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Graphical abstract A series of water-soluble fullerene derivatives are synthesized to produce antitumor effects on three types of lung cancer cells. The fullerene compounds with sulfur-containing aromatic rings showed the most effective inhibition on cell proliferation among all fullerene compounds. The quantitative structure–activity relationship (QSAR) models illustrate that thiophene moiety in the fullerene cage has positive correlation with the antitumor effect of the fullerene derivatives., Water-soluble fullerene derivatives are actively investigated as potential drugs for cancer treatment due to their favorable membranotropic properties. Herein, cytotoxic effects of twenty fullerene derivatives with different solubilizing addends were evaluated in three different types of non-small-cell lung carcinoma (NSCLC). The potential structural descriptors of the solubilizing addends related to the inhibitory activities on each type of lung cancer cell were investigated by the quantitative structure–activity relationship (QSAR) approach. The determination coefficient r2 for the recommended QSAR model were 0.9325, 0.8404, and 0.9011 for A549, H460, and H1299 cell lines, respectively. The results revealed that the chemical features of the fullerene-based compounds including aromatic bonds, sulfur-containing aromatic rings, and oxygen atoms are favored properties and promote the inhibitory effects on H460 and H1299 cells. Particularly, thiophene moiety is the key functional group, which was positively correlated with strong inhibitory effects on the three types of lung cancer cells. The useful information obtained from our regression models may lead to the design of more efficient inhibitors of the three types of NSCLC.

Background Nanotechnology-based strategies in the treatment of cancer have potential advantages because of the favorable delivery of nanoparticles into tumors through porous vasculature. Materials and Methods In the current study, we synthesized a series of water-soluble fullerene derivatives and observed their anti-tumor effects on human lung carcinoma A549 cell lines. The quantitative structure-activity relationship (QSAR) modeling was employed to investigate the relationship between anticancer effects and descriptors relevant to peculiarities of molecular structures of fullerene derivatives. Results In the QSAR regression model, the evaluation results revealed that the determination coefficient r2 and leave-one-out cross-validation q2 for the recommended QSAR model were 0.9966 and 0.9246, respectively, indicating the reliability of the results. The molecular modeling showed that the lack of chlorine atom and a lower number of aliphatic single bonds in saturated hydrocarbon chains may be positively correlated with the lung cancer cytotoxicity of fullerene derivatives. Synthesized water-soluble fullerene derivatives have potential functional groups to inhibit the proliferation of lung cancer cells. Conclusion The guidelines obtained from the QSAR model might strongly facilitate the rational design of potential fullerene-based drug candidates for lung cancer therapy in the future.

Kinematic lensing (KL) is a new cosmological measurement technique that combines traditional weak lensing (WL) shape measurements of disc galaxies with their kinematic information. Using the Tully-Fisher relation KL breaks the degeneracy between intrinsic and observed ellipticity and significantly reduces the impact of multiple systematics that are present in traditional WL. We explore the performance of KL given the instrument capabilities of the $\textit{Roman Space Telescope}$, assuming overlap of the High Latitude Imaging Survey (HLIS), the High Latitude Spectroscopy Survey (HLSS) over 2,000 deg$^2$. Our KL suitable galaxy sample has a number density of $n_{\mathrm{gal}}=4~\mathrm{arcmin}^{-1}$ with an estimated shape noise level of $\sigma_{\epsilon}=0.035$. We quantify the cosmological constraining power on $\Omega_{\mathrm{m}}$-$S_8$, $w_p$-$w_a$ by running simulated likelihood analyses that account for redshift and shear calibration uncertainties, intrinsic alignment and baryonic feedback. Compared to a traditional WL survey we find that KL significantly improves the constraining power on $\Omega_{\mathrm{m}}$-$S_8$ (FoM$_{\mathrm{KL}}$=1.70FoM$_{\mathrm{WL}}$) and $w_p$-$w_a$ (FoM$_{\mathrm{KL}}$=3.65FoM$_{\mathrm{WL}}$). We also explore a "narrow tomography KL survey" using 30 instead of the default 10 tomographic bins, however we find no meaningful enhancement to the FoM even when assuming a significant time-dependence in our fiducial dark energy input scenarios.

