Rouger Quentin, Giudice Emmanuel, Meyer Damien F, Macé Kévin
Univ. Rennes, CNRS, Institut de Génétique et Développement de Rennes (IGDR)-UMR6290, Rennes 35000, France.
CIRAD, UMR ASTRE, Petit-Bourg, Guadeloupe 97170, France.
Bioinform Adv. 2025 Apr 24;5(1):vbaf090. doi: 10.1093/bioadv/vbaf090. eCollection 2025.
Protein structure and protein-protein interaction (PPI) predictions based on coevolution have transformed structural biology, but managing pre-processing and post-processing can be complex and time-consuming, making these tools less accessible.
Here, we introduce PPIFold, a pipeline built on the AlphaPulldown Python package, designed to automate file handling and streamline the generation of outputs, facilitating the interpretation of PPI prediction results. The pipeline was validated on the bacterial Type 4 Secretion System nanomachine, demonstrating its effectiveness in simplifying PPI analysis and enhancing accessibility for researchers.
PPIFold is implemented as a pip package and available at: https://github.com/Qrouger/PPIFold.
基于共进化的蛋白质结构和蛋白质-蛋白质相互作用（PPI）预测已经改变了结构生物学，但预处理和后处理可能复杂且耗时，使得这些工具的使用不太便捷。
在此，我们介绍PPIFold，这是一个基于AlphaPulldown Python包构建的流程，旨在自动处理文件并简化输出生成，便于解释PPI预测结果。该流程在细菌IV型分泌系统纳米机器上得到验证，证明了其在简化PPI分析和提高研究人员可及性方面的有效性。
PPIFold作为一个pip包实现，可在以下网址获取：https://github.com/Qrouger/PPIFold 。
