Prediction and assignment of function for a divergent N-succinyl amino acid racemase

被引:74
作者
Song, Ling
Kalyanaraman, Chakrapani
Fedorov, Alexander A.
Fedorov, Elena V.
Glasner, Margaret E.
Brown, Shoshana
Imker, Heidi J.
Babbitt, Patricia C.
Almo, Steven C.
Jacobson, Matthew P.
Gerlt, John A.
机构
[1] Univ Illinois, Dept Chem & Biochem, Urbana, IL 61801 USA
[2] Univ Calif San Francisco, Sch Pharm, Dept Pharmaceut Chem, San Francisco, CA 94158 USA
[3] Albert Einstein Coll Med, Dept Biochem, Bronx, NY 10461 USA
[4] Univ Calif San Francisco, Sch Pharm, Dept Biopharmaceut Sci, San Francisco, CA 94158 USA
[5] Univ Calif San Francisco, Calif Inst Quantitat Biomed Res, San Francisco, CA 94158 USA
关键词
D O I
10.1038/nchembio.2007.11
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 protein databases contain many proteins with unknown function. A computational approach for predicting ligand specificity that requires only the sequence of the unknown protein would be valuable for directing experiment-based assignment of function. We focused on a family of unknown proteins in the mechanistically diverse enolase superfamily and used two approaches to assign function: (i) enzymatic assays using libraries of potential substrates, and (ii) in silico docking of the same libraries using a homology model based on the most similar (35% sequence identity) characterized protein. The results matched closely; an experimentally determined structure confirmed the predicted structure of the substrate-liganded complex. We assigned the N-succinyl arginine/lysine racemase function to the family, correcting the annotation (L-Ala-D/L-Glu epimerase) based on the function of the most similar characterized homolog. These studies establish that ligand docking to a homology model can facilitate functional assignment of unknown proteins by restricting the identities of the possible substrates that must be experimentally tested.
引用
收藏
页码:486 / 491
页数:6
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