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Comparison of Digital PCR and Quantitative PCR with Various SARS-CoV-2 Primer-Probe Sets
( Changwoo Park ),( Jina Lee ),( Zohaib Ul Hassan ),( Keun Bon Ku ),( Seong-jun Kim ),( Hong Gi Kim ),( Edmond Changkyun Park ),( Gun-soo Park ),( Daeui Park ),( Seung-hwa Baek ),( Dongju Park ),( Jih 한국미생물생명공학회(구 한국산업미생물학회) 2021 Journal of microbiology and biotechnology Vol.31 No.3
The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) as an international health emergency. Current diagnostic tests are based on the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) method, which is the gold standard test that involves the amplification of viral RNA. However, the RT-qPCR assay has limitations in terms of sensitivity and quantification. In this study, we tested both qPCR and droplet digital PCR (ddPCR) to detect low amounts of viral RNA. The cycle threshold (CT) of the viral RNA by RT-PCR significantly varied according to the sequences of the primer and probe sets with in vitro transcript (IVT) RNA or viral RNA as templates, whereas the copy number of the viral RNA by ddPCR was effectively quantified with IVT RNA, cultured viral RNA, and RNA from clinical samples. Furthermore, the clinical samples were assayed via both methods, and the sensitivity of the ddPCR was determined to be equal to or more than that of the RT-qPCR. However, the ddPCR assay is more suitable for determining the copy number of reference materials. These findings suggest that the qPCR assay with the ddPCR defined reference materials could be used as a highly sensitive and compatible diagnostic method for viral RNA detection.
Park, Daeui,Lee, Semin,Bolser, Dan,Schroeder, Michael,Lappe, Michael,Oh, Donghoon,Bhak, Jong Oxford University Press 2005 Bioinformatics Vol.21 No.15
<P><B>Motivation:</B> Many genomes have been completely sequenced. However, detecting and analyzing their protein–protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein–protein interaction rules within and among complete genomes.</P><P><B>Results:</B> We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed tohave played a key role in species diversification.</P><P><B>Contact:</B> jong@kribb.re.kr</P><P><B>Supplementary information:</B> http://interactomics.org</P>
Park, Tamina,Kang, Myung-gyun,Nah, Jinju,Ryoo, Soyoon,Wee, Sunghwan,Baek, Seung-hwa,Ku, Bokkyung,Oh, Yeonsu,Cho, Ho-seong,Park, Daeui The Korean Society of Veterinary Service 2019 韓國家畜衛生學會誌 Vol.42 No.2
Foot-and-Mouth Disease (FMD) is a highly contagious trans-boundary viral disease caused by FMD virus, which causes huge economic losses. FMDV infects cloven hoofed (two-toed) mammals such as cattle, sheep, goats, pigs and various wildlife species. To control the FMDV, it is necessary to understand the life cycle and the pathogenesis of FMDV in host. Especially, the protein-protein interaction between FMDV and host will help to understand the survival cycle of viruses in host cell and establish new therapeutic strategies. However, the computational approach for protein-protein interaction between FMDV and pig hosts have not been applied to studies of the onset mechanism of FMDV. In the present work, we have performed the prediction of the pig's proteins which interact with FMDV based on RNA-Seq data, protein sequence, and structure information. After identifying the virus-host interaction, we looked for meaningful pathways and anticipated changes in the host caused by infection with FMDV. A total of 78 proteins of pig were predicted as interacting with FMDV. The 156 interactions include 94 interactions predicted by sequence-based method and the 62 interactions predicted by structure-based method using domain information. The protein interaction network contained integrin as well as STYK1, VTCN1, IDO1, CDH3, SLA-DQB1, FER, and FGFR2 which were related to the up-regulation of inflammation and the down-regulation of cell adhesion and host defense systems such as macrophage and leukocytes. These results provide clues to the knowledge and mechanism of how FMDV affects the host cell.
