Synthetic Biology Tools for Targeted Incorporation of Non-Canonical Amino Acids into Cellular Proteins
Bibliographic record
Abstract
Proteins mediate many essential functions in cells, and methods to profile cellular proteins are of great interest for biological discovery. Whereas all of the cells in an organism share the same genome, the landscape of proteins (the proteome) varies between different cell types and over the lifetime of the organism. Rapid progress in mass spectrometers is enabling the detailed analysis of cellular proteomes. Whereas better instruments increase coverage, throughput, and measurement precision, new chemical reporters, metabolic tags, and synthetic biology techniques are required to enhance the specificity and spatiotemporal resolution of protein labeling and detection. This work introduces methods for cell-selective proteome analysis through the incorporation of non-canonical amino acids into newly synthesized proteins. Chapter I provides an overview of current technologies for translational profiling and proteomic analysis in cells. Strategies for the residue-specific incorporation of non-canonical amino acids and bioorthogonal non-canonical amino acid tagging are discussed. Chapter II introduces a new approach for the identification of secreted bacterial proteins from infected host cells using non-canonical amino acid labeling. This work demonstrates an application of cell-selective proteome labeling. Selectivity is achieved through controlled expression of a mutant aminoacyl tRNA synthetase (aaRS) enzyme that enables the metabolic incorporation of a non-canonical amino acid. Ideally, the activity of multiple genes should be used to genetically control the extent of proteome labeling in cells. This is useful because many cell states are characterized by the activity of multiple genes and identified based on the expression of several proteins. Therefore chapter III introduces a novel approach to control proteome labeling as a function of multiple promoters using a genetically encoded AND gate based on a bisected methionyl-tRNA synthetase, a class I aaRS. Cellular protein labeling occurs only upon activation of two different promoters that drive expression of the N- and C-terminal fragments of this bisected aaRS. The utility of this tool is demonstrated by the selective labeling of proteins in subpopulations of bacterial cells in a laminar-flow microfluidic channel. Chapter IV extends the cell-selective incorporation of non-canonical amino acids from bacterial systems to mammalian cells by introducing a mutant mammalian methionyl-tRNA synthetase for cell-targeted proteome labeling. This enzyme is genetically encoded and can be conditionally activated for time-resolved and cell-targeted proteome analysis in a variety of different mammalian cell types. Chapter V uses this enzyme for lineage-specific proteomic analysis of mouse embryonic stem cells during differentiation to cardiac and mesoderm lineages. This approach for lineage-specific protein labeling enables the unbiased and comprehensive analysis of proteomic changes that occur during stem cell differentiation and cell-fate commitment. Appendices A-G provide brief summaries of publications and research efforts during my PhD that are not directly related to this thesis. These publications are the result of a number of collaborations that I have been fortunate to be involved with during my graduate research. The technologies and methods introduced in this thesis provide versatile tools for the comprehensive and unbiased detection and identification of newly synthesized proteins in complex multicellular systems. Time-resolved, genetically encoded, and spatially defined non-canonical amino acid incorporation enables the identification of proteins involved in cell-cell interactions and the proteins made during specific cell states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".