Warren, Nicholas; Strom, Alexander; Nicolet, Brianna; Albin, Kristine; Albrecht, Joshua; Bausch, Brenna; Dobbe, Megan; Dudek, Megan; Firgens, Samuel; Fritsche, Chad; Gunderson, Anthony; Heimann, Joseph; Her, Cheng; Hurt, Jordan; Konorev, Dmitri; Lively, Matthew; Meacham, Stephanie; Rodriguez, Valentina; Tadayon, Stephanie; Trcka, David; Yang, Yer; Bhattacharyya, Sudeep; Hati, Sanchita
Comparison of the Intrinsic Dynamics of Aminoacyl-tRNA Synthetases
PROTEIN JOURNAL, 33:184-198, APR 2014

Aminoacyl-tRNA synthetases (AARSs) are an important family of enzymes that catalyze tRNA aminoacylation reaction (Ibba and Soll in Annu Rev Biochem 2000, 69:617-650) [1]. AARSs are grouped into two broad classes (class I and II) based on sequence/structural homology and mode of their interactions with the tRNA molecule (Ibba and Soll in Annu Rev Biochem 2000, 69:617-650) [1]. As protein dynamics play an important role in enzyme function, we explored the intrinsic dynamics of these enzymes using normal mode analysis and investigated if the two classes and six subclasses (Ia-c and IIa-c) of AARSs exhibit any distinct patterns of motion. The present study found that the intrinsic dynamics-based classification of these enzymes is similar to that obtained based on sequence/structural homology for most enzymes. However, the classification of seryl-tRNA synthetase was not straightforward; the internal mobility patterns of this enzyme are comparable to both IIa and IIb AARSs. This study revealed only a few general mobility patterns in these enzymes-(1) the insertion domain is generally engaged in anticorrelated motion with respect to the catalytic domain for both classes of AARSs and (2) anticodon binding domain dynamics are partly correlated and partly anticorrelated with respect to other domains for class I enzymes. In most of the class II AARSs, the anticodon binding domain is predominately engaged in anticorrelated motion with respect to the catalytic domain and correlated to the insertion domain. This study supports the notion that dynamic-based classification could be useful for functional classification of proteins.

DOI:10.1007/s10930-014-9548-z

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