MassNet: a functional annotation service for protein mass spectrometry data
Park, Daeui,Kim, Byoung-Chul,Cho, Seong-Woong,Park, Seong-Jin,Choi, Jong-Soon,Kim, Seung Il,Bhak, Jong,Lee, Sunghoon Oxford University Press 2008 Nucleic acids research Vol.36 No.suppl2
<P>Although mass spectrometry has been frequently used to identify proteins, there are no web servers that provide comprehensive functional annotation of those identified proteins. It is necessary to provide such web service due to a rapid increase in the data. We, therefore, introduce MassNet, which provides (i) physico-chemical analysis information, (ii) KEGG pathway assignment (iii) Gene Ontology mapping and (iv) protein–protein interaction (PPI) prediction for the data from MASCOT, Prospector and Profound. MassNet provides the prediction information for PPIs using both 3D structural interaction and experimental interaction deposited in PSIMAP, BIND, DIP, HPRD, IntAct, MINT, CYGD and BioGrid. The web service is freely available at http://massnet.kr or http://sequenceome.kobic.re.kr/MassNet/.</P>
Kim, Jeong-Gu,Park, Daeui,Kim, Byoung-Chul,Cho, Seong-Woong,Kim, Yeong Tae,Park, Young-Jin,Cho, Hee Jung,Park, Hyunseok,Kim, Ki-Bong,Yoon, Kyong-Oh,Park, Soo-Jun,Lee, Byoung-Moo,Bhak, Jong BioMed Central 2008 BMC bioinformatics Vol.9 No.-
<P><B>Background</B></P><P>Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of <I>Xanthomonas oryzae </I>pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways.</P><P><B>Description</B></P><P>A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome.</P><P><B>Conclusion</B></P><P>XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from .</P>
Baek, Seung Cheol,Park, Mi Hyeon,Ryu, Hyung Won,Lee, Jae Pil,Kang, Myung-Gyun,Park, Daeui,Park, Chul Min,Oh, Sei-Ryang,Kim, Hoon Elsevier 2019 Bioorganic chemistry Vol.83 No.-
<P><B>Abstract</B></P> <P>Three flavanones and two flavones were isolated from the leaves of <I>Prunus padus</I> var. <I>seoulensis</I> by the activity-guided screening for new monoamine oxidase (MAO) inhibitors. Among the compounds isolated, rhamnocitrin (<B>5</B>) was found to potently and selectively inhibit human MAO-A (hMAO-A, IC<SUB>50</SUB> = 0.051 µM) and effectively inhibit hMAO-B (IC<SUB>50</SUB> = 2.97 µM). The IC<SUB>50</SUB> value of <B>5</B> for hMAO-A was the lowest amongst all natural flavonoids reported to date, and the potency was 20.2 times higher than that of toloxatone (1.03 µM), a marketed drug. In addition, <B>5</B> reversibly and competitively inhibited hMAO-A and hMAO-B with K<SUB>i</SUB> values of 0.030 and 0.91 µM, respectively. Genkwanin (<B>4</B>) was also observed to strongly inhibit hMAO-A and hMAO-B (IC<SUB>50</SUB> = 0.14 and 0.35 µM, respectively), and competitively inhibit hMAO-A and hMAO-B (K<SUB>i</SUB> = 0.097 and 0.12 µM, respectively). Molecular docking simulation reveals that the binding affinity of <B>5</B> with hMAO-A (−18.49 kcal/mol) is higher than that observed with hMAO-B (0.19 kcal/mol). Compound <B>5</B> interacts with hMAO-A at four possible residues (Asn181, Gln215, Thr336, and Tyr444), while hMAO-B forms a single hydrogen bond at Glu84. These findings suggest that compound <B>5</B> as well as <B>4</B> can be considered as novel potent and reversible hMAO-A and/or hMAO-B inhibitors or useful lead compounds for future development of hMAO inhibitors in neurological disorder therapies.