Department of Molecular Microbiology, John Innes Centre, Norwich, UK.
Microbiology (Reading). 2024 Jul;170(7). doi: 10.1099/mic.0.001473.
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.
人工智能已经彻底改变了蛋白质结构预测领域。然而,随着更强大和复杂的软件的开发,对于最终用户来说,软件的可访问性和易用性而不是功能正迅速成为一个限制因素。LazyAF 是一个基于 Google Colaboratory 的流水线,它集成了现有的 ColabFold BATCH 软件,以简化中规模蛋白质-蛋白质相互作用预测的过程。LazyAF 被用于预测广谱多药耐药质粒 RK2 上编码的 76 个蛋白质的互作组,展示了该流水线提供的易用性和可访问性。