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Summary
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Deep learning enables designing T7 bacteriophage receptor binding proteins for multiple functions, enhancing infectivity and specificity. This approach optimizes protein design by considering complex multifunctional landscapes.

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Area of Science:

  • Protein engineering and computational biology.
  • Synthetic biology and virology.
  • Machine learning applications in biological systems.

Background:

  • Proteins are often engineered for single functions, neglecting their inherent multifunctionality.
  • Understanding multifunctional landscapes is key for effective protein design and engineering.
  • The T7 bacteriophage receptor binding protein serves as a model for studying host-targeting capabilities.

Purpose of the Study:

  • To apply deep learning for understanding and designing the multifunctional host-targeting landscape of the T7 bacteriophage receptor binding protein.
  • To enhance protein infectivity, specificity, and virulence generality using multiobjective machine learning.
  • To explore the plasticity and tunability of phage targeting capabilities.

Main Methods:

  • Utilized deep learning models to analyze and predict protein functions.
  • Compared various model architectures and design strategies for protein optimization.
  • Experimentally characterized designed phages optimized for 26 distinct tasks.
  • Employed multiobjective machine learning for complex specificity design.

Main Results:

  • Demonstrated successful design of complex specificities with high success rates enabling validation.
  • Showcased the high plasticity of T7 bacteriophage targeting capabilities, with few mutations altering specificity.
  • Validated the effectiveness of multiobjective machine learning in navigating multifunctional landscapes.
  • Identified key principles of phage biology and specificity through multifunctional data analysis.

Conclusions:

  • Multiobjective machine learning provides a viable strategy for designing proteins with complex, tailored functionalities.
  • The T7 bacteriophage system exhibits significant tunability, offering insights into protein specificity.
  • The developed modeling framework can be generalized for the multiobjective design of other proteins and biological systems.
  • This work advances the understanding of multifunctional landscapes in protein engineering.