3% (all patients) to 12.3%-14.9% (subset with baseline A1c ≥ 9.0%), with similar rates across the intervention and control cohorts. Reductions in A1c from baseline were also similar across cohorts for all patients (0.1%-0.6%), as well as for those with a baseline A1c ≥ 9.0% (0.9%-1.6%). Approximately 14%-20% of patients achieved A1c < 7.0%, with no differences across cohorts. Changes in mean total all-cause and diabetes-related health care costs were also similar across cohorts., Conclusions: The findings of this real-world, randomized intervention call into question the value of educational mailings as a means to overcoming clinical inertia and improving health outcomes in patients with T2D, at least in the context of insulin initiation., Disclosures: This study was funded by Sanofi US. Bieszk and Wei are employees of Sanofi US. Grabner, Barron, and Quimbo are employees of HealthCore, which was under contract with Sanofi US for the conduct of this study. Yan is an employee of PHAR, LLC and was employed by HealthCore at the time this study was conducted and completed. Biel is an employee of Anthem. Chu is a consultant for Sanofi US; a member of the lecture bureaus for AstraZeneca, Eli Lilly, and Sanofi US; and has received research funding from Novo Nordisk. Study concept and design were contributed by Bieszk, Grabner, Wei, Quimbo, and Barron. Yan, Barron, Quimbo, and Grabner collected the data, which were interpreted by Biel, Chu, Bieszk, and Wei, with assistance from the other authors. The manuscript was written by Bieszk, with assistance from the other authors, and revised by Bieszk, Grabner, Biel, and Chu, along with the other authors. Part of this work was presented in poster format at the 76th Scientific Sessions of the American Diabetes Association; June 10-14, 2016; New Orleans, Louisiana.

Objectives: A lack of understanding in menopausal and postmenopausal women's (PMW) risk perception towards osteoporosis and breast cancer still exists, which is explored in this study. This information might allow health professionals to conduct interventions to improve health behaviors before menopause-related diseases are imminent., Methods: Between 10 December 2015 and 31 January 2016, 573 menopausal or PMW were successfully interviewed on 17 questions, comprising separate sections for osteoporosis and breast cancer. The target respondents were menopausal or PMW aged 45 to 60 years, with no previous diagnosis of osteoporosis or breast cancer, who attended private clinics across Hong Kong for annual physical examination., Results: Regarding menopausal issues, the top three concerns were osteoporosis and fracture (72%), breast cancer (44%), and sleep disorder/insomnia/headache (40%). Among 314 respondents (55%) who tried to prevent osteoporosis, 74% of them began to do it after they were 40 years old. On the other hand, 65% of respondents never had a bone density test. For respondents who said "I'm too young, so I don't need to check", their mean age was 52 years old. Ninety percent of respondents mistakenly believed that regular breast examination, regular breast massage, drink soy milk, or vaccine can prevent breast cancer., Conclusions: This survey revealed osteoporosis and breast cancer as the top concerns among menopausal and PMW in Hong Kong. Inadequate health behaviors and misconceptions still exist despite widespread health education in the recent years., Competing Interests: Conflict of Interest: No potential conflict of interest relevant to this article was reported.

Aims: Examine real-world outcomes in patients with type 2 diabetes mellitus (T2DM) initiating injectable therapy as part of the Initiation of New Injectable Treatment Introduced after Antidiabetic Therapy with Oral-only Regimens (INITIATOR) study., Materials and Methods: Linked insurance claims and medical record data were collected from 2 large US health insurers (April 1, 2010 to March 31, 2012) of T2DM adults initiating treatment with glargine (GLA) or liraglutide (LIRA). Baseline characteristics were examined and changes in 12-month follow-up outcomes were described for both treatment groups: HbA1c, weight change, hypoglycaemia, persistence, healthcare utilisation and costs., Results: A total of 4490 patients were included (GLA, 2116; LIRA, 2374). At baseline, GLA patients had significantly higher HbA1c vs LIRA patients (9.72% vs 8.19%; P < .001), lower likelihood of having HbA1c < 7% (7.1% vs 23.8%; P < .001), lower bodyweight (100.9 kg vs 110.9 kg, P < .001), higher Charlson Comorbidity Index score (0.88 vs 0.63; P < .001), and higher diabetes-related costs ($3492 vs $2089; P < .001), respectively. During 12-months of follow-up, treatment persistence was 64%, mean HbA1c reduction was -1.24% and weight change was + 1.17 among GLA patients, and was 49%, -0.51% and -2.74 kg, respectively, among LIRA patients. Diabetes-related costs increased significantly from baseline to follow-up for LIRA patients ($2089 vs $3258, P < .001) but not for GLA patients ($3492 vs $3550, P = .890)., Conclusions: There were clinically relevant baseline differences in both groups, suggesting that GLA and LIRA are prescribed for different patient groups, and highlighting that efficacy results from clinical trials do not always translate into real-world practice. Significant increases in healthcare costs were observed in the LIRA group, warranting further cost-effectiveness analysis., (© 2016 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

