• Journal of Industrial and Engineering Chemistry
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• v.67
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• pp.293-300
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• 2018

Lipocalins are diverse group of small extracellular proteins found in various organisms. In this study, members of 10 non-homologous lipocalin-ligand crystal complex structures were remodeled using rigid and flexible ligand modes to validate the prediction efficiency of molecular docking simulation. The modeled ligand conformations indicated a high prediction accuracy in rigid ligand mode using cluster based analysis for most cases whereas the flexible ligand mode required further considerations such as ligand binding energy and RMSD for some cases. This in silico study is expected to serve as a platform in the screening of novel ligands against lipocalin family of proteins.

• Journal of Integrative Natural Science
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• v.3 no.3
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• pp.143-147
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• 2010

Since it is of great importance to know how a ligand binds to a receptor, there have been a lot of efforts to improve the quality of prediction of docking poses. Earlier efforts were focused on improving search algorithm and scoring function in a docking program resulting in a partial improvement with a lot of variations. Although these are basically very important and essential, more tangible improvements came from the reduction of search space. In a normal docking study, the approximate active site is assumed to be known. After defining active site, scoring functions and search algorithms are used to locate the expected binding pose within this search space. A good search algorithm will sample wisely toward the correct binding pose. By careful study of receptor structure, it was possible to prioritize sub-space in the active site using "receptor-based pharmacophores" or "hot spots". In a sense, these techniques reduce the search space from the beginning. Further improvements were made when the bound ligand structure is available, i.e., the searching could be directed by molecular similarity using ligand information. This could be very helpful to increase the accuracy of binding pose. In addition, if the biological activity data is available, docking program could be improved to the level of being useful in affinity prediction for a series of congeneric ligands. Since the number of co-crystal structures is increasing in protein databank, "Ligand-Guided Docking" to reduce the search space would be more important to improve the accuracy of docking pose prediction and the efficiency of virtual screening. Further improvements in this area would be useful to produce more reliable docking programs.

• Journal of Integrative Natural Science
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• v.11 no.1
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• pp.25-29
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• 2018

Protein phosphatase manganese dependent 1D (PPM1D), a Ser/Thr protein phosphatise, play major role in the cancer tumorigenesis of various tumors including neuroblastoma, pancreatic adenocarcinoma, medulloblastoma, breast cancer, prostate cancer and ovarian cancer. Hence, analysis on the structural features required for the formation of PPM1D-inhibitor complex becomes essential. In this study, we have performed molecular docking of SL-175 and -176 and protein-protein docking of CDC5L with PPM1D. On analysing the docked complexes, we have identified the important residues involved in the formation of protein-ligand complex. Research concentrating on these residues could be helpful in understanding the pathophysiology of various tumors related to PPM1D.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.126-130
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• 2016

Epidermal growth factor receptor(EGFR)는 HER family에 속하는 tyrosine kinase receptor로서 다양한 하류경로로 신호를 전달하여 세포 증식, 혈관 형성, 세포 사멸을 억제하는 역할을 한다. EGFR이 폐암의 형성에 중요한 역할을 하고 많은 상피세포 종양에서 비정상적으로 활성화됨에 따라 암 치료에 중요한 역할을 하고 있어 EGFR tyrosine kinase inhibitor(TKI)에 관한 많은 연구가 이루어졌다. 위와 같은 약 개발에 있어서 현재 가상 시뮬레이션을 통한 약 후보물질 개발이 진행되고 있다. 특히, Molecular docking 시뮬레이션은 기존의 실험적인 기술(X-ray crystallography, NMR)로는 연구하기가 어려웠던 protein과 ligand간의 상호작용을 예측하여 이에 대한 정보를 제공할 수 있다. 하지만, 우선적으로 Molecular docking 시뮬레이션은 정확한 validation을 기반으로 진행되어야 신뢰할 수 있는 정보를 얻을 수 있다. 따라서 이번 연구에서는 EDISON에서 제공하는 Dock 프로그램과 일반적으로 잘 알려진 Glide, Autodock 프로그램으로 protein data bank(PDB)에서 제공하는 EGFR wild type cocrystal을 redocking하는 방식을 통하여 최상위 rank pose의 RMSD 값을 통한 validation 성능을 비교함으로써 어떤 프로그램이 EGFR과 ligand 간의 결합예측을 하는데 있어서 보다 더 정확한 결과를 낼 수 있는지 알아보고자 하였고 시뮬레이션 결과 Autodock에서 가장 우수한 결과 값을 보여주었다.

• Journal of Ginseng Research
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• v.41 no.4
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• pp.534-539
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• 2017

Background: Ginsenosides are the main ingredients of ginseng, which, in traditional Eastern medicine, has been claimed to have therapeutic values for many diseases. In order to verify the effects of ginseng that have been empirically observed, we utilized the reverse docking method to screen for target proteins that are linked to specific diseases. Methods: We constructed a target protein database including 1,078 proteins associated with various kinds of diseases, based on the Potential Drug Target Database, with an added list of kinase proteins. We screened 26 kinds of ginsenosides of this target protein database using docking. Results: We found four potential target proteins for ginsenosides, based on docking scores. Implications of these "hit" targets are discussed. From this screening, we also found four targets linked to possible side effects and toxicities, based on docking scores. Conclusion: Our method and results can be helpful for finding new targets and developing new drugs from natural products.