One of the primary sources of uncertainties in modeling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for Stage-IV surveys requires knowledge of the matter power spectrum deep in the nonlinear regime at the percent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an extended covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a $\Lambda$CDM model using the extended halo model code {\tt HMCode} to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on $\Omega_m$ and $\sigma_8$ of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision., Comment: 10 pages, 5 figures

We explore synergies between the space-based Wide-Field Infrared Survey Telescope (WFIRST) and the ground-based Rubin Observatory Legacy Survey of Space and Time (LSST). In particular, we consider a scenario where the currently envisioned survey strategy for WFIRST's High Latitude Survey (HLS), i.e., 2000 square degrees in four narrow photometric bands is altered in favor of a strategy that combines rapid coverage of the LSST area (to full LSST depth) in one band. We find that a 5-month WFIRST survey in the W-band can cover the full LSST survey area providing high-resolution imaging for >95% of the LSST Year 10 gold galaxy sample. We explore a second, more ambitious scenario where WFIRST spends 1.5 years covering the LSST area. For this second scenario we quantify the constraining power on dark energy equation of state parameters from a joint weak lensing and galaxy clustering analysis, and compare it to an LSST-only survey and to the Reference WFIRST HLS survey. Our survey simulations are based on the WFIRST exposure time calculator and redshift distributions from the CANDELS catalog. Our statistical uncertainties account for higher-order correlations of the density field, and we include a wide range of systematic effects, such as uncertainties in shape and redshift measurements, and modeling uncertainties of astrophysical systematics, such as galaxy bias, intrinsic galaxy alignment, and baryonic physics. Assuming the 5-month WFIRST wide scenario, we find a significant increase in constraining power for the joint LSST+WFIRST wide survey compared to LSST Y10 (FoM(Wwide)= 2.4 FoM(LSST)) and compared to LSST+WFIRST HLS (FoM(Wwide)= 5.5 FoM(HLS)).

In the near future, the overlap of the Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) will present an ideal opportunity for joint cosmological dataset analyses. In this paper we simulate the joint likelihood analysis of these two experiments using six two-point functions derived from galaxy position, galaxy shear, and CMB lensing convergence fields. Our analysis focuses on realistic noise and systematics models and we find that the dark energy Figure-of-Merit (FoM) increases by 53% (92%) from LSST-only to LSST+SO in Year 1 (Year 6). We also investigate the benefits of using the same galaxy sample for both clustering and lensing analyses, and find the choice improves the overall signal-to-noise by ~30-40%, which significantly improves the photo-z calibration and mildly improves the cosmological constraints. Finally, we explore the effects of catastrophic photo-z outliers finding that they cause significant parameter biases when ignored. We develop a new mitigation approach termed "island model", which corrects a large fraction of the biases with only a few parameters while preserving the constraining power., 16 pages, 9 figures, 4 tables, matching MNRAS accepted version

Modifications of the matter power spectrum due to baryonic physics are one of the major theoretical uncertainties in cosmological weak lensing measurements. Developing robust mitigation schemes for this source of systematic uncertainty increases the robustness of cosmological constraints, and may increase their precision if they enable the use of information from smaller scales. Here we explore the performance of two mitigation schemes for baryonic effects in weak lensing cosmic shear: the PCA method and the halo-model approach in \textsc{HMcode}. We construct mock tomographic shear power spectra from four hydrodynamical simulations, and run simulated likelihood analyses with \textsc{CosmoLike} assuming LSST-like survey statistics. With an angular scale cut of $\ell_{\rm max} 10\ h^{-1}\mathrm{Mpc}$. We investigate variants of the PCA method and improve the bias mitigation through PCA by accounting for the noise properties in the data via Cholesky decomposition of the covariance matrix. Our improved PCA method allows us to retain more statistical constraining power while effectively mitigating baryonic uncertainties even for a broad range of baryonic physics scenarios., 29 pages, 19 figures, accepted to MNRAS

Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3$\times$2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modeling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude ($Q_1$) generally reflects the strength of baryon feedback. With the upper limit of $Q_1$ prior being bound by the Illustris feedback scenarios, we reach $\sim 20\%$ improvement in the constraint of $S_8=\sigma_8(\Omega_{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3$\times$2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative $Q_1$ prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than $2 \sigma$., Comment: 22 pages, 18 figures, 2 tables. accepted to MNRAS. A brief video summary of this paper is available at https://www.youtube.com/watch?v=QbeNwk5papU