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Three flavanones and two flavones were isolated from the leaves of <I>Prunus padus</I> var. <I>seoulensis</I>. </LI> <LI> Rhamnocitrin was a potent hMAO-A inhibitor (IC<SUB>50</SUB> = 0.051 µM), the lowest natural flavonoid to date. </LI> <LI> The binding affinity of <B>5</B> for hMAO-A was greater than that for hMAO-B. </LI> <LI> Genkwanin (<B>4</B>) strongly inhibited hMAO-A and hMAO-B (IC<SUB>50</SUB> = 0.14 and 0.35 µM, respectively). </LI> <LI> Compounds <B>5</B> and <B>4</B> can be considered as new potent and reversible hMAO-A and/or hMAO-B inhibitors. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Ha, Young Mi,Uehara, Yohei,Park, Daeui,Jeong, Hyoung Oh,Park, Ji Young,Park, Yun Jung,Lee, Ji Yeon,Lee, Hye Jin,Song, Yu Min,Moon, Hyung Ryong,Chung, Hae Young Humana Press 2012 Applied biochemistry and biotechnology Vol.168 No.6
<P>We describe the design, synthesis, and biological activities of 5-chloro-2-(substituted phenyl)benzo[d]thiazole derivatives as novel tyrosinase inhibitors. Among them, 4-(5-chloro-2,3-dihydrobenzo[d]thiazol-2-yl)-2,6-dimethoxyphenol (MHY884) and 2-bromo-4-(5-chloro-benzo[d]thiazol-2-yl)phenol (MHY966) showed inhibitory activity higher than or similar to kojic acid, against mushroom tyrosinase. Therefore, we carried out kinetic studies on the two compounds with potent tyrosinase inhibitory effects. Kinetic analysis of tyrosinase inhibition revealed that all of these compounds are competitive inhibitors. MHY884 and MHY966 effectively inhibited tyrosinase activity and reduced melanin levels in B16 cells treated with α-melanocyte stimulating hormone (α-MSH). These data strongly suggest that the newly synthesized compounds MHY884 and MHY966 could suppress production of melanin via inhibition of tyrosinase activity.</P>
Ha, Young Mi,Kim, Jin-Ah,Park, Yun Jung,Park, Daeui,Choi, Yeon Ja,Kim, Ji Min,Chung, Ki Wung,Han, Yu Kyeong,Park, Ji Young,Lee, Ji Yeon,Moon, Hyung Ryong,Chung, Hae Young Royal Society of Chemistry 2011 MedChemComm Vol.2 No.6
<P>In this study, we describe the synthesis and tyrosinase inhibitory activity of a new family of hydroxybenzylidenyl pyrrolidine-2,5-dione compounds. Among them, compound 3f (HMP) exhibited the highest inhibition, 83.87%, at a concentration of 20 μM, on the <SMALL>L</SMALL>-DOPA oxidase activity of mushroom tyrosinase. We also predicted the tertiary structure of tyrosinase, simulated its docking with HMP and confirmed that HMP strongly interacts with tyrosinase residues. This result suggested that the binding activity of HMP with tyrosinase could be high. Based on these results, we determined the IC<SUB>50</SUB> value for HMP inhibition of mushroom tyrosinase activity. HMP inhibited mushroom tyrosinase with an IC<SUB>50</SUB> value of 2.23 ± 0.44 μM, which is more potent than the anti-tyrosinase activity of kojic acid (IC<SUB>50</SUB> = 20.99 ± 1.80 μM), a well-known tyrosinase inhibitor. Kinetic analysis of tyrosinase inhibition revealed that HMP is a competitive inhibitor (<I>K</I><SUB>i</SUB> = 4.24 × 10<SUP>−7</SUP> M at 1.25 μM and <I>K</I><SUB>i</SUB> = 1.82 × 10<SUP>−6</SUP> M at 20 μM). HMP also inhibited melanin production and tyrosinase activity in B16F10 melanoma cells (B16 cells). These data strongly suggest that HMP can suppress the production of melanin <I>via</I> the modulation of tyrosinase activity.</P> <P>Graphic Abstract</P><P>Computational structure prediction for mushroom tyrosinase and docking simulation with compound HMP. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c0md00234h'> </P>