A double-negative feedback loop formed by the morning genes CIRCADIAN CLOCK ASSOCIATED1 (CCA1)/LATE ELONGATED HYPOCOTYL (LHY) and the evening gene TIMING OF CAB EXPRESSION1 (TOC1) contributes to regulation of the circadian clock in Arabidopsis. A 24-h circadian cycle starts with the peak expression of CCA1 at dawn. Although CCA1 is targeted by multiple transcriptional repressors, including PSEUDO-RESPONSE REGULATOR9 (PRR9), PRR7, PRR5 and CCA1 HIKING EXPEDITION (CHE), activators of CCA1 remain elusive. Here we use mathematical modelling to infer a co-activator role for LIGHT-REGULATED WD1 (LWD1) in CCA1 expression. We show that the TEOSINTE BRANCHED 1-CYCLOIDEA-PCF20 (TCP20) and TCP22 proteins act as LWD-interacting transcriptional activators. The concomitant binding of LWD1 and TCP20/TCP22 to the TCP-binding site in the CCA1 promoter activates CCA1. Our study reveals activators of the morning gene CCA1 and provides an action mechanism that ensures elevated expression of CCA1 at dawn to sustain a robust clock.

In this work, a computational framework is presented to compute the time evolution of force constants for a coarse grained (CG) elastic network model along an all-atom molecular dynamics trajectory of a protein system. Obtained via matching distance fluctuations, these force constants represent strengths of mechanical coupling between CG beads. Variation of coupling strengths with time is hence termed the fluctuogram of protein dynamics. In addition to the schematic procedure and implementation considerations, several ways of combining force constants and data analysis are presented to illustrate the potential application of protein fluctuograms. The unique angle provided by the fluctuogram expands the scope of atomistic simulations and is expected to impact upon fundamental understanding of protein dynamics as well as protein engineering technologies., (© 2016 Elsevier Inc. All rights reserved.)

Deoxygenation in the global ocean is predicted to induce ecosystem-wide changes. Analysis of multidecadal oxygen time-series projects the northeast Pacific to be a current and future hot spot of oxygen loss. However, the response of marine communities to deoxygenation is unresolved due to the lack of applicable data on component species. We repeated the same benthic transect (n = 10, between 45 and 190 m depths) over 8 years in a seasonally hypoxic fjord using remotely operated vehicles equipped with oxygen sensors to establish the lower oxygen levels at which 26 common epibenthic species can occur in the wild. By timing our surveys to shoaling hypoxia events, we show that fish and crustacean populations persist even in severe hypoxia (<0.5 mL L(-1) ) with no mortality effects but that migration of mobile species occurs. Consequently, the immediate response to hypoxia expansion is the collapse of community structure; normally partitioned distributions of resident species coalesced and localized densities increased. After oxygen renewal and formation of steep oxygen gradients, former ranges re-established. High frequency data from the nearby VENUS subsea observatory show the average oxygen level at our site declined by ~0.05 mL L(-1) year(-1) over the period of our study. The increased annual duration of the hypoxic (<1.4 mL L(-1) ) and severely hypoxic periods appears to reflect the oxygen dynamics demonstrated in offshore source waters and the adjacent Strait of Georgia. Should the current trajectory of oxygen loss continue, community homogenization and reduced suitable habitat may become the dominant state of epibenthic systems in the northeast Pacific. In situ oxygen occurrences were not congruent with lethal and sublethal hypoxia thresholds calculated across the literature for major taxonomic groups indicating that research biases toward laboratory studies on Atlantic species are not globally applicable. Region-specific hypoxia thresholds are necessary to predict future impacts of deoxygenation on marine biodiversity., (© 2015 John Wiley & Sons Ltd.)