Background Nanotechnology-based strategies in the treatment of cancer have potential advantages because of the favorable delivery of nanoparticles into tumors through porous vasculature. Materials and Methods In the current study, we synthesized a series of water-soluble fullerene derivatives and observed their anti-tumor effects on human lung carcinoma A549 cell lines. The quantitative structure–activity relationship (QSAR) modeling was employed to investigate the relationship between anticancer effects and descriptors relevant to peculiarities of molecular structures of fullerene derivatives. Results In the QSAR regression model, the evaluation results revealed that the determination coefficient r2 and leave-one-out cross-validation q2 for the recommended QSAR model were 0.9966 and 0.9246, respectively, indicating the reliability of the results. The molecular modeling showed that the lack of chlorine atom and a lower number of aliphatic single bonds in saturated hydrocarbon chains may be positively correlated with the lung cancer cytotoxicity of fullerene derivatives. Synthesized water-soluble fullerene derivatives have potential functional groups to inhibit the proliferation of lung cancer cells. Conclusion The guidelines obtained from the QSAR model might strongly facilitate the rational design of potential fullerene-based drug candidates for lung cancer therapy in the future.

Soft materials have been developed very rapidly in the biomedical field over the past 10 years because of advances in medical devices, cell therapy, and 3D printing for precision medicine. Smart polymers are one category of soft materials that respond to environmental changes. One typical example is the thermally-responsive polymers, which are widely used as cell carriers and in 3D printing. Self-healing polymers are one type of smart polymers that have the capacity to recover the structure after repeated damages and are often injectable through needles. Shape memory polymers are another type with the ability to memorize their original shape. These smart polymers can be used as cell/drug/protein carriers. Their injectability and shape memory performance allow them to be applied in bioprinting, minimally invasive surgery, and precision medicine. This review will describe the general materials design, characterization, as well as the current progresses and challenges of these smart polymers.

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM candidates that can dock well with multiple targets. Support vector machine (SVM) and multiple linear regression (MLR) methods were utilized to obtain predicted models. In particular, the deep learning method and the random forest (RF) algorithm were adopted. We achieved R2 values of 0.927 on the training set and 0.862 on the test set with deep learning and 0.869 on the training set and 0.890 on the test set with RF. Besides, comparative molecular similarity indices analysis (CoMSIA) was performed to get a predicted model. All of the training models achieved good results on the test set. The stability of GSK3β protein-ligand complexes was evaluated using 100 ns of MD simulation. Methyl 3- O-feruloylquinate and cynanogenin A induced both more compactness to the GSK3β complex and stable conditions at all simulation times, and the GSK3β complex also had no substantial fluctuations after a simulation time of 5 ns. For TCM molecules, we used the trained models to calculate predicted bioactivity values, and the optimum TCM candidates were obtained by ranking the predicted values. The results showed that methyl 3- O-feruloylquinate contained in Phellodendron amurense and cynanogenin A contained in Cynanchum atratum are capable of forming stable interactions with GSK3β.

