Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features
Tze Young Thung, Murray E. White, Wei Dai, Jonathan J. Wilksch, Rebecca S. Bamert, Andrea Rocker, Christopher J. Stubenrauch, Daniel R. Williams, Cheng Huang, Ralf Schittelhelm, Jeremy J. Barr, Eleanor Jameson, Sheena McGowan, Yanju Zhang, Jiawei Wang, Rhys A. Dunstan, Trevor Lithgow
Abstract
In response to the global problem of antimicrobial resistance, there are moves to use bacteriophages (phages) as therapeutic agents. Selecting which phages will be effective therapeutics relies on interpreting features contributing to shelf-life and applicability to diagnosed infections. However, the protein components of the phage virions that dictate these properties vary so much in sequence that best estimates suggest failure to recognize up to 90% of them. We have utilized this diversity in evolutionary features as an advantage, to apply machine learning for prediction accuracy for diverse components in phage virions. We benchmark this new tool showing the accurate recognition and evaluation of phage component parts using genome sequence data of phages from undersampled environments, where the richest diversity of phage still lies.