The Structure-Function Linkage Database

被引：181

作者：

Akiva, Eyal ^{[1
]}

Brown, Shoshana

Almonacid, Daniel E. ^{[2
]}

Barber, Alan E., II ^{[3
]}

Custer, Ashley F. ^{[4
]}

Hicks, Michael A. ^{[5
]}

Huang, Conrad C. ^{[6
]}

Lauck, Florian ^{[7
]}

Mashiyama, Susan T. ^{[1
]}

Meng, Elaine C. ^{[6
]}

Mischel, David ^{[1
]}

Morris, John H. ^{[6
]}

Ojha, Sunil ^{[8
]}

Schnoes, Alexandra M. ^{[9
]}

Stryke, Doug ^{[6
]}

Yunes, Jeffrey M. ^{[10
,11
]}

Ferrin, Thomas E. ^{[1
,6
,12
]}

Holliday, Gemma L. ^{[1
]}

Babbitt, Patricia C. ^{[1
,6
,12
]}

机构：

[1] Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94158 USA

[2] Univ Andres Bello, Ctr Bioinformat & Integrat Biol, Fac Ciencias Biol, Santiago 8370146, Chile

[3] Nodality Inc, San Francisco, CA 94080 USA

[4] Boston Univ, Coll Engn, Dept Elect & Comp Engn, Boston, MA 02215 USA

[5] MIT, Dept Chem Engn, Cambridge, MA 02139 USA

[6] Univ Calif San Francisco, Sch Pharm, Dept Pharmaceut Chem, San Francisco, CA 94158 USA

[7] Univ Hamburg, Ctr Bioinformat ZBH, D-20146 Hamburg, Germany

[8] Montana State Univ, Dept Chem & Biochem, Bozeman, MT 59717 USA

[9] Univ Calif San Francisco, Sch Med, San Francisco, CA 94143 USA

[10] Univ Calif San Francisco, UC Berkeley, UCSF Grad Program Bioengn, San Francisco, CA 94158 USA

[11] Univ Calif San Francisco, UC Berkeley, UCSF Grad Program Bioengn, Berkeley, CA 94720 USA

[12] Univ Calif San Francisco, Calif Inst Quantitat Biosci, San Francisco, CA 94158 USA

来源：

NUCLEIC ACIDS RESEARCH | 2014年 / 42卷 / D1期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

RESOURCE; EVOLUTION; MODELS; CLASSIFICATION; ANNOTATION; PREDICTION; DISCOVERY; HOMOLOGY; ENZYMES; SITES;

D O I：

10.1093/nar/gkt1130

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The Structure-Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies 'look alike', making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity.

引用

页码：D521 / D530

页数：10

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