1887

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.

Funding
This study was supported by the:
  • Biotechnology and Biological Sciences Research Council (Award BB/X01097X/1)
    • Principle Award Recipient: ThomasCameron McLean
  • Wellcome Trust (Award 221776/Z/2/Z)
    • Principle Award Recipient: ThomasCameron McLean
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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/content/journal/micro/10.1099/mic.0.001473
2024-07-05
2024-07-15
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