SPINE

SPINE enables saturated, programmable insertion engineering to perform targeted insertional mutagenesis and generate comprehensive domain insertion profiles across proteins.


Key Features:

  • Unbiased Insertion: SPINE overcomes sequence bias inherent in MuA transposon-based approaches to produce more equitable representation of donor-domain insertions across target proteins.
  • Comprehensive Coverage: Oligo library synthesis combined with multi-step Golden Gate cloning achieves near-complete and highly redundant coverage of potential insertion sites.
  • High-Quality Libraries: Benchmarking against MuA transposon-mediated techniques demonstrated enrichment for in-frame insertions and drastically reduced sequence bias in SPINE-generated libraries.
  • Versatility Across Genes: SPINE consistently generates high-quality insertion libraries for a wide range of target proteins, avoiding sparse or biased coverage observed with traditional transposon methods.

Scientific Applications:

  • Protein structure–function mapping: Create saturated domain insertion profiles to identify positions that tolerate or disrupt donor-domain insertions and thereby map structure–function relationships.
  • Evolutionary analysis: Examine evolutionary forces shaping protein architecture by assessing positional permissibility for domain insertions.
  • Protein engineering for biomedical applications: Identify permissive insertion sites to engineer proteins with novel or enhanced functions by inserting donor domains.

Methodology:

Oligo library synthesis combined with multi-step Golden Gate cloning is used to insert donor domains at near-comprehensive positions; permissibility maps were constructed and validated on the Inward Rectifier K+ channel Kir2.1 and compared to MuA transposon-mediated libraries.

Topics

Details

License:
MIT
Programming Languages:
Python
Added:
1/14/2020
Last Updated:
11/24/2024

Operations

Publications

Coyote-Maestas W, Nedrud D, Okorafor S, He Y, Schmidt D. Targeted insertional mutagenesis libraries for deep domain insertion profiling. Nucleic Acids Research. 2019;48(2):e11-e11. doi:10.1093/nar/gkz1110. PMID:31745561. PMCID:PMC6954442.

PMID: 31745561
PMCID: PMC6954442
Funding: - National Institutes of Health: MH109038