Chimeric recombinases with designed DNA sequence recognition.
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ABSTRACT:
SUBMITTER: Akopian A
PROVIDER: S-EPMC166373 | biostudies-other | 2003 Jul
REPOSITORIES: biostudies-other
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Project description:Site-specific recombinases are powerful tools for genome engineering. Hyperactivated variants of the resolvase/invertase family of serine recombinases function without accessory factors, and thus can be re-targeted to sequences of interest by replacing native DNA-binding domains (DBDs) with engineered zinc-finger proteins (ZFPs). However, imperfect modularity with particular domains, lack of high-affinity binding to all DNA triplets, and difficulty in construction has hindered the widespread adoption of ZFPs in unspecialized laboratories. The discovery of a novel type of DBD in transcription activator-like effector (TALE) proteins from Xanthomonas provides an alternative to ZFPs. Here we describe chimeric TALE recombinases (TALERs): engineered fusions between a hyperactivated catalytic domain from the DNA invertase Gin and an optimized TALE architecture. We use a library of incrementally truncated TALE variants to identify TALER fusions that modify DNA with efficiency and specificity comparable to zinc-finger recombinases in bacterial cells. We also show that TALERs recombine DNA in mammalian cells. The TALER architecture described herein provides a platform for insertion of customized TALE domains, thus significantly expanding the targeting capacity of engineered recombinases and their potential applications in biotechnology and medicine.
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Project description:Binding of transcription factors to DNA is mediated by the recognition of the chemical signatures of the DNA bases and the three-dimensional shape of the DNA molecule. The direct contribution of DNA shape to DNA-binding specificity has been difficult to assess, as DNA shape is a consequence of its sequence. Here, we teased apart these two modes of recognition in the context of Hox-DNA binding. We made a series of mutations in Hox residues that, in a co-crystal structure, only recognize DNA shape, and tested the effect on DNA binding preferences using SELEX-seq. Analysis of shape features of selected sequences revealed that these residues are both necessary and sufficient for selection of sequences with distinct shape features. We used statistical machine learning to show that the accuracy of binding specificity predictions improves by adding shape features to a model that only depends on sequence. We conclude that shape readout is a direct and critical component of binding site selection by Hox proteins.
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