• Genomics & Informatics
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• v.15 no.4
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• pp.162-169
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• 2017

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.937-943
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• 2014

Polymorphisms in miRNA binding sites have been shown to affect miRNA binding to target genes, resulting in differential mRNA and protein expression and susceptibility to common diseases. Our purpose was to predict SNPs (single nucleotide polymorphisms) within miRNA binding sites of inflammatory genes in relation to gastric cancer. A complete list of SNPs in the 3'UTR regions of all inflammatory genes associated with gastric cancer was obtained from Pubmed. miRNA target prediction databases (MirSNP, Targetscan Human 6.2, PolymiRTS 3.0, miRNASNP 2.0, and Patrocles) were used to predict miRNA target sites. There were 99 SNPs with MAF>0.05 within the miRNA binding sites of 41 genes among 72 inflammation-related genes associated with gastric cancer. NF-${\kappa}B$ and JAK-STAT are the two most important signaling pathways. 47 SNPs of 25 genes with 95 miRNAs were predicted. CCL2 and IL1F5 were found to be the shared target genes of hsa-miRNA-624-3p. Bioinformatic methods could identify a set of SNPs within miRNA binding sites of inflammatory genes, and provide data and direction for subsequent functional verification research.

• Molecules and Cells
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• v.46 no.1
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• pp.21-32
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• 2023

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

• Genomics & Informatics
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• v.3 no.3
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• pp.53-62
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• 2005

MicroRNAs play an important role in regulating gene expression, but their target identification is a difficult task due to their short length and imperfect complementarity. Burge and coworkers developed a program called TargetScan that allowed imperfect complementarity and established a procedure favoring targets with multiple binding sites conserved in multiple organisms. We improved their algorithm in two major aspects - (i) using well-defined UTR (untranslated region) database, (ii) examining the extent of conservation inside the 3' UTR specifically. Average length in our UTR database, based on the ECgene annotation, is more than twice longer than the Ensembl. Then, TargetScan was used to identify putative binding sites. The extent of conservation varies significantly inside the 3' UTR. We used the 'tight' tracks in the UCSC genome browser to select the conserved binding sites in multiple species. By combining the longer 3' UTR data, TargetScan, and tightly conserved blocks of genomic DNA, we identified 107 putative target genes with multiple binding sites conserved in multiple species, of which 85 putative targets are novel.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3601-3606
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• 2014

Single nucleotide polymorphisms located at microRNA (miRNA)-binding sites are likely to affect the expression of miRNA targets and may contribute to the susceptibility of humans to common diseases. Here 335 candidate lung cancer-related inflammatory genes were selected according to the existing literature and database. We identified putative miRNA-binding sites of 149 genes by specialised algorithms and screened SNPs in the 3'UTRs of these genes. By calculating binding free energy, we sorted 269 SNPs on the basis of the possibility of prediction. The proposed approach could help to easy the identification of functionally relevant SNPs and minimize the workflow and the costs.

• Journal of Marine Bioscience and Biotechnology
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• v.7 no.2
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• pp.35-41
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• 2015

Whales are marine mammals that are fully adapted to aquatic environment. Whales breathe by lungs so they require adaptive system to low oxygen concentration (hypoxia) while deep and prolonged diving. However, the study for the molecular mechanism underlying cetacean adaptation to hypoxia has been limited. Hypoxia-inducible factor (HIF) is the central transcription factor that regulates hypoxia-related gene expression. Here we identified HIF-binding sites in whale genome by phylogenetic footprinting and analyzed HIF-target genes to understand how whales cope with hypoxia. By comparison with the HIF-target genes of terrestrial mammals, it was suggested that whales may retain unique adaptation mechanisms to hypoxia.

