• 통합자연과학논문집
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• 제16권4호
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• pp.123-131
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• 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.

• Interdisciplinary Bio Central
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• 제3권4호
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• pp.15.1-15.11
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• 2011

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.

• Molecules and Cells
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• 제24권3호
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• pp.307-315
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• 2007

Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.

• 한국융합학회논문지
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• 제7권4호
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• pp.181-190
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• 2016

The objective of this study is to find the effective stiffness and compressive strengths of a unit-cell pinwheel truss and double pinwheel truss model designed following a double helical geometry similar to that of the DNA (deoxyribonucleic acid) structure in biology. The ideal solution for their derived relative density is correlated with a ratio of the truss thickness and length. To validate the relative stiffness or relative strength, ABAQUS software is used for the computational model analysis on five models having a different size of truss diameter from 1mm to 5mm. Applied material properties are stainless steel type 304. The boundary conditions applied were fixed bottom and 5 mm downward displacement. It was assumed that the width, length, and height are all equal. Consequently, it is found that the truss model has a lower effective stiffness and a lower effective yielding strength.

• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.355-364
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• 2008

Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters.

• Journal of applied mathematics & informatics
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• 제31권3_4호
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• pp.457-467
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• 2013

There exist many deterministic models for signaling pathways in systems biology. However the models do not consider the stochastic properties of the pathways, which means the models fit well with experimental data in certain situations but poorly in others. Incorporating stochasticity into deterministic models is one way to handle this problem. In this paper the way is used to produce stochastic models based on the deterministic differential equations for the published extracellular signal-regulated kinase (ERK) pathway. We consider strong convergence and stability of the numerical approximations for the stochastic models.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.179-182
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• 2003

The Development of promoter recognition systems is a interesting problem in computational biology. In this paper, we introduce a intelligent system fur promoter recognition with multiple decision models using artificial neural networks. We have trained this models with 1871 human promoter sequences and 5230exon and intron sequences. Our system is found to perform better than other promoter finding systems insensitivity and specificity measures. We have tested our system with Chromosome 22 dataset.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.193-199
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• 2003

The application of finding occurrences of a pattern that contains gaps includes information retrieval, data mining, and computational biology. As the biological sequences may contain errors, it is important to find not only the exact occurrences of a pattern but also approximate ones. In this paper we present an O(mnk$_{max}$/w) time algorithm for the approximate gapped pattern matching problem, where m is the length of the text, H is the length of the pattern, w is the word size of the target machine, and k$_{max}$ is the greatest error bound for subpatterns.

• BMB Reports
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• 제47권9호
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• pp.518-523
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• 2014

The maternal transcription factor Dorsal (Dl) functions as both an activator and a repressor in a context-dependent manner to control dorsal-ventral patterning in the Drosophila embryo. Previous studies have suggested that Dl is an intrinsic activator and its repressive activity requires additional corepressors that bind corepressor-binding sites near Dl-binding sites. However, the molecular identities of the corepressors have yet to be identified. Here, we present evidence that Capicua (Cic) is involved in Dl-mediated repression in the zerkn$\ddot{u}$llt (zen) ventral repression element (VRE). Computational and genetic analyses indicate that a DNA-binding consensus sequence of Cic is highly analogous with previously identified corepressor-binding sequences and that Dl failed to repress zen expression in lateral regions of cic mutant embryos. Furthermore, electrophoretic mobility shift assay (EMSA) shows that Cic directly interacts with several corepressor-binding sites in the zen VRE. These results suggest that Cic may function as a corepressor by binding the VRE.

• Genomics & Informatics
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• 제11권1호
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• pp.24-33
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• 2013

We had analyzed 10 exome sequencing data and single nucleotide polymorphism chips for blood cancer provided by the PGM21 (The National Project for Personalized Genomic Medicine) Award program. We had removed sample G06 because the pair is not correct and G10 because of possible contamination. In-house software somatic copy-number and heterozygosity alteration estimation (SCHALE) was used to detect one loss of heterozygosity region in G05. We had discovered 27 functionally important mutations. Network and pathway analyses gave us clues that NPM1, GATA2, and CEBPA were major driver genes. By comparing with previous somatic mutation profiles, we had concluded that the provided data originated from acute myeloid leukemia. Protein structure modeling showed that somatic mutations in IDH2, RASGEF1B, and MSH4 can affect protein structures.