4.7 Article

Network Analysis of Sequence-Function Relationships and Exploration of Sequence Space of TEM β-Lactamases

期刊

ANTIMICROBIAL AGENTS AND CHEMOTHERAPY
卷 60, 期 5, 页码 2709-2717

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AMER SOC MICROBIOLOGY
DOI: 10.1128/AAC.02930-15

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资金

  1. German Research Foundation DFG [SFB716]
  2. Ministry of Science, Research, and the Arts of Baden-Wuerttemberg, Germany (Biosynthesis netWork BW2)

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The Lactamase Engineering Database (www.LacED.uni-stuttgart.de) was developed to facilitate the classification and analysis of TEM beta-lactamases. The current version contains 474 TEM variants. Two hundred fifty-nine variants form a large scale-free network of highly connected point mutants. The network was divided into three subnetworks which were enriched by single phenotypes: one network with predominantly 2be and two networks with 2br phenotypes. Fifteen positions were found to be highly variable, contributing to the majority of the observed variants. Since it is expected that a considerable fraction of the theoretical sequence space is functional, the currently sequenced 474 variants represent only the tip of the iceberg of functional TEM beta-lactamase variants which form a huge natural reservoir of highly interconnected variants. Almost 50% of the variants are part of a quartet. Thus, two single mutations that result in functional enzymes can be combined into a functional protein. Most of these quartets consist of the same phenotype, or the mutations are additive with respect to the phenotype. By predicting quartets from triplets, 3,916 unknown variants were constructed. Eighty-seven variants complement multiple quartets and therefore have a high probability of being functional. The construction of a TEM beta-lactamase network and subsequent analyses by clustering and quartet prediction are valuable tools to gain new insights into the viable sequence space of TEM beta-lactamases and to predict their phenotype. The highly connected sequence space of TEM beta-lactamases is ideally suited to network analysis and demonstrates the strengths of network analysis over tree reconstruction methods.

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