iPASS

iPASS identifies permissive amber stop codon sites for efficient non-canonical amino acid (ncAA) incorporation in mammalian cells by predicting sequence-context–dependent suppression efficiencies.

Key Features:

• Amber site identification: Identifies permissive in-frame UAG (amber) stop codon positions for ncAA incorporation in target proteins.
• Orthogonal suppression system: Utilizes the orthogonal pyrrolysyl-tRNA synthetase (PylRS)/tRNAPylCUA (PylT) pair to enable suppression of UAG codons and ncAA integration.
• Proteomics-based quantification: Quantifies ncAA incorporation rates across numerous endogenous amber stop codons in mammalian cells using proteomics data.
• Predictive modeling: Generates a linear regression model that predicts relative ncAA incorporation efficiencies from the nucleotide sequence context surrounding each UAG codon.
• Experimental validation: Validated with a dual-fluorescence reporter system and high-throughput flow-cytometry to measure context-specific ncAA incorporation efficiencies.
• Context dependence: Reports that nucleotides both upstream and downstream of the UAG codon synergistically influence incorporation rates and that these effects are consistent across different cell lines and ncAAs.
• Codon optimization guidance: Guides synonymous codon exchanges of residues flanking the amber codon to improve ncAA incorporation efficiency.
• Integrated in silico and in vivo approach: Combines in silico predictions with experimental validation in living mammalian cells.

Scientific Applications:

• Genetic code expansion: Supports expansion of the genetic code in mammalian systems by enabling targeted ncAA integration at UAG codons.
• Protein engineering: Facilitates selection and optimization of incorporation sites for introducing ncAAs into proteins for functional studies or novel chemistries.
• Synthetic biology: Provides predictive and experimental workflows for designing and validating ncAA-containing constructs in mammalian cell contexts.

Methodology:

Proteomics-based quantification of ncAA incorporation at endogenous UAG codons followed by linear regression modeling of the surrounding nucleotide sequence context to predict relative incorporation efficiencies.