• Molecules and Cells
• /
• 제28권3호
• /
• pp.195-200
• /
• 2009

Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation, proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes. PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases (NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel statistical model for the classification of PTKs that is based on their structural features. The approach allows for both the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs.

• Journal of Computational Design and Engineering
• /
• 제1권2호
• /
• pp.79-87
• /
• 2014

Voronoi diagrams are powerful for solving spatial problems among particles and have been used in many disciplines of science and engineering. In particular, the Voronoi diagram of three-dimensional spheres, also called the additively-weighted Voronoi diagram, has proven its powerful capabilities for solving the spatial reasoning problems for the arrangement of atoms in both molecular biology and material sciences. In order to solve application problems, the dual structure, called the quasi-triangulation, and its derivative structure, called the beta-complex, are frequently used with the Voronoi diagram itself. However, the Voronoi diagram, the quasi-triangulation, and the beta-complexes are sometimes regarded as somewhat difficult for ordinary users to understand. This paper presents the two-dimensional counterparts of their definitions and introduce the BetaConcept program which implements the theory so that users can easily learn the powerful concept and capabilities of these constructs in a plane. The BetaConcept program was implemented in the standard C++ language with MFC and OpenGL and freely available at Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr).

• 전기전자학회논문지
• /
• 제13권3호
• /
• pp.18-23
• /
• 2009

FEATURE는 단백질 내에서 특정 기능이나 구조를 가지고 있는 site의 미세환경분포를 이용하여 다른 단백질 내에서 이와 유사한 미세환경을 가지고 있는 부분을 찾아 그 분분이 site일 확률을 수치적으로 제시해 줌으로써 사용자로 하여금 site의 존재 유무와 그 위치를 판단하는데 기준을 제공해주는 유용한 툴이다. 하지만 기존의 FEATURE에서 사용된 데이터 이외의 새로운 단백질 구조 데이터를 FEATURE에 적용하기 위해서는 FEATURE 내부의 module을 입력 데이터 구조에 맞게 수정해야 한다. 그러나 FEATURE 내부의 module 구조를 수정하는 방식이 직관적이지 않기 때문에 많은 연구자들이 FEATURE를 원활하게 사용하지 못하였다. 따라서 본 논문에서는 FEATURE의 내부 구조를 분석하고 FEATURE를 새로운 단백질 데이터에 적용하기 위한 방법을 제시한다.

• Journal of applied mathematics & informatics
• /
• 제29권3_4호
• /
• pp.529-546
• /
• 2011

A deterministic tuberculosis model for theoretically assessing the potential impact of the combined effects of case detection in the presence of treatment is formulated. The qualitative features of its equilibria are analyzed and it is found that the disease-free equilibrium may not be globally asymptotically stable when the reproduction number is less than unity. This disease threshold number is further used to assess the impact of active TB case finding alone and in conjunction with treatment. A critical threshold parameter ${\Theta}$ say for which case detection will have a positive impact is derived. Using the Centre Manifold theory, the model may exhibit the phenomenon of backward bifurcation (coexistence of a locally stable endemic equilibrium with a stable disease-free equilibrium) when the reproduction number is less than unity. It is shown that the possibility of backward bifurcation occurring decreases with increase case detection. Graphical representations suggest that increase in case finding accompanied by treatment of detected TB cases, result in a marked decrease of TB cases (both latent and active TB).

• ETRI Journal
• /
• 제43권3호
• /
• pp.483-510
• /
• 2021

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in O (N) time, whereas quantum algorithm design is based on Grover's method, which completes the search in $O(\sqrt{N})$ time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are $O(\sqrt{t})$ and $O(\sqrt{N})$, and the exceptional worst case is bounded by O (t) and O (N). Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.

• Genomics & Informatics
• /
• 제18권1호
• /
• pp.8.1-8.10
• /
• 2020

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/.

