A global map of receptor-binding protein compatibility for the programmable design of Klebsiella and Acinetobacter phages
Asboth, A.; Stirling, T.; Mehi, O.; Apjok, G.; Klein De Sousa, V.; Taylor, N. M.; Hadj Mehdi, H.; Papp, B.; Kintses, B.
Show abstract
The narrow host range of bacteriophages limits their application against genetically diverse bacteria, motivating rational host-range modification. Progress in programmable engineering depends on establishing design principles that determine the compatibility of possible host-recognition modules with phage scaffolds. Here, we establish a genomics-guided framework that systematically identifies compatibility-determining adapter domains in receptor-binding proteins for the rational engineering of phages to target clinically relevant pathogen populations. Applying this approach to 1,270 phage genomes infecting Acinetobacter baumannii and Klebsiella pneumoniae, we show that viral diversity is highly structured: 60% of the 2,313 receptor-binding proteins group into only 19 major clusters sharing conserved N-terminal compatibility adapters. The structurally most conserved adapters in Autographivirales are associated with diverse capsule-degrading depolymerases. Known host-specificities within single-adapter clusters target capsule types that represent up to 29% of A. baumannii and 44% of K. pneumoniae carbapenem-resistant populations. Overall, we define a global repertoire of modular host-recognition components for programmable configuration of phage therapeutics.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Gamma-Mobile-Trio systems define a new class of mobile elements rich in bacterial defensive and offensive tools 96%
- Rational Design of Frontline Institutional Phage Cocktail for the Treatment of Nosocomial Enterobacter cloacae Complex Infections 96%
- The impact of genetic diversity on gene essentiality within the E. coli species 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.