• 제목/요약/키워드: molecular modeling

검색결과 415건 처리시간 0.029초

협업 가상현실 기반의 분자모델링 교육 시스템 (A Molecular Modeling Education System based on Collaborative Virtual Reality)

  • 김정호;이준;김형석;김지인
    • 한국컴퓨터그래픽스학회논문지
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    • 제14권4호
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    • pp.35-39
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    • 2008
  • 협업 시스템은 원거리의 다수 사용자들이 시간과 공간의 제약을 받지 않고 공동의 작업을 진행 할 수 있도록 서비스를 제공하는 시스템이다. 하지만 모든 시스템들이 그렇듯이 처음 사용할 때는 여러 가지 시행작오가 생기게 된다. 가상분자모델링 시스템인 VRMMS[1]는 여러 사용자들이 온라인상에서 분자 구조를 관찰하고 시뮬레이션 결과를 확인 할 수 있고 또한 피드백까지 줄 수 있는 협업시스템인데, 분자 모델을 연구하는 사람들에게는 유용한 시스템이지만 익숙하게 분자 구조를 조작하기 위해서는 인터페이스에 대한 학습이 필요하다. 가장 좋은 방법은 직접 만나서 교육을 시켜주는 것이지만 한 장소가 아닌 여러 장소에서 원격으로 실험을 진행하는 상황에서는 사용법에 대한 직접적인 교육이 힘들기 때문에 시행착오 기간이 길어질 수 있다. 본 논문에서는 이러한 시행착오를 줄이기 위해 가상의 현실세계인 세컨드라이프[2]를 통해 분자 구조를 관찰하고 시뮬레이션을 할 수 있는 협업시스템을 제안한다. 각각의 사용자들은 가상세계에서 자신의 아바타를 통해 가상분자모델링 시스템인 VRMMS를 사용하여 현실 세계에서와 같은 방식으로 실험을 진행할 수 있으며, 실험에 익숙하지 않는 사용자들은 실험방법을 직접 보고 따라 해 볼 수 있어서 좋은 학습효과를 기대할 수 있다.

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Molecular Modeling of Small Molecules as BVDV RNA-Dependent RNA Polymerase Allosteric Inhibitors

  • Chai, Han-Ha;Lim, Dajeong;Chai, Hee-Yeoul;Jung, Eunkyoung
    • Bulletin of the Korean Chemical Society
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    • 제34권3호
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    • pp.837-850
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    • 2013
  • Bovine viral diarrhea virus (BVDV), a major pathogen of cattle, is a well-characterized pestivirus which has been used as a good model virus for HCV. The RNA-dependent RNA polymerase (RdRp) plays a key role in the RNA replication process, thus it has been targeted for antivirus drugs. We employed two-dimensional quantitative structure-activity relationship (2D-QSAR) and molecular field analysis (MFA) to identify the molecular substructure requirements, and the particular characteristics resulted in increased inhibitory activity for the known series of compounds to act as effective BVDV inhibitors. The 2D-QSAR study provided the rationale concept for changes in the structure to have more potent analogs focused on the class of arylazoenamines, benzimidazoles, and acridine derivatives with an optimal subset of descriptors, which have significantly contributed to overall anti-BVDV activity. MFA represented the molecular patterns responsible for the actions of antiviral compound at their receptors. We conclude that the polarity and the polarizability of a molecule play a main role in the inhibitory activity of BVDV inhibitors in the QSAR modeling.

Identification of ${\omega}$-Aminotransferase from Caulobacter crescentus and Sitedirected Mutagenesis to Broaden Substrate Specificity

  • Hwang, Bum-Yeol;Ko, Seung-Hyun;Park, Hyung-Yeon;Seo, Joo-Hyun;Lee, Bon-Su;Kim, Byung-Gee
    • Journal of Microbiology and Biotechnology
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    • 제18권1호
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    • pp.48-54
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    • 2008
  • A putative ${\omega}$-aminotransferase gene, cc3143 (aptA), from Caulobacter crescentus was screened by bioinformatical tools and overexpressed in E. coli, and the substrate specificity of the ${\omega}$-aminotransferase was investigated. AptA showed high activity for short-chain ${\beta}$-amino acids. It showed the highest activity for 3-amino-n-butyric acid. It showed higher activity toward aromatic amines than aliphatic amines. The 3D model of the ${\omega}$-aminotransferase was constructed by homology modeling using a dialkylglycine decarboxylase (PDB ID: 1DGE) as a template. Then, the ${\omega}$-aminotransferase was rationally redesigned to increase the activity for 3-amino-3-phenylpropionic acid. The mutants N285A and V227G increased the relative activity for 3-amino-3-phenylpropionic acid to 3-amino-n-butyric acid by 11-fold and 3-fold, respectively, over that of wild type.

