Assigning function to yeast proteins by integration of technologies.
Interpreting genome sequences requires the functional analysis of thousands of predicted proteins, many of which are uncharacterized and without obvious homologs. To assess whether the roles of large sets of uncharacterized genes can be assigned by targeted application of a suite of technologies, we used four complementary protein-based methods to analyze a set of 100 uncharacterized but essential open reading frames (ORFs) of the yeast Saccharomyces cerevisiae. These proteins were subjected to affinity purification and mass spectrometry analysis to identify copurifying proteins, two-hybrid analysis to identify interacting proteins, fluorescence microscopy to localize the proteins, and structure prediction methodology to predict structural domains or identify remote homologies. Integration of the data assigned function to 48 ORFs using at least two of the Gene Ontology (GO) categories of biological process, molecular function, and cellular component; 77 ORFs were annotated by at least one method. This combination of technologies, coupled with annotation using GO, is a powerful approach to classifying genes.
Hazbun
Tony R
TR
Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
Malmström
Lars
L
Anderson
Scott
S
Graczyk
Beth J
BJ
Fox
Bethany
B
Riffle
Michael
M
Sundin
Bryan A
BA
Aranda
J Derringer
JD
McDonald
W Hayes
WH
Chiu
Chun-Hwei
CH
Snydsman
Brian E
BE
Bradley
Phillip
P
Muller
Eric G D
EG
Fields
Stanley
S
Baker
David
D
Yates
John R
JR
Davis
Trisha N
TN
D019295:Computational Biology; D016681:Genome, Fungal; D020411:Oligonucleotide Array Sequence Analysis; D016366:Open Reading Frames; D020543:Proteome / Q000032:analysis; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism*; D020798:Two-Hybrid System Techniques
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins