RadicalSAM.org: A Resource to Interpret Sequence-Function Space and Discover New Radical SAM Enzyme Chemistry
被引:65
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作者:
Oberg, Nils
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机构:
Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USAUniv Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Oberg, Nils
[1
]
Precord, Timothy W.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Univ Illinois, Dept Chem, Urbana, IL 61801 USAUniv Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Precord, Timothy W.
[1
,2
]
Mitchell, Douglas A.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Univ Illinois, Dept Microbiol, Urbana, IL 61801 USAUniv Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Mitchell, Douglas A.
[1
,4
]
Gerlt, John A.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Univ Illinois, Dept Biochem, Urbana, IL 61801 USAUniv Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
Gerlt, John A.
[1
,3
]
机构:
[1] Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA
[2] Univ Illinois, Dept Chem, Urbana, IL 61801 USA
[3] Univ Illinois, Dept Biochem, Urbana, IL 61801 USA
[4] Univ Illinois, Dept Microbiol, Urbana, IL 61801 USA
Radical SAM superfamily;
genomic enzymology;
web tools;
functional assignment;
isofunctional;
families;
protein sequence similarity networks;
genome neighborhood diagrams;
ENZYMOLOGY WEB TOOLS;
DIVERGENT EVOLUTION;
PROTEIN FAMILIES;
ALIGNMENT;
PREDICTION;
PATHWAYS;
DOMAINS;
MUSCLE;
D O I:
10.1021/acsbiomedchemau.1c00048
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 radical SAM superfamily (RSS), arguably the most functionally diverse enzyme superfamily, is also one of the largest with similar to 700 K members currently in the UniProt database. The vast majority of the members have uncharacterized enzymatic activities and metabolic functions. In this Perspective, we describe RadicalSAM.org, a new web-based resource that enables a userfriendly genomic enzymology strategy to explore sequencefunction space in the RSS. The resource attempts to enable identification of isofunctional groups of radical SAM enzymes using sequence similarity networks (SSNs) and the genome context of the bacterial, archaeal, and fungal members provided by genome neighborhood diagrams (GNDs). Enzymatic activities and in vivo functions frequently can be inferred from genome context given the tendency for genes of related function to be clustered. We invite the scientific community to use RadicalSAM.org to (i) guide their experimental studies to discover new enzymatic activities and metabolic functions, (ii) contribute experimentally verified annotations to RadicalSAM.org to enhance the ability to predict novel activities and functions, and (iii) provide suggestions for improving this resource.