To better understand the adsorption of long-chain poly(1 → 4)-β-D-glucans on carbon surfaces as well as interactions responsible for this adsorption, we use a comparative study involving mesoporous carbon-silica composite materials that have been etched to varying degrees and all-atom molecular dynamics simulations. The materials synthesized as part of this etching study consist of an as-synthesized composite material (MCN-MSN), MCN-MSN-0.5 (composite materials consisting of 50% carbon by mass), MCN-MSN-0.3 (composite materials consisting of 70% carbon by mass), and MCN, in which silica etching was conducted using an aqueous ethanolic solution of either NaOH or HF. Data for the adsorption of long-chain glucans to these materials from concentrated aqueous HCl (37 wt %) solution demonstrate a direct relationship between the amount of β-glu adsorption and the magnitude of exposed carbon mesopore surface area, which systematically increases and is also accompanied by an increase in the mesopore size during silica etching. This demonstrates β-glu adsorption as occurring on internal carbon mesopores rather than exclusively on the external carbon surface. These experimental data on adsorption were corroborated by molecular dynamics (MD) simulations of β-glu adsorption to a graphene bilayer separated by a distance of 3.2 nm, chosen to correspond to the carbon mesopore diameter of the experimental system. Simulation results using a variety of β-glu solvent systems demonstrate the rapid adsorption of a β-glu strand on the graphitic carbon surface via axial coupling and are consistent with experimentally observed trends in fast adsorption kinetics. Solvent-mediated effects such as small-scale hydrophobicity and preferential interactions with ions are shown to play important roles in modulating glucan adsorption to carbon surfaces, whereas experimental data on hydrophobically modified silica demonstrate that hydrophobicity in and of itself is insufficient to cause β-glu adsorption from concentrated aqueous HCl solution.

This study investigated the effect of nobiletin, a flavonoid found in citrus fruits, on the degeneration of dopaminergic (DA) neurons in a neurotoxin model of Parkinson's disease (PD). 1-Methyl-4-phenylpyridinium (MPP(+)) was unilaterally injected into the median forebrain bundle of rat brains (to generate a neurotoxin model of PD) with or without daily intraperitoneal injection of nobiletin. Our results showed that nobiletin treatment at 10 mg/kg bw, but not at 1 or 20 mg/kg bw, significantly protected DA neurons in the substantia nigra (SN) of MPP(+)-treated rats. In parallel to the neuroprotection, nobiletin treatment at 10 mg/kg inhibited microglial activation and preserved the expression of the glial cell line-derived neurotrophic factor, which is a therapeutic agent against PD, in the SN. These results suggest that the proper supplementation with nobiletin may protect against the neurodegeneration involved in PD.

Fluctuating hydrodynamics (FHD) is a general framework of mesoscopic modeling and simulation based on conservational laws and constitutive equations of linear and nonlinear responses. However, explicit representation of electrical forces in FHD has yet to appear. In this work, we devised an Ansatz for the dynamics of dipole moment densities that is linked with the Poisson equation of the electrical potential ϕ in coupling to the other equations of FHD. The resulting ϕ-FHD equations then serve as a platform for integrating the essential forces, including electrostatics in addition to hydrodynamics, pressure-volume equation of state, surface tension, and solvent-particle interactions that govern the emergent behaviors of molecular systems at an intermediate scale. This unique merit of ϕ-FHD is illustrated by showing that the water dielectric function and ion hydration free energies in homogeneous and heterogenous systems can be captured accurately via the mesoscopic simulation. Furthermore, we show that the field variables of ϕ-FHD can be mapped from the trajectory of an all-atom molecular dynamics simulation such that model development and parametrization can be based on the information obtained at a finer-grained scale. With the aforementioned multiscale capabilities and a spatial resolution as high as 5 Å, the ϕ-FHD equations represent a useful semi-explicit solvent model for the modeling and simulation of complex systems, such as biomolecular machines and nanofluidics.

