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RISS 인기검색어
- 검색키워드
- 저자 : Bolser, Dan (검색결과 6 건)
- 무료
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- 유료
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Biological Network Evolution Hypothesis Applied to Protein Structural Interactome
Bolser, Dan M.,Park, Jong Hwa Korea Genome Organization 2003 Genomics & informatics Vol.1 No.1
The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.
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Bhak, Jonghwa,Bolser, Dan,Park, Daeui,Cho, Yoobok,Yoo, Kiesuk,Lee, Semin,Gong, SungSam,Jang, Insoo,Park, Changbum,Huston, Maryana,Choi, Hwanho Korea Genome Organization 2004 Genomics & informatics Vol.2 No.4
The main mechanism of evolution is that biological entities change, are selected, and reproduce. We propose a different concept in terms of the main agent or atom of evolution: in the biological world, not an individual object, but its interactive network is the fundamental unit of evolution. The interaction network is composed of interaction pairs of information objects that have order information. This indicates a paradigm shift from 3D biological objects to an abstract network of information entities as the primary agent of evolution. It forces us to change our views about how organisms evolve and therefore the methods we use to analyze evolution.
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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>
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PSIbase: a database of Protein Structural Interactome map (PSIMAP)
Gong, Sungsam,Yoon, Giseok,Jang, Insoo,Bolser, Dan,Dafas, Panos,Schroeder, Michael,Choi, Hansol,Cho, Yoobok,Han, Kyungsook,Lee, Sunghoon,Choi, Hwanho,Lappe, Michael,Holm, Liisa,Kim, Sangsoo,Oh, Dongho Oxford University Press 2005 Bioinformatics Vol.21 No.10
<P><B>Summary:</B> Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain–domain and protein–protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences.</P><P><B>Availability:</B> http://psimap.org and http://psibase.kaist.ac.kr/</P><P><B>Contact:</B> biopark@kaist.ac.kr</P><P><B>Supplementary information:</B> Supplementary material is available at http://psibase.kaist.ac.kr/Doc/supplementary_material.htm</P>
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Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates
Jeon, Sungwon,Blazyte, Asta,Yoon, Changhan,Ryu, Hyojung,Jeon, Yeonsu,Bhak, Youngjune,Bolser, Dan,Manica, Andrea,Shin, Eun-Seok,Cho, Yun Sung,Kim, Byung Chul,Ryoo, Namhee,Choi, Hansol,Bhak, Jong Korean Society for Molecular and Cellular Biology 2021 Molecules and cells Vol.44 No.9
Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing.
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BioCC: An Openfree Hypertext Bio Community Cluster for Biology
Gong Sung-Sam,Kim Tae-Hyung,Oh Jung-Su,Kwon Je-Keun,Cho Su-An,Bolser Dan,Bhak Jong Korea Genome Organization 2006 Genomics & informatics Vol.4 No.3
We present an openfree hypertext (also known as wiki) web cluster called BioCC. BioCC is a novel wiki farm that lets researchers create hundreds of biological web sites. The web sites form an organic information network. The contents of all the sites on the BioCC wiki farm are modifiable by anonymous as well as registered users. This enables biologists with diverse backgrounds to form their own Internet bio-communities. Each community can have custom-made layouts for information, discussion, and knowledge exchange. BioCC aims to form an ever-expanding network of openfree biological knowledge databases used and maintained by biological experts, students, and general users. The philosophy behind BioCC is that the formation of biological knowledge is best achieved by open-minded individuals freely exchanging information. In the near future, the amount of genomic information will have flooded society. BioGG can be an effective and quickly updated knowledge database system. BioCC uses an opensource wiki system called Mediawiki. However, for easier editing, a modified version of Mediawiki, called Biowiki, has been applied. Unlike Mediawiki, Biowiki uses a WYSIWYG (What You See Is What You Get) text editor. BioCC is under a share-alike license called BioLicense (http://biolicense.org). The BioCC top level site is found at http://bio.cc/
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