• Journal of the Korea Computer Graphics Society
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• v.14 no.4
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• pp.35-39
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• 2008

A computer supported collaborative system provides with a shared virtual workspace over the Internet where its remote users cooperate in order to achieve their goals by overcoming problems caused by distance and time. VRMMS (Virtual Reality Molecular Modeling System) [1] is a VR based collaborative system where biologists can remotely participate in and exercise molecular modeling tasks such as viewing three dimensional structures of molecular models, confirming results of molecular simulations and providing with feedbacks for the next simulations. Biologists can utilize VRMMS in executing molecular simulations. However, first-time users and beginners need to spend some time for studying and practicing in order to skillfully manipulate molecular models and the system. The best way to resolve the problem is to have a face-to-face session of teaching and learning VRMMS. However, it is not practically recommended in the sense that the users are remotely located. It follows that the learning time could last longer than desired. In this paper, we propose to use Second Life [2] combining with VRMMS for removing the problem. It can be used in building a shared workplace over the Internet where molecular simulations using VRMMS can be exercised, taught, learned and practiced. Through the web, users can collaborate with each other using VRMMS. Their avatars and tools of molecular simulations can be remotely utilized in order to provide with senses of 'being there' to the remote users. The users can discuss, teach and learn over the Internet. The shared workspaces for discussion and education are designed and implemented in Second Life. Since the activities in Second Life and VRMMS are designed to realistic, the system is expected to help users in improving their learning and experimental performances.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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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.

• Journal of Microbiology and Biotechnology
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• v.18 no.1
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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.

• Proceedings of the Membrane Society of Korea Conference
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• 2003.07a
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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.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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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.

• Bulletin of the Korean Chemical Society
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• v.21 no.4
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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.

• BMB Reports
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• v.48 no.5
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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]

• Proceedings of the Korean Fiber Society Conference
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• 1999.10a
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• pp.307-310
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• 1999

• Bulletin of the Korean Chemical Society
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• v.23 no.7
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• pp.929-930
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• 2002

• Proceedings of the Korean Magnetic Resonance Society Conference
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• 1999.02a
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• pp.7-7
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• 1999