The analytical expression for the trajectory entropy of the overdamped Langevin equation is derived via two approaches. The first route goes through the Fokker-Planck equation that governs the propagation of the conditional probability density, while the second method goes through the path integral of the Onsager-Machlup action. The agreement of these two approaches in the continuum limit underscores the equivalence between the partial differential equation and the path integral formulations for stochastic processes in the context of trajectory entropy. The values obtained using the analytical expression are also compared with those calculated with numerical solutions for arbitrary time resolutions of the trajectory. Quantitative agreement is clearly observed consistently across different models as the time interval between snapshots in the trajectories decreases. Furthermore, analysis of different scenarios illustrates how the deterministic and stochastic forces in the Langevin equation contribute to the variation in dynamics measured by the trajectory entropy.

We propose to quantify the trajectory entropy of a dynamic system as the information content in excess of a free-diffusion reference model. The space-time trajectory is now the dynamic variable, and its path probability is given by the Onsager-Machlup action. For the time propagation of the overdamped Langevin equation, we solved the action path integral in the continuum limit and arrived at an exact analytical expression that emerged as a simple functional of the deterministic mean force and the stochastic diffusion. This work may have direct implications in chemical and phase equilibria, bond isomerization, and conformational changes in biological macromolecules as well transport problems in general.

Single-molecule Fӧrster-type resonance energy transfer (smFRET) is a unique technique capable of following conformational motions of individual protein molecules. The direct observation of individual proteins provides rich information that would be washed away in ensemble measurements, hence opening up new avenues for establishing the protein structure-function relationships through dynamics. Retrieving dynamics information of biomolecular motions via smFRET, though, requires careful experiment design and rigorous treatment of single-molecule statistics. Here, we describe the rudimentary steps for an smFRET experiment, including sample preparation for the microscope, building of critical parts for single-molecule FRET detection, and a robust methodology for photon-by-photon data analysis.

The dynamics of a protein along a well-defined coordinate can be formally projected onto the form of an overdamped Lagevin equation. Here, we present a comprehensive statistical-learning framework for simultaneously quantifying the deterministic force (the potential of mean force, PMF) and the stochastic force (characterized by the diffusion coefficient, D) from single-molecule Förster-type resonance energy transfer (smFRET) experiments. The likelihood functional of the Langevin parameters, PMF and D, is expressed by a path integral of the latent smFRET distance that follows Langevin dynamics and realized by the donor and the acceptor photon emissions. The solution is made possible by an eigen decomposition of the time-symmetrized form of the corresponding Fokker-Planck equation coupled with photon statistics. To extract the Langevin parameters from photon arrival time data, we advance the expectation-maximization algorithm in statistical learning, originally developed for and mostly used in discrete-state systems, to a general form in the continuous space that allows for a variational calculus on the continuous PMF function. We also introduce the regularization of the solution space in this Bayesian inference based on a maximum trajectory-entropy principle. We use a highly nontrivial example with realistically simulated smFRET data to illustrate the application of this new method.

Paced breathing is a frequently performed technique for cardiovascular autonomic studies. The relative timing of inspiration and expiration during paced breathing, however, is not consistent. We, therefore, examined whether indexes of heart rate variability and spontaneous baroreflex sensitivity would be affected by the respiratory time ratio that is set. We studied 14 healthy young adults who controlled their breathing rates to either 0.1 or 0.25 Hz in the supine and sitting positions. Four different inspiratory-to-expiratory time ratios (I/E) (uncontrolled, 1:1, 1:2, and 1:3) were examined for each condition in a randomized order. The results showed spectral indexes of heart rate variability and spontaneous baroreflex sensitivity were not influenced by the I/E that was set during paced breathing under supine and sitting positions. Porta's and Guzik's indexes of heart rate asymmetry were also not different at various I/E during 0.1-Hz breathing, but had larger values at 1:1 during 0.25-Hz breathing, although significant change was found in the sitting position only. At the same time, Porta's and Guzik's indexes obtained during 0.1-Hz breathing were greater than during 0.25-Hz breathing in both positions. The authors suggest that setting the I/E during paced breathing is not necessary when measuring spectral indexes of heart rate variability and spontaneous baroreflex sensitivity under the conditions used in this study. The necessity of paced breathing for the measurement of heart rate asymmetry, however, requires further investigation.