• Journal of Integrative Natural Science
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• v.3 no.1
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• pp.49-53
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• 2010

Molecular docking is a critical event which mostly forms Van der waals complex in molecular recognition. Since the majority of developed drugs are small molecules, docking them into proteins has been a prime concern in drug discovery community. Since the binding pose space is too vast to cover completely, many search algorithms such as genetic algorithm, Monte Carlo, simulated annealing, distance geometry have been developed. Proper evaluation of the quality of binding is an essential problem. Scoring functions derived from force fields handle the ligand binding prediction with the use of potential energies and sometimes in combination with solvation and entropy contributions. Knowledge-based scoring functions are based on atom pair potentials derived from structural databases. Forces and potentials are collected from known protein-ligand complexes to get a score for their binding affinities (e.g. PME). Empirical scoring functions are derived from training sets of protein-ligand complexes with determined affinity data. Because non of any single scoring function performs generally better than others, some other approaches have been tried. Although numerous scoring functions have been developed to locate the correct binding poses, it still remains a major hurdle to derive an accurate scoring function for general targets. Recently, consensus scoring functions and target specific scoring functions have been studied to overcome the current limitations.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.103-108
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• 2006

Computational prediction of protein-ligand binding has been widely used as a tool to discover lead compounds fur new drugs. Prediction accuracy is determined in part by the scoring function used in docking calculations. Diverse scoring functions are available, and these can be classified into force-field based, empirical, and knowledge-based functions depending upon the basic assumptions made in development. Among these, force-field based functions consider physical interactions the most in detail. However, the force-field based functions have the drawback of not including the entropic effect while considering only the energy contribution such as dispersion or electrostatic forces. In this article, a method to take into account of the entropic effect using the colony energy is suggested when force-field based scoring functions is used by extracting conformational information obtained from the pre-existing docking program. An improved result for decoy discrimination is illustrated when the method is applied to the DOCK scoring function, and this implies that more accurate docking calculation is possible.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2433-2442
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• 2011

The three dimensional quantitative structure activity relationship (3D QSAR) models were developed using Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and docking studies. The fit of Quinazolinone antifolates inside the active site of modeled bovine dihydrofolate reductase (DHFR) was assessed. Both ligand based (LB) and receptor based (RB) QSAR models were generated, these models showed good internal and external statistical reliability that is evident from the $q^2_{loo}$, $r^2_{ncv}$ and $r^2_{pred}$. The identified key features enabled us to design new Quinazolinone analogues as DHFR inhibitors. This study is a building bridge between docking studies of homology modeled bovine DHFR protein as well as ligand and target based 3D QSAR techniques of CoMFA and CoMSIA approaches.

• Proceeding of EDISON Challenge
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• 2013.04a
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• pp.89-100
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• 2013

생물정보학의 다양한 이론적 내용과 계산적 방법들이 갈수록 전문화 되어짐에 따라 신약 개발, 신 물질 합성, 단백질의 구조 예측 등 다양한 분야에서 필요성이 커져가고 있다. 이 중 molecular docking 기술은 단백질과 특정 분자간의 결합 형태를 분자 모델링 기법을 통해 알아내는 방법이며 신약개발 연구에 큰 영향을 미치고 있다. Molecular docking을 통하여 분자간의 결합 형태를 예측하는 과정에서 Protein-ligand complex의 정확한 에너지 측정을 가능하게 하는 scoring function이 필요하다. 그런데 본 연구에서 사용한 B-Raf kinase protein 은 active site 부분에서 ligand와 receptor 간에 aromatic ring로 인한 ${\pi}-{\pi}$ interaction이 정확한 에너지 계산을 어렵게 한다. 이러한 ${\pi}-{\pi}$ interaction 부분의 에너지를 정확하게 계산하기 위해 양자역학 계산을 실시하였다. Active site 부분에서 ligand와 receptor에서 발생하는 각각 다른 5개의 ${\pi}-{\pi}$ interaction 구조를 준비하여 Gaussian을 통해 양자역학 에너지를 계산하였다. 그리고 이러한 결과 값들이 ligand의 활성 값과 어떤 상관관계를 갖는지 살펴보았다. 그 결과 ${\pi}-{\pi}$ interaction을 양자역학으로 계산한 값이 그렇지 않은 것보다 더 좋은 상관관계를 보여주었다. 이는 특별한 구조의 영향으로 ligand와 receptor 간의 결합에너지를 정확하게 계산하기 어려운 문제에서 양자역학을 적용할 경우 더욱 좋은 결과값을 얻을 수 있었다. 또한 이러한 데이터가 신 물질 개발이나 신약 개발 등의 다양한 분야에서 계산화학 방법이 신뢰성을 얻는데 도움 될 수 있다고 생각된다.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.221-233
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• 2004

Molecular docking falls into the general category of global optimization problems since its main purpose is to find the most stable complex consisting of a receptor and its ligand. Conformational space annealing (CSA), a powerful global optimization method, is incorporated with the Tinker molecular modeling package to perform molecular docking simulations of six receptor-ligand complexes (3PTB, 1ULB, 2CPP, 1STP, 3CPA and 1PPH) from the Protein Data Bank. In parallel, Monte Carlo with minimization (MCM) method is also incorporated into the Tinker package for comparison. The energy function, consisting of electrostatic interactions, van der Waals interactions and torsional energy terms, is calculated using the AMBER94 all-atom empirical force field. Rigid docking simulations for all six complexes and flexible docking simulations for three complexes (1STP, 3CPA and 1PPH) are carried out using the CSA and the MCM methods. The simulation results show that the docking procedures using the CSA method generally find the most stable complexes as well as the native -like complexes more efficiently and accurately than those using the MCM, demonstrating that CSA is a promising search method for molecular docking problems.