Massively parallel measurement of protein-protein interactions by sequencing using MP3-seq.

Protein-protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers.

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

Active Analyte Import Improves the Dynamic Range and Sensitivity of a Vitamin B12 Biosensor.

Cell-free systems provide a versatile platform for the development of low-cost, easy-to-use sensors for diverse analytes. However, sensor affinity dictates response sensitivity, and improving binding affinity can be challenging. Here, we describe efforts to address this problem while developing a biosensor for vitamin B12, a critical micronutrient. We first use a B12-responsive transcription factor to enable B12-dependent output in a cell-free reaction, but the resulting sensor responds to B12 far above clinically relevant concentrations. Surprisingly, when expressed in cells, the same sensor mediates a much more sensitive response to B12. The sensitivity difference is partly due to regulated import that accumulates cytoplasmic B12. Overexpression of importers further improves sensitivity, demonstrating an inherent advantage of whole-cell sensors. The resulting cells can respond to B12 in serum, can be lyophilized, and are functional in a minimal-equipment environment, showing the potential utility of whole-cell sensors as sensitive, field-deployable diagnostics.

Dynamic and tunable metabolite control for robust minimal-equipment assessment of serum zinc.

Bacterial biosensors can enable programmable, selective chemical production, but difficulties incorporating metabolic pathways into complex sensor circuits have limited their development and applications. Here we overcome these challenges and present the development of fast-responding, tunable sensor cells that produce different pigmented metabolites based on extracellular concentrations of zinc (a critical micronutrient). We create a library of dual-input synthetic promoters that decouple cell growth from zinc-specific metabolite production, enabling visible cell coloration within 4 h. Using additional transcriptional and metabolic control methods, we shift the response thresholds by an order of magnitude to measure clinically relevant zinc concentrations. The resulting sensor cells report zinc concentrations in individual donor serum samples; we demonstrate that they can provide results in a minimal-equipment fashion, serving as the basis for a field-deployable assay for zinc deficiency. The presented advances are likely generalizable to the creation of other types of sensors and diagnostics.