LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions

Microbiology (Reading). 2024 Jul;170(7):001473. doi: 10.1099/mic.0.001473.

Abstract

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

Keywords: alphafold; colabfold; modelling; protein-protein interactions; structure prediction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacterial Proteins / chemistry
  • Bacterial Proteins / genetics
  • Bacterial Proteins / metabolism
  • Computational Biology* / methods
  • Computer Simulation
  • Plasmids / genetics
  • Protein Binding
  • Protein Interaction Mapping*
  • Software*

Substances

  • Bacterial Proteins