• 제목/요약/키워드: molecular modeling

검색결과 417건 처리시간 0.022초

방선균에 의해 생산된 항 MRSA 항생물질 AM3의 구조 연구 (Structure Elucidation of a Potent Anti-MRSA Antibiotic, AM3, Produced by Streptomyces sp.)

  • 임융호;장준환;김종훈;서정우;정재경;이철훈
    • Applied Biological Chemistry
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    • 제38권6호
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    • pp.516-521
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    • 1995
  • 항 MRSA 물질을 찾기 위하여 찬국 해양 토양을 검색하였고, 거기서 분리된 방선균의 이차 대사물질 중 항 MRSA 효능을 보이는 물질을 AM3이라고 명명하고 이에 대한 연구를 하였다.

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Electrophysiological insights with brain organoid models: a brief review

  • Rian Kang;Soomin Park;Saewoon Shin;Gyusoo Bak;Jong-Chan Park
    • BMB Reports
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    • 제57권7호
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    • pp.311-317
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    • 2024
  • Brain organoid is a three-dimensional (3D) tissue derived from stem cells such as induced pluripotent stem cells (iPSCs) embryonic stem cells (ESCs) that reflect real human brain structure. It replicates the complexity and development of the human brain, enabling studies of the human brain in vitro. With emerging technologies, its application is various, including disease modeling and drug screening. A variety of experimental methods have been used to study structural and molecular characteristics of brain organoids. However, electrophysiological analysis is necessary to understand their functional characteristics and complexity. Although electrophysiological approaches have rapidly advanced for monolayered cells, there are some limitations in studying electrophysiological and neural network characteristics due to the lack of 3D characteristics. Herein, electrophysiological measurement and analytical methods related to neural complexity and 3D characteristics of brain organoids are reviewed. Overall, electrophysiological understanding of brain organoids allows us to overcome limitations of monolayer in vitro cell culture models, providing deep insights into the neural network complex of the real human brain and new ways of disease modeling.

그리드 서비스 기반 분자 다킹 어플리케이션 개발 (Development of Grid Service Based Molecular Docking Application)

  • 이화민;진성호;이종혁;박성빈;유헌창
    • 컴퓨터교육학회논문지
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    • 제9권4호
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    • pp.63-74
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    • 2006
  • 분자 다킹은 신약, 신소재, 고분자의 개발 과정에서 대규모의 화학분자 데이터베이스의 화학분자 데이터들을 실제 실험을 통하지 않고 시뮬레이션을 통해 한정된 화학 분자만을 스크링하는 과정이다. 분자 다킹은 대규모 컴퓨팅 파워와 데이터 저장 용량을 요구하는 대표적인 대규모의 과학 어플리케이션이다. 기존의 분자 다킹 어플리케이션들은 슈퍼컴퓨터, 클러스터, 워크스테이션 등을 이용하여 작업을 수행하도록 개발되었다. 하지만 슈퍼컴퓨터를 이용한 분자 다킹은 너무 많은 비용이 든다는 문제점이 있고, 클러스터나 워크스테이션을 이용한 분자 다킹은 오랜 수행 시간이 요구된다는 문제점을 가지고 있다. 이에 본 논문에서는 그리드 서비스 기반 분자 다킹 어플리케이션을 제안하였다. 이를 위해 본 논문에서는 효율적인 분자 다킹 서비스를 제공하기 위해 자원 브로커와 데이터 브로커를 설계하고, 분자 다킹을 위한 다양한 그리드 서비스들을 개발하였다.