Interprotein and enzyme-substrate couplings in interfacial biocatalysis induce spatial correlations beyond the capabilities of classical mass-action principles in modeling reaction kinetics. To understand the impact of spatial constraints on enzyme kinetics, we developed a computational scheme to simulate the reaction network of enzymes with the structures of individual proteins and substrate molecules explicitly resolved in the three-dimensional space. This methodology was applied to elucidate the rate-limiting mechanisms of crystalline cellulose decomposition by cellobiohydrolases. We illustrate that the primary bottlenecks are slow complexation of glucan chains into the enzyme active site and excessive enzyme jamming along the crowded substrate. Jamming could be alleviated by increasing the decomplexation rate constant but at the expense of reduced processivity. We demonstrate that enhancing the apparent reaction rate required a subtle balance between accelerating the complexation driving force and simultaneously avoiding enzyme jamming. Via a spatiotemporal systems analysis, we developed a unified mechanistic framework that delineates the experimental conditions under which different sets of rate-limiting behaviors emerge. We found that optimization of the complexation-exchange kinetics is critical for overcoming the barriers imposed by interfacial confinement and accelerating the apparent rate of enzymatic cellulose decomposition.

The evaluation of the Fisher information matrix for the probability density of trajectories generated by the over-damped Langevin dynamics at equilibrium is presented. The framework we developed is general and applicable to any arbitrary potential of mean force where the parameter set is now the full space dependent function. Leveraging an innovative Hermitian form of the corresponding Fokker-Planck equation allows for an eigenbasis decomposition of the time propagation probability density. This formulation motivates the use of the square root of the equilibrium probability density as the basis for evaluating the Fisher information of trajectories with the essential advantage that the Fisher information matrix in the specified parameter space is constant. This outcome greatly eases the calculation of information content in the parameter space via a line integral. In the continuum limit, a simple analytical form can be derived to explicitly reveal the physical origin of the information content in equilibrium trajectories. This methodology also allows deduction of least informative dynamics models from known or available observables that are either dynamical or static in nature. The minimum information optimization of dynamics is performed for a set of different constraints to illustrate the generality of the proposed methodology.

We examine how the catalytic domain of a glycoside hydrolase family 7 endoglucanase catalytic domain (Cel7B CD) facilitates complexation of cellulose chains from a crystal surface. With direct relevance to the science of biofuel production, this problem also represents a model system of biopolymer processing by proteins in Nature. Interactions of Cel7B CD with a cellulose microfibril along different paths of complexation are characterized by mapping the atomistic fluctuations recorded in free-energy simulations onto the parameters of a coarse-grain model. The resulting patterns of protein-biopolymer couplings also uncover the sequence signatures of the enzyme in peeling off glucan chains from the microfibril substrate. We show that the semiopen active site of Cel7B CD exhibits similar barriers and free energies of complexation over two distinct routes; namely, scooping of a chain into the active-site cleft and threading from the chain end into the channel. On the other hand, the complexation energetics strongly depends on the surface packing of the targeted chain and the resulting interaction sites with the enzyme. A revealed principle is that Cel7B CD facilitates cellulose deconstruction via adaptive coupling to the emergent substrate. The flexible, peripheral segments of the protein outside of the active-site cleft are able to accommodate the varying features of cellulose along the simulated paths of complexation. The general strategy of linking physics-based molecular interactions to protein sequence could also be helpful in elucidating how other protein machines process biopolymers.

Background: This study aimed to investigate whether the endogenous active levels of MMP-9 or tissue inhibitor of metalloproteinases-1 (TIMP-1) were related to indices of diastolic dysfunction (DD) in the setting of contemporary treatment of coronary artery disease (CAD)., Methods and Results: We prospectively studied 116 patients with CAD and preserved left ventricular LV systolic function (ejection fraction ≥ 45%). All patients were free of heart failure symptoms at recruitment and underwent percutaneous intervention (PCI) of culprit lesions. Demographic and angiographic characteristics were collected. Plasma samples were analysed for the active form of MMP-9 and TIMP-1 using enzyme-linked immunosorbent assay-based isoform sensitive assays. Conventional and tissue Doppler-echocardiographic assessment of diastolic filling was undertaken with measurements of maximal early (E) and late (A) transmitral velocities in diastole, E/A ratio, E-wave deceleration time, isovolumic relaxation time, peak systolic (S), diastolic (D) and atrial reversal velocities of pulmonary venous flow, S/D fraction, time difference between A and duration of atrial reversal flow, early diastolic peak velocities of the lateral mitral annulus (E') and E/E'. Active MMP-9 level was higher in patients with more severe phases of DD (normal [n=22]: median 0.57 ng/ml; mild [n=19] 0.83 ng/ml; mild-moderate [n=41] 0.64 ng/ml; moderate or severe [n=34] 1.63 ng/ml; p<0.0001 for trend). Three month post-PCI elevated levels of active MMP-9 had an adjusted odds ratio of 11.2 (2.3-56.0, p<0.004) for association with moderate or severe DD., Conclusion: Elevated active MMP-9 level is associated with more severe DD in patients with CAD and preserved systolic function, which may indicate abnormal extracellular matrix metabolism in myocardial ischaemia., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

