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.
摘要：
人工智能彻底改变了蛋白质结构预测领域。然而,随着更强大、更复杂的软件的开发，它是可访问性和易用性，而不是功能，正在迅速成为最终用户的限制因素。LazyAF是一个基于GoogleColaboratory的管道，它集成了现有的ColabFoldBATCH软件，以简化中等规模的蛋白质-蛋白质相互作用预测过程。LazyAF用于预测在广泛宿主范围的多药抗性质粒RK2上编码的76种蛋白质的相互作用组，证明了管道提供的易用性和可及性。