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Molecular gas and star formation in early-type galaxies

  • Bureau, Martin
    • 천문학회보
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    • 제36권2호
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    • pp.65-65
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    • 2011
  • Early-type galaxies represent the end point of galaxy evolution and, despite pervasive residual star formation, are generally considered "red and dead", that is composed exclusively of old stars with no star formation. Here, their molecular gas content is constrained and discussed in relation to their evolution, supporting the continuing importance of minor mergers and/or cold gas accretion. First, as part of the Atlas3D survey, the first complete, large, volume-limited survey of CO in normal early-type galaxies is presented. At least of 23% of local early-types possess a substantial amount of molecular gas, the necessary ingredient for star formation, independent of mass and environment but dependent on the specific stellar angular momentum. Second, using CO synthesis imaging, the extent of the molecular gas is constrained and a variety of morphologies is revealed. The kinematics of the molecular gas and stars are often misaligned, implying an external gas origin in over a third of all systems, more than half in the field, while external gas accretion must be shot down in clusters. Third, many objects appear to be in the process of forming regular kpc-size decoupled disks, and a star formation sequence can be sketched by piecing together multi-wavelength information on the molecular gas, current star formation, and young stars. Fourth, early-type galaxies do not seem to systematically obey all our usual prejudices regarding star formation (e.g. Schmidt-Kennicutt law, far infrared-radio continuum correlation), suggesting a greater diversity in star formation processes than observed in disk galaxies and the possibility of "morphological quenching". Lastly, a first step toward constraining the physical properties of the molecular gas is taken, by modeling the line ratios of density- and opacity-sensitive molecules in a few objects. Taken together, these observations argue for the continuing importance of (minor) mergers and cold gas accretion in local early-types, and they provide a much greater understanding of the gas cycle in the galaxies harbouring most of the stellar mass. In the future, better dust masses and dust-to-gas mass ratios from Herschel should allow to place entirely independent constraints on the gas supply, while spatially-resolved high-density molecular gas tracers observed with ALMA will probe the interstellar medium and star formation laws locally in a regime entirely different from that normally probed in spiral galaxies.

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Comparative Molecular Field Analysis of Dioxins and Dioxin-like Compounds

  • Ashek, Ali;Cho, Seung-Joo
    • Molecular & Cellular Toxicology
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    • 제1권3호
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    • pp.157-163
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    • 2005
  • Because of their widespread occurrence and substantial biological activity, halogenated aromatic hydrocarbons are one of the important classes of contaminants in the environment. We have performed comparative molecular field analysis (CoMFA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA models have given $q^{2}$ value of more than 0.5 and $r^{2}$ value of more than 0.83. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive $r^{2}$ values. Best predictions were obtained with CoMFA model of combined modified training set ($q^{2}=0.631,\;r^{2}=0.900$), giving predictive residual value = 0.002 log unit for the test compound. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands. The implications of the CoMFA/QSAR model presented herein are explored with respect to quantitative hazard identification of potential toxicants.

Aqueous Solubility Enhancement of Some Flavones by Complexation with Cyclodextrins

  • Kim, Hyun-Myung;Kim, Hyun-Won;Jung, Seun-Ho
    • Bulletin of the Korean Chemical Society
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    • 제29권3호
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    • pp.590-594
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    • 2008
  • The inclusion complexes of cyclodextrins (CDs) with flavones in aqueous solution were investigated by phase solubility measurements. The effect of b -cyclodextrin (b -CD), heptakis (2,6-di-O-methyl) b -cyclodextrin (DM-b -CD) and 2-hydroxypropyl-b -cyclodextrin (HP-b -CD) on the aqueous solubility of three flavones, namely, chrysin, apigenin and luteolin was investigated, respectively. Solubility enhancements of all flavones obtained with three CDs followed the rank order: HP-b -CD > DM-b -CD > b -CD, and besides, CDs show higher stability constant on luteolin than that on others flavones. 1H-nuclear magnetic resonance (NMR) spectroscopy and molecular modeling was used to help establish the model of interaction of the CDs with luteolin. NMR spectroscopic analysis suggested that A-C ring, and part of the B ring of luteolin display favorable interaction with the CDs, which was also confirmed by docking studies based on the molecular simulation. The observed augmentation of solubility of luteolin by three CDs was explained by the difference of electrostatic interaction of each complex, especially hydrogen bonding.