The development of a hybrid fluctuating hydrodynamics (FHD) and molecular dynamics (MD) simulation method that combines the molecular dynamics of moving particles with the fluctuating hydrodynamics of solvent fields on Eulerian grid cells is presented. This method allows resolution of solute-solvent interfaces and realization of excluded volumes of particles in the presence of hydrodynamic coupling. With these capabilities, we show that the ubiquitous forces mediated by the solvent, hydrophobicity and hydrodynamics, can be linked in a mesoscopic simulation. The strategies we devise to overcome the numerical issues of mixing variables in the Eulerian and Lagrangian coordinate systems, i.e., using a pair of auxiliary fluids to realize the excluded volumes of particles and assigning collocating gridding systems on solutes to interface with solvent fields, are also presented. Simulation results show that the hybrid FHD and MD method can reproduce the solvation free energies and scaling laws of particles dynamics for hydrophobes of different sizes. The collapse of two hydrophobic particles was also simulated to illustrate that the hybrid FHD and MD method has the potential to be generally applied to study nanoscale self-assembly and dynamics-structure-function relationships of biomolecules.

Naturally occurring cellulose is crystalline as a consequence of the strong interactions between the glucan chains that comprise it and therefore is insoluble in most solvents. One of the few solvent systems able to dissolve cellulose is lithium chloride (LiCl) dissolved in N,N-dimethylacetamide (DMA). By an integrated application of all-atom molecular dynamics (MD) simulations, reaction path optimization, free-energy calculations, and a force-matching analysis of coarse-grained atomistic simulations, we establish that DMA-mediated preferential interactions of Li(+) cations and Cl(-) anions with glucan chains enable cellulose dissolution in LiCl/DMA. The relatively weak solvation of Li(+), Cl(-), and glucan chains by DMA results in strong effective interactions of Li(+) and Cl(-) ions with the glucans, leading to cellulose dissolution. The small size of the Li(+) cations allows them to strongly couple to multiple interaction sites on the glucan chains of cellulose, including the spatially restricted regions around the ether linkages connecting neighboring glucose residues. Li(+) cations were thus identified as the main component responsible for driving cellulose dissolution. The mechanism for explaining the solubility of cellulose in the LiCl/DMA system deduced from the analysis of atomistic-scale simulations conducted in this work is also consistent with most of the empirical observations related to cellulose dissolution in salt/amide solvent systems.

This work illustrates that fluctuating hydrodynamics (FHD) simulations can be used to capture the thermodynamic and hydrodynamic responses of molecular fluids at the nanoscale, including those associated with energy and heat transfer. Using all-atom molecular dynamics (MD) trajectories as the reference data, the atomistic coordinates of each snapshot are mapped onto mass, momentum, and energy density fields on Eulerian grids to generate a corresponding field trajectory. The molecular length-scale associated with finite molecule size is explicitly imposed during this coarse-graining by requiring that the variances of density fields scale inversely with the grid volume. From the fluctuations of field variables, the response functions and transport coefficients encoded in the all-atom MD trajectory are computed. By using the extracted fluid properties in FHD simulations, we show that the fluctuations and relaxation of hydrodynamic fields quantitatively match with those observed in the reference all-atom MD trajectory, hence establishing compatibility between the atomistic and field representations. We also show that inclusion of energy transfer in the FHD equations can more accurately capture the thermodynamic and hydrodynamic responses of molecular fluids. The results indicate that the proposed MD-to-FHD mapping with explicit consideration of finite molecule size provides a robust framework for coarse-graining the solution phase of complex molecular systems.