Large-scale structure surveys allow us to constrain cosmology through the growth of structure and the expansion history. Together with Cosmic Microwave Background (CMB) measurements from the early Universe, the standard CDM with general relativity has become a concordance model that explains the origin and evolution of our Universe for over thirteen billion years. However, recent analyses reveal hints of tension between the early and late time Universe observables. The tension could point to newphysics beyond CDM, but could also be due to unknown systematic biases. Improving the precision and accuracy of cosmological measurements could be the key to a revolutionary new discovery about gravity on cosmological scales or about the nature dark energy. The challenges of cosmology in this era is to broadly explore and control possible systematics that could limit science from on going Stage III and future Stage IV surveys. In this thesis, we investigate astrophysical systematics of intrinsic alignment (IA) and the baryonic physics that would contaminate the weak lensing observables. Intrinsic alignment of galaxies arise under the effect of their local gravitational field. This effect mimics the shear correlation and introduces a systematic bias in the measured weak lensing signal.We carried out IA analyses using 8000 redMaPPer clusters. There are two types of alignment withinthe one-halo scale: the alignment of the central galaxy with respect to the host halo shape and the radial alignment of satellite galaxies toward the halo center. For the central galaxy alignment, the mean misalignment angle between the central and cluster major axes is measured to be 35 (random alignments give 45 ). The satellite galaxy alignment is a more subtle effect. We concluded that no net radial alignment signal is detected across the entire sample based on the re-Gaussianization shapes (the most conservative measurements). We also studied the dependence of IA signal on a total of 17 cluster and galaxy related properties in a concordant framework to properly account for parameterdegeneracies. With several predictors identified for central and satellite alignments inside galaxy clusters, our results suggest that small-scale IA is a complicated phenomenon potentially involving multiple relevant physical processes during galaxy and cluster formation and evolution history.The modification of the matter distribution due to baryonic physics is a non-negligible source of uncertainty for precision cosmology. Using various sets of hydrodynamical simulations, we investigated the effect of baryons on the matter power spectrum as functions of wavenumber frequency and redshift. We developed methodology to model the effect of baryons on weak lensing cosmic shearobservables through the principal component analysis (PCA). We constructed mock cosmic shear observables with contamination from different hydrodynamical scenarios, and then performed PCA on the contaminated mock observable vectors. The resulting principal component (PC) modes are then form a set of efficient bases to span uncertainties of baryons in the observable data vector space.Overall, our results suggest that the effects of baryonic physics on cosmic shear power spectra can beefficiently captured and mitigated using a few linear combinations of PC modes. Small-scale information in galaxy surveys has substantial statistical power to improve cosmological constraints, but conventional cosmological analyses discard this information to avoid biased inferenceon parameters due to lack of good astrophysical models for it. The continued developments on astrophysical modeling techniques in advance with better statistical quality of data will be essential in the new era of precision cosmology.

The shapes of cluster central galaxies are not randomly oriented, but rather exhibit coherent alignments with the shapes of their parent clusters as well as with the surrounding large-scale structures. In this work, we aim to identify the galaxy and cluster quantities that most strongly predict the central galaxy alignment phenomenon among a large parameter space with a sample of 8237 clusters and 94817 members within 0.1, Comment: matches version accepted to MNRAS; minor changes in presentation compared to v1, no changes to results

Catalase is an important enzyme, which performs the decomposition of two molecules of hydrogen peroxide into water molecules and oxygen, in an aerobic organism. Deficient or inactive catalase are implicated in cell damage and lead to inflammation, aging and cancer. In order to develop a novel natural product that prevents inactive catalase generation, the world largest traditional Chinese medicine (TCM) database (http://tcm.cmu.edu.tw/) was employed for this study, which combined high-throughput virtual screening and molecular dynamics (MD) simulation to investigate potent natural compounds for keeping catalase active. We found two natural products, hesperidin and 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (THSG), and they present high binding affinity with catalase. The results of MD simulation show that THSG is the most stable in trajectory analysis in all simulations. Moreover, THSG can make the catalase structure more compact during the process of MD simulation. In addition, the radical scavenging assay showed that THSG has more potential antioxidant activity than hesperidin. Therefore, we regard if the natural TCM compound, THSG, could be used to develop potential drugs that might have similar effect to keep catalase active and prevent the inactive form generation by hydrogen peroxide.

We study the orientations of satellite galaxies in redMaPPer clusters constructed from the Sloan Digital Sky Survey at $0.1, Comment: 25 pages, 16 figures, 7 tables, accepted to MNRAS. Main statistical analysis tool changed, with the results remain similar

Matrix metalloproteinase-9 (MMP-9) appears to play an important role in acute skin inflammation. Subantimicrobial dose of tetracycline has been demonstrated to inhibit the activity of MMP-9 protein. However, long-term use tetracycline will induce side effect. The catalytic site of MMP-9 is located at zinc-binding amino acids, His401, His405 and His411. We attempted to search novel medicine formula as MMP-9 inhibitors from traditional Chinese medicine (TCM) database by using in silico studies. We utilized high-throughput virtual screening to find which natural compounds could bind to the zinc-binding site. The quantitative structure-activity relationship (QSAR) models, which constructed by scaffold of MMP-9 inhibitors and its activities, were employed to predict the bio-activity of the natural compounds for MMP-9. The results showed that Celacinnine, Lobelanidine and Celallocinnine were qualified to interact with zinc-binding site and displayed well predictive activity. We found that celallocinnine was the best TCM compound for zinc binging sites of MMP-9 because the stable interactions were observed under dynamic condition. In addition, Celacinnine and Lobelanidine could interact with MMP-9 related protein that identified by drug-target interaction network analysis. Thus, we suggested the herbs Hypericum patulum, Sedum acre, and Tripterygium wilfordii that containing Celallocinnine, Celacinnine and Lobelanidine might be a novel medicine formula to avoid the side effect of tetracycline and increase the efficacy of treatment.

