Evert, S. | bioRxiv Science

bioRxiv2025

Mapping Structural Constraints and Adaptive Potential in a Capsule-Degrading Phage Tailspike Protein

Bacteriophage tailspike proteins (TSPs) degrade bacterial capsules to enable infection, yet the molecular determinants of their function and host range remain unclear. We applied deep mutational scanning (DMS) to the endosialidase TSP of Escherichia coli K1 phage K1F, generating 22,365 single-amino-acid variants using an enhanced ORACLE phage engineering platform. Functional scores revealed the TSP is structurally fragile yet harbors pockets of adaptive flexibility. Mutations within the {beta}-propeller active site uncovered residues accommodating longer sialic acid chains than captured by structural studies, while the {beta}-helix stalk emerged as an adaptive "tuning knob" modulating processivity and specificity. Comparative selections across K1 strains identified discrimination hotspots in {beta}-barrel loops and distal residues outside canonical binding sites, implicating capsule modifications and O-antigen presence as key modulators of host range. By resolving how specific mutations modulate function and host range, this study offers a roadmap for designing phages that overcome capsule-based defenses in pathogenic bacteria.

biochemistry↗

bioRxiv2025

Multiobjective learning and design of bacteriophage specificity

To better understand and design proteins, it is crucial to consider the multifunctional landscapes on which all proteins exist. Proteins are often optimized for single functions during design and engineering, without considering the countless other functionalities that may contribute to or interfere with the intended outcome. In this work, we apply deep learning to understand and design the multifunctional host-targeting landscape of the T7 bacteriophage receptor binding protein for enhanced infectivity, pre-defined specificity, and high generality in virulence toward unseen strains. We compare several different model architectures and design approaches and experimentally characterize designed phages optimized for 26 diverse tasks. We demonstrate that with multiobjective machine learning, it is possible to design complex specificities at success rates that can enable low-throughput validation of predicted hits. Our results show that the targeting capabilities of T7 are highly plastic, with opposite specificities often separated by only a few mutations. This level of tunability underscores how models trained on multifunctional data can uncover key principles of phage biology and specificity. The same modeling framework can be applied to guide the multiobjective design of other proteins or mutable biological systems, offering a general strategy for navigating multifunctional landscapes.

synthetic biology↗