A Staggered LATtice (SLAT) model is developed for modeling cellulose microfibrils. The simple representation of molecular packing and interactions employed in SLAT allows simulations of structure fluctuations and phase transition of cellulose microfibrils at sufficiently long and large scales for comparison with experiments. Glucan chains in the microfibril are modeled as connected monomers, each corresponding to a cellobiose subunit, and the surrounding space around the cellulose is composed of solvent cells. Interaction parameters of monomer-monomer interactions were parametrized based on the results of atomistic molecular dynamics simulations. The monomer-solvent interaction was optimized to give a melting temperature of ∼695 K for the 36-glucan chain model cellulose microfibril, which is consistent with the estimation based on experimental data. Monte Carlo simulations of the SLAT model also capture experimentally measured X-ray diffraction patterns of cellulose as a function of temperature, including the region of melting transition, as well as predict the highly flexible regions in the microfibril. Beyond the diameter of ∼3 nm, we found that melting temperature of the cellulose microfibril is not significantly shifted by changing the thickness. On the other hand, a slight decrease in the degree of polymerization of glucan chains is shown to enhance structure fluctuations through the ends of glucan chains, i.e., the defect sites, and thereby significantly reduce the melting temperature. Analysis of the sizes, densities, and lifetimes of defect structures in the microfibril indicates a significant extent of fluctuations on the surfaces even at room temperature and that defect statistics are strong but distinct functions of temperature and solvent quality. The SLAT model is the first of its kind for simulating cellulosic materials, and this work shows that it can be used to incorporate information obtained from atomistic simulations and experimental data to enable the aforementioned findings through computation.

The entropic driving forces of cellulose dissolution in water and in the ionic liquid 1-butyl-3-methylimidazolium chloride (BmimCl) are investigated via molecular dynamics simulations and the two-phase thermodynamic model. An atomistic model of cellulose was simulated at a dissociated state and a microfibril state to represent dissolution. The calculated values of entropy and internal energy changes between the two states inform the interplay of energetic and entropic driving forces in cellulose dissolution. In both water and BmimCl, we found that the entropy associated with the solvent degrees of freedom (DOF) decreases upon cellulose dissolution. However, solvent entropy reduction in BmimCl is much smaller than that in water and counteracts the entropy gain from the solute DOF to a much lesser extent. Solvent entropy reduction in water also plays a major role in making the free energy change of cellulose dissolution unfavorable at room temperature. In BmimCl, the interaction energies between solvent molecules and glucan chains and the total entropy change both contribute favorably to the dissolution free energy of cellulose. Calculations at different temperatures in the two solvents indicate that changes in total internal energy are a good indicator of the sign of the free energy change of cellulose dissolution.

A mechanical view provides an attractive alternative for predicting the behavior of complex systems since it circumvents the resource-intensive requirements of atomistic models; however, it remains extremely challenging to characterize the mechanical responses of a system at the molecular level. Here, the structural distribution is proposed to be an effective means to extracting the molecular mechanical properties. End-to-end distance distributions for a series of short poly-L-proline peptides with the sequence P(n)CG(3)K-biotin (n = 8, 12, 15 and 24) were used to experimentally illustrate this new approach. High-resolution single-molecule Förster-type resonance energy transfer (FRET) experiments were carried out and the conformation-resolving power was characterized and discussed in the context of the conventional constant-time binning procedure for FRET data analysis. It was shown that the commonly adopted theoretical polymer models-including the worm-like chain, the freely jointed chain, and the self-avoiding chain-could not be distinguished by the averaged end-to-end distances, but could be ruled out using the molecular details gained by conformational distribution analysis because similar polymers of different sizes could respond to external forces differently. Specifically, by fitting the molecular conformational distribution to a semi-flexible polymer model, the effective persistence lengths for the series of short poly-L-proline peptides were found to be size-dependent with values of ~190 Å, ~67 Å, ~51 Å, and ~76 Å for n = 8, 12, 15, and 24, respectively. A comprehensive computational modeling was carried out to gain further insights for this surprising discovery. It was found that P(8) exists as the extended all-trans isomaer whereas P(12) and P(15) predominantly contained one proline residue in the cis conformation. P(24) exists as a mixture of one-cis (75%) and two-cis (25%) isomers where each isomer contributes to an experimentally resolvable conformational mode. This work demonstrates the resolving power of the distribution-based approach, and the capacity of integrating high-resolution single-molecule FRET experiments with molecular modeling to reveal detailed structural information about the conformation of molecules on the length scales relevant to the study of biological molecules.