Viral infection by human immunodeficiency virus (HIV) requires integration of viral DNA with host DNA which involves the binding of HIV integrase (IN) with its co-factor lens epithelium-derived growth factor (LEDGF/p75). Since disrupted binding of IN with LEDGF/p75 inhibits proliferation of HIV, inhibition or denaturation of IN is a possible method for inhibiting HIV replication. D77 is a known drug with demonstrated inhibition against HIV by binding to IN. Herein, we utilized D77 as a control to screen for traditional Chinese medicine (TCM) compounds that exhibit similar atomic-level characteristics. 9-Hydroxy-(10E)-octadecenoic acid and Beauveriolide I were found to have higher Dock Scores to IN than D77 through virtual screening. Multiple linear regression (R2 = 0.9790) and support vector machine (R2 = 0.9114) models consistently predicted potential bioactivity of the TCM candidates against IN. The 40 ns molecular dynamics simulation showed that the TCM compounds fulfilled the drug-like criteria of forming stable complexes with IN. Atomic-level investigations revealed that 9-hydroxy-(10E)-octadecenoic acid bound to an important residue A:Lys173, and Beauveriolide I formed stable interactions with the core LEDGF binding site and with Asn256 of the IN binding site on LEDGF. The TCM candidates also initiated loss of α-helices that could affect the functionality of IN. Taken together, the ability of 9-hydroxy-(10E)-octadecenoic acid and Beauveriolide I to (1) form stable interactions affecting IN-LEDGF binding and (2) have predicted bioactivity against IN suggests that the TCM candidates might be potential starting structures for developing compounds that may disrupt IN-LEDGF binding. An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:40.

Nowadays, the occurrence of metabolic syndrome, which is characterized by obesity and clinical disorders, has been increasing rapidly over the world. It induces several serious chronic diseases such as cardiovascular disease, dyslipidemia, gall bladder disease, hypertension, osteoarthritis, sleep apnea, stroke, and type 2 diabetes mellitus. Peroxisome proliferator-activated receptors (PPARs), which have three isoforms: PPAR-α, PPAR-γ, and PPAR-δ, are key regulators of adipogenesis, lipid and carbohydrate metabolism, and are potential drug targets for treating metabolic syndrome. The traditional Chinese medicine (TCM) compounds from TCM Database@Taiwan ( http://tcm.cmu.edu.tw/ ) were employed to virtually screen for potential PPAR agonists, and structure-based pharmacophore models were generated to identify the key interactions for each PPAR protein. In addition, molecular dynamics (MD) simulation was performed to evaluate the stability of the PPAR-ligand complexes in a dynamic state. (S)-Tryptophan-betaxanthin and berberrubine, which have higher Dock Score than controls, form stable interactions during MD, and are further supported by the structure-based pharmacophore models in each PPAR protein. Key features include stable H-bonds with Thr279 and Ala333 of PPAR-α, with Thr252, Thr253 and Lys331 of PPAR-δ, and with Arg316 and Glu371 of PPAR-γ. Hence, we propose the top two TCM candidates as potential lead compounds in developing agonists targeting PPARs protein for treating metabolic syndrome.

(2011). iSMART: An Integrated Cloud Computing Web Server for Traditional Chinese Medicine for Online Virtual Screening, de novo Evolution and Drug Design. Journal of Biomolecular Structure and Dynamics: Vol. 29, No. 1, pp. 243-250.

The continuous advancement in molecular biology and information technology aided the development of a rich molecular simulation repertoire that can be applied in system biology, proteomics, molecular biology, bioinformatics, and materials science. We attempt to introduce the latest developments in drug design based on computational techniques, including protein structure modeling, docking, binding site prediction, quantitative structure–activity relationship (QSAR), and molecular dynamics simulation. Furthermore, a brief discussion on current docking issues, including accuracy of protein structure and protein–ligand interaction, is also included. Weight equation and rules and a new concept on flexibility are also described here as possible solution for these issues.