• Interdisciplinary Bio Central
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• v.2 no.3
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• pp.8.1-8.5
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• 2010

Introduction: Recently identified human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Bioinformatics study of hABH2 and hABH3 had led to the discovery of a novel DNA repair mechanism. Very little is known about structure and function of hABH4, one of the members of this superfamily. Therefore, in present study we are intended to predict its structure and function through various bioinformatics tools. Materials and Methods: Modeling was done with modeler 9v7 to predict the 3D structure of the hABH4 protein. This model was validated with the program Procheck using Ramachandran plot statistics and was submitted to PMDB with ID PM0076284. The 3d2GO server was used to predict the functions. Residues at protein ligand and protein RNA binding sites were predicted with 3dLigandSite and KYG programs respectively. Results and Discussion: 3-D model of hABH4, ALKBH4.B99990003.pdb was predicted and evaluated. Validation result showed that 96.4 % residues lies in favored and additional allowed region of Ramachandran plot. Ligand binding residues prediction showed four Ligand clusters, having 24 ligands in cluster 1. Importantly, conserved pattern of Glu196-X-Pro198- Xn-His254 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. Conclusion and Prospects: The predicted and validated model of human homologue hABH4 resulted from this study may unveil the mechanism of DNA damage repair in human and accelerate the research on designing of appropriate inhibitors aiding in chemotherapy and cancer related diseases.

• Bulletin of the Korean Chemical Society
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• v.34 no.2
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• pp.545-548
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• 2013

It has been shown that calculating atomic charges using quantum mechanical level theory greatly improves the accuracy of docking. A protocol was developed and shown to be effective. That this protocol works is just a manifestation of the fact that electrostatic interactions are important in protein-ligand binding. In order to investigate how the same protocol helps in prediction of binding affinities, we took a series of known cocrystal structures of influenza neuraminidase inhibitors with the receptor and performed docking with Glide SP, Glide XP, and QPLD, the last being a workflow that incorporates QM/MM calculations to replace the fixed atomic charges of force fields with quantum mechanically recalculated ones at a given docking pose, and predicted the binding affinities of each cocrystal. The correlation with experimental binding affinities considerably improved with QPLD compared to Glide SP/XP yielding $r^2$ = 0.83. The results suggest that for binding sites, such as that of neuraminidase, which are laden with hydrophilic residues, protocols such as QPLD which utilizes QM-based atomic charges can better predict the binding affinities.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.21-28
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• 2012

Predication of protein interaction sites for monomer structures can reduce the search space for protein docking and has been regarded as very significant for predicting unknown functions of proteins from their interacting proteins whose functions are known. In the other hand, the prediction of interaction sites has been limited in crystallizing weakly interacting complexes which are transient and do not form the complexes stable enough for obtaining experimental structures by crystallization or even NMR for the most important protein-protein interactions. This work reports the calculation of 3D surface patches of complex structures and their properties and a machine learning approach to build a predictive model for the 3D surface patches in interaction and non-interaction sites using support vector machine. To overcome classification problems for class imbalanced data, we employed an under-sampling technique. 9 properties of the patches were calculated from amino acid compositions and secondary structure elements. With 10 fold cross validation, the predictive model built from SVM achieved an accuracy of 92.7% for classification of 3D patches in interaction and non-interaction sites from 147 complexes.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.63-74
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• 2014

Predicting protein loop structures is an important modeling problem since protein loops are often involved in diverse biological functions by participating in enzyme active sites, ligand binding sites, etc. However, loop structure prediction is difficult even when structures of homologous proteins are known due to large sequence and structure variability among loops of homologous proteins. Therefore, an ab initio approach is necessary to solve loop modeling problems. One of the difficulties in the development of ab initio loop modeling method is to derive an accurate scoring function that closely approximates the true free energy function. In particular, entropy as well as energy contribution have to be considered adequately for loops because loops tend to be flexible compared to other parts of protein. In this study, the contribution of conformational entropy is considered in scoring loop conformations by employing "colony energy" which was previously proposed to estimate the free energy for an ensemble of conformations. Loop conformations were generated by using two EDISON_Chem programs GalaxyFill and GalaxySC, and colony energy was designed for this sampling by tuning relevant parameters. On a test set of 40 loops, the accuracy of predicted loop structure improved on average by scoring with the colony energy compared to scoring by energy alone. In addition, high correlation between colony energy and deviation from the native structure suggested that more extensive sampling can further improve the prediction accuracy. In another test on 6 ligand-binding loops that show conformational changes by ligand binding, both ligand-free and ligand-bound states could be identified by using colony energy when no information on the ligand-bound conformation is used.