Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): A web tool for generating protein sequence similarity networks.
Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): A web tool for generating protein sequence similarity networks.
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DOI:
10.1016/j.bbapap.2015.04.015
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发表时间:
2015-08
影响因子:
3.2
通讯作者:
Whalen, Katie L.
中科院分区:
文献类型:
--
作者:
Gerlt, John A.;Bouvier, Jason T.;Davidson, Daniel B.;Imker, Heidi J.;Sadkhin, Boris;Slater, David R.;Whalen, Katie L.
The Enzyme Function Initiative, an NIH/NIGMS-supported Large-Scale Collaborative Project (EFI; U54GM093342; http://enzymefunction.org/), is focused on devising and disseminating bioinformatics and computational tools as well as experimental strategies for the prediction and assignment of functions (in vitro activities and in vivo physiological/metabolic roles) to uncharacterized enzymes discovered in genome projects. Protein sequence similarity networks (SSNs) are visually powerful tools for analyzing sequence relationships in protein families (H.J. Atkinson, J.H. Morris, T.E. Ferrin, and P.C. Babbitt, PLoS One 2009, 4, e4345). However, the members of the biological/biomedical community have not had access to the capability to generate SSNs for their “favorite” protein families. In this article we announce the EFI-EST (Enzyme Function Initiative-Enzyme Similarity Tool) web tool (http://efi.igb.illinois.edu/efi-est/) that is available without cost for the automated generation of SSNs by the community. The tool can create SSNs for the “closest neighbors” of a user-supplied protein sequence from the UniProt database (Option A) or of members of any user-supplied Pfam and/or InterPro family (Option B). We provide an introduction to SSNs, a description of EFI-EST, and a demonstration of the use of EFI-EST to explore sequence-function space in the OMP decarboxylase superfamily (PF00215). This article is designed as a tutorial that will allow members of the community to use the EFI-EST web tool for exploring sequence/function space in protein families.
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影响因子:
14.9
作者:
Mitchell A;Chang HY;Daugherty L;Fraser M;Hunter S;Lopez R;McAnulla C;McMenamin C;Nuka G;Pesseat S;Sangrador-Vegas A;Scheremetjew M;Rato C;Yong SY;Bateman A;Punta M;Attwood TK;Sigrist CJ;Redaschi N;Rivoire C;Xenarios I;Kahn D;Guyot D;Bork P;Letunic I;Gough J;Oates M;Haft D;Huang H;Natale DA;Wu CH;Orengo C;Sillitoe I;Mi H;Thomas PD;Finn RD
通讯作者:
Finn RD
影响因子:
14.9
作者:
UniProt Consortium
通讯作者:
UniProt Consortium
影响因子:
15
作者:
Fan, Hao;Hitchcock, Daniel S.;Seidel, Ronald D., II;Hillerich, Brandan;Lin, Henry;Almo, Steven C.;Sali, Andrej;Shoichet, Brian K.;Raushel, Frank M.
通讯作者:
Raushel, Frank M.
影响因子:
9.8
作者:
Mashiyama, Susan T.;Malabanan, M. Merced;Babbitt, Patricia C.
通讯作者:
Babbitt, Patricia C.
DOI:
10.1093/bioinformatics/bts532
发表时间:
2012-11-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Barber AE 2nd;Babbitt PC
通讯作者:
Babbitt PC