Peroxisome proliferator-activated receptors alpha, delta and gamma are a collection of ligand-activated transcription factors crucial in lipid and glucose homeostasis. The involvement of these receptors in lipid metabolism makes them perfect therapeutic target for treating obesity and stroke. In this study, 'sum of activity' model was employed to design multi-target agonists. We used a new strategy to design agonists that fit both alpha and delta but not gamma, to avoid side effect. The CoMFA and CoMSIA models were used to explore the pharmacophore features by constructing three individual models: (a) alpha-model, (b) delta-model and (c) gamma-model, and two sum models: (d) alpha, delta- model, and (e) alpha, delta and gamma-model. The CoMFA model yielded a significant cross validation value, q(2), of 0.729 and non-cross validation value, r(2), of 0.933 in the alpha, delta-model. The CoMSIA studies yielded the best predictive models with q(2) of 0.622 in A+S and with r(2) of 0.911 in the alpha, delta-model. Finally, we proposed that distinct features shown in models (a), (b), (d) but not (c) and (e) should be accounted in designing weight-controlling drugs.

Human epidermal growth factor receptor 2, HER2, is a commonly over-expressed tyrosine kinase receptor found in many types of carcinoma. Despite that there are several HER2 inhibitors, namely Iressa, Tarceva and Tykerb, currently in clinical trials, all can cause several side effects. In this study, both structure-based and ligand-based drug design were employed to design novel HER2 inhibitors from traditional Chinese medicine (TCM). The HER2 structure model was built in homology modeling based on known receptors of the same family. Docking and de novo evolution experiments were performed to identify candidates and to build derivatives. A training set of 32 compounds with inhibitory activities to HER2 was used to formulate the pharmacophore hypotheses that were subsequently used to examine candidates obtained from the docking study. Hydrogen bond interactions, salt-bridge formations and pi-stacking were observed between the ligands and Phe731, Lys753, Asp863 and Asp808 of HER2 protein. Combining results from both docking and pharmacophore mapping analysis, CLC015-5, CLC604-11 and CLC604-18 were well accepted and consistent in both approaches and were considered as the most potential HER2 inhibitors.

AMP-activated protein kinase (AMPK) senses the cellular energy state by monitoring the AMP in various eukaryotes. The activated AMPK regulates a wide range of important pathways which cause diabetes, obesity, metabolic aberrant, and also breast cancer. In this study, the crystal structure of yeast AMPK was used as a template to construct the reliable homology model of human AMPK with corresponding binding site. The quantitative structure–activity relationship ( QSAR) models, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models, were utilized to obtain the pharmacophore feature. By virtual screening the data, the top eight phenylamide compounds screened by “ De novo evolution” were selected to design new ligands for AMPK. A total of 531 derivatives were generated subsequently. All the potent ligands, same as AMP and AICAR, carried H-bond interacting with the key interactive amino acids of AMPK. The features of the derivatives were also fitted with the CoMFA and CoMSIA models. Thus, these data suggested that the phenylamide derivates might be the potent AMPK agonists.

Using large samples containing nearly $2300$ active galaxies of low radio luminosity (1.4 GHz luminosity between $2\times 10^{23}$ and $3\times 10^{25}$ W/Hz, essentially low-excitation radio galaxies) at $z\lesssim 0.3$, we present a self-contained analysis of the dependence of the nuclear radio activity on both intrinsic and extrinsic properties of galaxies, with the goal of identifying the best predictors of the nuclear radio activity. While confirming the established result that stellar mass must play a key role in the triggering of radio activities, we point out that for central, most massive galaxies, the radio activity also shows a strong dependence on halo mass, which is unlikely due to enhanced interaction rates in denser regions in massive, cluster-scale halos. We thus further investigate the effects of various properties of the intracluster medium (ICM) in massive clusters on the radio activities, employing two standard statistical tools, Principle Component Analysis and Logistic Regression. It is found that ICM entropy, local cooling time, and pressure are the most effective in predicting the radio activity, pointing to the accretion of gas cooling out of a hot atmosphere to be the likely origin in triggering such activities in galaxies residing in massive dark matter halos. Our analysis framework enables us to logically discern the mechanisms responsible for the radio activity separately for central and satellite galaxies., AJ, in press; 11 pages, 9 figures, 8 tables. The radio source catalogs can be downloaded from https://tinyurl.com/yd3lnatk (table A1) and https://tinyurl.com/ycp2r34e (table A2)