Artificial Neural Network Prediction of Normalized Polarity Parameter for Various Solvents with Diverse Chemical Structures

  • Habibi-Yangjeh, Aziz
    • Bulletin of the Korean Chemical Society
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    • 제28권9호
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    • pp.1472-1476
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    • 2007
  • Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

Molecular genetic decoding of malformations of cortical development

  • Lim, Jae Seok;Lee, Jeong Ho
    • Journal of Genetic Medicine
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    • 제12권1호
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    • pp.12-18
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    • 2015
  • Malformations of cortical development (MCD) cover a broad spectrum of developmental disorders which cause the various clinical manifestations including epilepsy, developmental delay, and intellectual disability. MCD have been clinically classified based on the disruption of developmental processes such as proliferation, migration, and organization. Molecular genetic studies of MCD have improved our understanding of these disorders at a molecular level beyond the clinical classification. These recent advances are resulted from the development of massive parallel sequencing technology, also known as next-generation sequencing (NGS), which has allowed researchers to uncover novel molecular genetic pathways associated with inherited or de novo mutations. Although an increasing number of disease-related genes or genetic variations have been identified, genotype-phenotype correlation is hampered when the biological or pathological functions of identified genetic variations are not fully understood. To elucidate the causality of genetic variations, in vivo disease models that reflect these variations are required. In the current review, we review the use of NGS technology to identify genes involved in MCD, and discuss how the functions of these identified genes can be validated through in vivo disease modeling.

Characterization of Binding Mode of the Heterobiaryl gp120 Inhibitor in HIV-1 Entry: A Molecular Docking and Dynamics Simulation Study

  • Gadhe, Changdev G.;Kothandan, Gugan;Cho, Seung Joo
    • Bulletin of the Korean Chemical Society
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    • 제34권8호
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    • pp.2466-2472
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    • 2013
  • Human immunodeficiency virus type-1 (HIV-1) is a causative agent of Acquired immunodeficiency syndrome (AIDS), which has affected a large population of the world. Viral envelope glycoprotein (gp120) is an intrinsic protein for HIV-1 to enter into human host cells. Molecular docking guided molecular dynamics (MD) simulation was performed to explore the interaction mechanism of heterobiaryl derivative with gp120. MD simulation result of inhibitor-gp120 complex demonstrated stability. Our MD simulation results are consistent with most of the previous mutational and modeling studies. Inhibitor has an interaction with the CD4 binding region. Van der Waals interaction between inhibitor and Val255, Thr257, Asn425, Met426 and Trp427 were important. This preliminary MD model could be useful in exploiting heterobiaryl-gp120 interaction in greater detail, and will likely to shed lights for further utilization in the development of more potent inhibitors.

From the Sequence to Cell Modeling: Comprehensive Functional Genomics in Escherichia coli

  • Mori, Hirotada
    • BMB Reports
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    • 제37권1호
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    • pp.83-92
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    • 2004
  • As a result of the enormous amount of information that has been collected with E. coli over the past half century (e.g. genome sequence, mutant phenotypes, metabolic and regulatory networks, etc.), we now have detailed knowledge about gene regulation, protein activity, several hundred enzyme reactions, metabolic pathways, macromolecular machines, and regulatory interactions for this model organism. However, understanding how all these processes interact to form a living cell will require further characterization, quantification, data integration, and mathematical modeling, systems biology. No organism can rival E. coli with respect to the amount of available basic information and experimental tractability for the technologies needed for this undertaking. A focused, systematic effort to understand the E. coli cell will accelerate the development of new post-genomic technologies, including both experimental and computational tools. It will also lead to new technologies that will be applicable to other organisms, from microbes to plants, animals, and humans. E. coli is not only the best studied free-living model organism, but is also an extensively used microbe for industrial applications, especially for the production of small molecules of interest. It is an excellent representative of Gram-negative commensal bacteria. E. coli may represent a perfect model organism for systems biology that is aimed at elucidating both its free-living and commensal life-styles, which should open the door to whole-cell modeling and simulation.