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Workshop: Machine Learning for Drug Discovery (MLDD)

Machine Learning to Hunt for Phage Proteins to Catch Klebsiella

George Wright · Fayyaz ul Amir Minhas · Slawomir Michniewski · Eleanor Jameson

Keywords: [ machine learning ]


Antimicrobial resistance (AMR) has been declared a global threat by the WorldHealth Organization. Development of novel and effective therapies against mi-crobes is an active research area of ever-growing importance. One of the leadingthreats are Klebsiella species, which cause virulent AMR infections with highdeath rates, particularly in hospital settings. Klebsiella species are particularlyproblematic because they produce a thick sticky polysaccharide capsule that pro-tects them from antimicrobials and allows them to build highly resistant biofilms- defensive layers of cells. A natural solution to eradicate Klebsiella capsules andbiofilms are depolymerase proteins that can target and neutralize polysaccharidecapsules of specific Klebsiella species, often found in bacteriophages. However,machine learning guided discovery of depolymerase proteins in such phages is anunexplored area.In this work, we use machine learning to help identify proteins in phage pro-teomes that can act as depolymerases against Klebsiella. Specifically, we utilize adataset of phages, containing depolymerase proteins, that can target and neutralizepolysaccharide capsules of specific Klebsiella species. We train a ranking modelto rank proteins in an input phage proteome based on their predicted ability to actas a depolymerase. We use a non-redundant validation protocol to evaluate thepredictive accuracy of the proposed model. Our analysis shows that for all testproteomes containing at least one depolymerase, the depolymerase protein wasranked within the top scoring 5% of proteins. We expect that the proposed ap-proach (called Depolymerase Ranker) will be useful in accelerating the discoveryof such antibacterial proteins in the wet lab.

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