Pervaporation separation of polyion complex composite membranes for the separation of water/alcohol mixtures: characterization of permeation behavior by using molecular modeling techniques

  • Kim, Sang-Gyun;Lee, Yoon-Gyu;Jonggeon Jegal;Lee, Kew-Ho
    • 한국막학회:학술대회논문집
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    • 한국막학회 2003년도 The 4th Korea-Italy Workshop
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    • pp.91-94
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    • 2003
  • In this work, the physicochemical properties for permeant molecules and polyion complex membrane prepared by complexation between SA and chitosan were determined by using molecular modeling methods, and the permeation behaviors of water and alcohol molecules through the PIC membrane have been investigated. In the case of penetrant molecule, the experimental results showed that the prepared membrane was excellent pervaporation performance result in most solution, and the selectivity and permeability of the membrane were dependent on the molecular size, the polarity and the hydrophilic surface of permeant organics. However, the separation behavior of methanol aqueous solution exhibited other permeation tendency with other feed solutions and contradictory result. That is, the membrane were preferentially permeable to methanol over water despite water molecule has stronger polarity and small molecular size than methanol molecule. In this study, the results were discussed from the viewpoint of chemical and physical properties between permeant molecules and membrane in the diffusion state.

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Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells

  • Lee, Dohoon;Kim, Sun
    • Clinical and Experimental Pediatrics
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    • 제65권5호
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    • pp.239-249
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    • 2022
  • Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

Determination of Proper Time Step for Molecular Dynamics Simulation

  • 조종철;김병철
    • Bulletin of the Korean Chemical Society
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    • 제21권4호
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    • pp.419-424
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    • 2000
  • In this study we have investigated the determination of proper time step in molecular dynamics simulation.Since the molecular dynamics is mathematically related to nonlinear dynamics, the analysis of eigenvalues isused to explain the relationship between the time step and dynamics. The tracings of H2 and CO2 molecular dynamics simulation agrees very well with the analytical solutions. For H2, the time step less than 1.823 fs pro-vides stable dynamics. ForCO2, 3.808 fs might be the maximum time step for proper molecular dynamics. Al-though this results were derived for most simple cases of hydrogen and carbon dioxide, we could quantitatively explain why improperly large time step destroyed the molecular dynamics. From this study we could set the guide line of the proper time step for stable dynamics simulation in molecular modeling software.

iPSC technology-Powerful hand for disease modeling and therapeutic screen

  • Kim, Changsung
    • BMB Reports
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    • 제48권5호
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    • pp.256-265
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    • 2015
  • Cardiovascular and neurodegenerative diseases are major health threats in many developed countries. Recently, target tissues derived from human embryonic stem (hES) cells and induced pluripotent stem cells (iPSCs), such as cardiomyocytes (CMs) or neurons, have been actively mobilized for drug screening. Knowledge of drug toxicity and efficacy obtained using stem cell-derived tissues could parallel that obtained from human trials. Furthermore, iPSC disease models could be advantageous in the development of personalized medicine in various parts of disease sectors. To obtain the maximum benefit from iPSCs in disease modeling, researchers are now focusing on aging, maturation, and metabolism to recapitulate the pathological features seen in patients. Compared to pediatric disease modeling, adult-onset disease modeling with iPSCs requires proper maturation for full manifestation of pathological features. Herein, the success of iPSC technology, focusing on patient-specific drug treatment, maturation-based disease modeling, and alternative approaches to compensate for the current limitations of patient iPSC modeling, will be further discussed. [BMB Reports 2015; 48(5): 256-265]