Alexey E. Kazakov

This study identifies host determinants for phage infection in Mycobacterium smegmatis using CRISPR interference, revealing receptor proteins and lipid components involved in phage entry.

PhageTAILor uses LightGBM on 74 detector features to flag prophages and bacterial phage tail-like elements (tailocins, T6SS, eCIS), recovering 12 of 13 validated tailocins under leave-one-genus-out.

The paper identifies target genes for 31 transcription factors in two Rhodanobacter strains using DAP-seq and comparative genomics, revealing conserved regulatory networks and a key global regulator Clp.

The paper predicts phage receptor specificity from genome data using large-scale genetic screens and machine learning, achieving high accuracy for E. coli phages.

This paper develops a machine learning framework to predict strain-level phage-host interactions from genomes, achieving high accuracy without phylogenetic constraints.