An outbreak of influenza A virus subtype H1N1, also known as swine flu, in Mexico was occurred in April 2009. To design drugs for treating this epidemic is urgency. In this study, we employed the new sequences (2009) to build the N1 simulation structure by homology modeling, which has been checked for high reliability by Verify Score and Ramachandran plot. The latest H1 homology model was employed from Chen's report. 365,602 compounds from NCI database have been screened by docking study of H1 and N1, respectively. And then, nine candidates were screened and suggested as potent dual target candidates from the docking studies. In our investigation, drug resistance was found by our molecular simulation in the new N1 modeling structure to oseltamivir. However, the mechanism is still not clear; further clinical investigations are urgently required.

In this study, we aimed at producing a MFNP consist of a dielectric core encapsulated fluorescein coating by a thin gold shell, which facilitate it with highly favorable optical and chemical properties for biomedical imaging and therapeutic applications. We have composed a novel multi-functional nanodesign, Sample A3, with fluorescein encapsulated in SiO2 nanoparticles with gold shell. The A3 nanoparticles were uniform in size, no aggregation, homogeneous and stable in the solution under the SEM image system. We have also investigated its cytotoxicity to A10 human smooth muscle cell line by MTT assay, and found that the novel sample A3 were of little cytotoxic to the cells. In conclusion, we have proposed a novel method of preparing multi-functional nanoparticles assemblies, which has added fluorescein to SiO2 in a gold shell, to make it simultaneously have the functions of photothermal therapy and tracer.

To explore the potent inhibitor from one of the Traditional Chinese medicine (TCM), Epimedium sagittatum. We predicted the potent compound, ES03b, de novo evolution from the four Epimedium sagittatum components were verified by molecular docking, pharmacophore analysis, and analysis of quantitative structure-activity relationship (QSAR) model, which was constructed by multiple linear regression. ES03b was chosen to undergo drug modification via de novo evolution. By analyzing the pharmacophore features, we found that the hydrophobic core in the binding site and the hydrogen bond generated at Asn663 played key roles in designing PDE5 inhibitors. ES03b generated 49 diversities (Evo01-49). Evo48 had high activity in prediction. Although the value of prediction was overestimated, Evo48 was suggested as the potent lead. In this study, we showed that the hydrophobic core in the binding site and hydrogen bond production on Asn663 played key roles to design PDE5 inhibitors. From several require validation analysis, Evo48 was suggested to be a potent inhibitor.

Background This study identified susceptible loci related to the Yu-Zhi (YZ) constitution, which indicates stasis-stagnation, found in a genome-wide association study (GWAS) in patients with type 2 diabetes and possible regulated traditional Chinese medicine (TCM) using docking and molecular dynamics (MD) simulation. Methods Non-aboriginal Taiwanese with type 2 diabetes were recruited. Components of the YZ constitution were assessed by a self-reported questionnaire. Genome-wide SNP genotypes were obtained using the Illumina HumanHap550 platform. The world’s largest TCM database (http://tcm.cmu.edu.tw/) was employed to investigate potential compounds for PON2 interactions. Results The study involved 1,021 unrelated individuals with type 2 diabetes. Genotyping data were obtained from 947 of the 1,021 participants. The GWAS identified 22 susceptible single nucleotide polymorphisms on 13 regions of 11 chromosomes for the YZ constitution. Genotypic distribution showed that PON2 on chromosome 7 was most significantly associated with the risk of the YZ constitution. Docking and MD simulation indicated 13-hydroxy-(9E_11E)-octadecadienoic acid was the most stable TCM ligand. Conclusions Risk loci occurred in PON2, which has antioxidant properties that might protect against atherosclerosis and hyperglycemia, showing it is a susceptible gene for the YZ constitution and possible regulation by 13-hydroxy-(9E_11E)-octadecadienoic acid. Electronic supplementary material The online version of this article (doi:10.1186/s12906-015-0761-x) contains supplementary material, which is available to authorized users.