UCSF

In chapter 1, we discuss the development of an ubiquitin-based proximity tagger for cell surface interactomics. Despite tremendous progress in developing technologies for unbiased analysis of protein-protein interactions for soluble proteins, technologies for cell surface ”interactomics” has proven more challenging. This is largely due to a lack of good proteomic tools for identifying weak, transient, and lipophilic complexes. However, due to the abundance of mammalian genes that encode for membrane proteins and the large number of drug targets that are found at the membrane, the ability to explore protein-protein and drug-protein interactions in this context has significant therapeutic importance. We describe the development of a tool we call the ”Ubiquitron”, which utilizes components of the endogenous ubiquitination system to label targets on the surface of intact cells with biotinylated ubiquitin, and does so in a minimally biased fashion with great sensitivity. We validate the speed and sensitivity of the tagger in a growth hormone/GHR system, and show that at least in a model system, targets can be unambiguously identified in a GFP-based model system by affinity purification-tandem mass spectrometry (AP-MS/MS). We also describe experiments where the tagger can be genetically fused to a receptor on the cell surface, and show that this ”Receptortron” can be used to assay the strength of interaction between two receptors. We envision using the ”Ubiquitron” to rapidly identify a partially- or fully-orphan ligand’s interactome, in contexts from phenotypic selections in phage display to identifying the targets of autoimmune antibodies.

In chapter 2, we turn to the development of a tool called ”PhaNGS” or Phage-antibody Next Generation Sequencing, a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies. Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. Using 144 pre-selected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B-cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL), and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1, dominate the response to similar oncogenic perturbations in B-cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNAseq for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states.