eLifeSynthetic BiologyPeer-reviewed journal article
Mapping the Functional Landscape of the Receptor Binding Domain of T7 Bacteriophage by Deep Mutational Scanning
Phil HussiD, Anthony MegeriD, Megan LeanderiD, Kyle K. NishikawaiD, Srivatsan RamaniD
Abstract
The interaction between a bacteriophage and its host is mediated by the phages receptor binding protein (RBP). Despite its fundamental role in governing phage activity and host range, the molecular rules of RBP function remain a mystery. Here, we systematically dissect the functional role of every residue in the tip domain of T7 phage RBP using a novel phage genome engineering method called ORACLE (Optimized Recombination, Accumulation and Library Expression). ORACLE is a high-throughput, locus-specific, sequence-programmable method to create a large, unbiased library of phage variants at a targeted gene locus. Using ORACLE, we generated all single amino acid substitutions at every site (1660 variants) of the tip domain to quantify the functional role of all variants on multiple bacterial hosts. This rich dataset allowed us to cross compare functional profiles of each host to precisely identify regions of functional importance, many which were previously unknown. Host-specific substitution patterns displayed differences in site specificity and physicochemical properties of mutations indicating exquisite adaptation to individual hosts. Comparison of enriched variants across hosts also revealed a tradeoff between activity and host range. We discovered gain-of-function variants effective against resistant hosts and host-constricting variants that selectively eliminated certain hosts. We demonstrate therapeutic utility against uropathogenic E. coli by engineering a highly active T7 variants to avert emergence of spontaneous resistance of the pathogen. Our approach presents a generalized framework for systematic and comprehensive characterization of sequence-function relationships in phages on an unprecedented scale.
- organism: E. coli
- assay: CRISPR screen
- assay: proteomics
- research focus: variant effect prediction
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: deep learning
- approach: experimental assay
- Citations
- 68
- FWCI
- 5.67
- Field percentile
- top 3%
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