Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes
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- 作者：Matthew W. Vetting ; Nawar Al-Obaidi ; Suwen Zhao ; Brian San Francisco ; Jungwook Kim ; Daniel J. Wichelecki ; Jason T. Bouvier ; Jose O. Solbiati ; Hoan Vu ; Xinshuai Zhang ; Dmitry A. Rodionov ; James D. Love ; Brandan S. Hillerich ; Ronald D. Seidel ; Ronald J. Quinn ; Andrei L. Osterman ; John E. Cronan ; Matthew P. Jacobson ; John A. Gerlt ; Steven C. Almo
- 刊名：Biochemistry
- 出版年：2015
- 出版时间：January 27, 2015
- 年：2015
- 卷：54
- 期：3
- 页码：909-931
- 全文大小：1334K
- ISSN：1520-4995
文摘
The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. We describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of the library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of d-Ala-d-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.