• Journal of Microbiology and Biotechnology
• /
• 제18권7호
• /
• pp.1245-1251
• /
• 2008

Oxalate decarboxylases (OXDCs) (E.C. 4.1.1.2) are enzymes catalyzing the conversion of oxalate to formate and $CO_2$. The OXDCs found in fungi and bacteria belong to a functionally diverse protein superfamily known as the cupins. Fungi-originated OXDCs are secretory enzymes. However, most bacterial OXDCs are localized in the cytosol, and may be involved in energy metabolism. In Agrobacterium tumefaciens C58, a locus for a putative oxalate decarboxylase is present. In the study reported here, an enzyme was overexpressed in Escherichia coli and showed oxalate decarboxylase activity. Computational analysis revealed the A. tumefaciens C58 OXDC contains a signal peptide mediating translocation of the enzyme into the periplasm that was supported by expression of signal-peptideless and full-length versions of the enzyme in A. tumefaciens C58. Further site-directed mutagenesis experiment demonstrated that the A. tumefaciens C58 OXDC is most likely translocated by a twin-arginine translocation (TAT) system.

• Molecules and Cells
• /
• 제42권8호
• /
• pp.589-596
• /
• 2019

${\beta}Pix$ is a guanine nucleotide exchange factor for the Rho family small GTPases, Rac1 and Cdc42. It is known to regulate focal adhesion dynamics and cell migration. However, the in vivo role of ${\beta}Pix$ is currently not well understood. Here, we report the production and characterization of ${\beta}Pix$-KO mice. Loss of ${\beta}Pix$ results in embryonic lethality accompanied by abnormal developmental features, such as incomplete neural tube closure, impaired axial rotation, and failure of allantois-chorion fusion. We also generated ${\beta}Pix$-KO mouse embryonic fibroblasts (MEFs) to examine ${\beta}Pix$ function in mouse fibroblasts. ${\beta}Pix$-KO MEFs exhibit decreased Rac1 activity, and defects in cell spreading and platelet-derived growth factor (PDGF)-induced ruffle formation and chemotaxis. The average size of focal adhesions is increased in ${\beta}Pix$-KO MEFs. Interestingly, ${\beta}Pix$-KO MEFs showed increased motility in random migration and rapid wound healing with elevated levels of MLC2 phosphorylation. Taken together, our data demonstrate that ${\beta}Pix$ plays essential roles in early embryonic development, cell spreading, and cell migration in fibroblasts.

• 한국미생물·생명공학회지
• /
• 제51권1호
• /
• pp.109-123
• /
• 2023

Preterm birth (PTB) is defined as giving birth prior to the 37th week of pregnancy and is a major cause of infant mortality. Studies have indicated that the vaginal microbiota's composition and its dysbiosis, particularly during pregnancy, may play a major role in PTB. While previous research work concentrated on well-studied microorganisms such as Lactobacillus, Prevotella, Gardnerella, various other microbes, and their significance in the vaginal microbiota's stability remain unknown. Moreover, current studies have focused primarily on the relative abundances of the microbes found, without considering their interactions with other members of the vaginal microbiota. In this work, we developed a novel computational approach and performed taxonomic classification of vaginal microbiota samples stratified longitudinally (Term/PTB) to observe compositional disparities and find underexamined microbes that may be contributing to PTB. Furthermore, we carried out a correlational analysis to build a microbial co-interaction network and investigated the functional implications of the genes present in both Term and PTB samples. The co-occurrence network revealed that Lactobacillus acts in solidarity to maintain the stability of the vaginal microbiota and did not have strong co-interactions with any of the other microbes. Similarly, microbes with strong interactions with Atopobium, a well-known marker microbe of PTB, were also observed. Additionally, several genes such as PTXA, FANCM, GPX, and DUSP were found to be playing an important role in the occurrence of PTB. This study provides a novel conceptual framework revealing distinct vaginal microbiota signatures that could be potential therapeutic targets for the prevention of PTB.

• 통합자연과학논문집
• /
• 제16권4호
• /
• pp.123-131
• /
• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.