Costa Francesco, Barringer Rob, Riziotis Ioannis, Andreeva Antonina, Bateman Alex
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom.
School of Biochemistry, University of Bristol, University Walk, Bristol, BS8 1TD, United Kingdom.
Bioinform Adv. 2025 Mar 11;5(1):vbaf049. doi: 10.1093/bioadv/vbaf049. eCollection 2025.
Intramolecular isopeptide bonds contribute to the structural stability of proteins, and have primarily been identified in domains of bacterial fibrillar adhesins and pili. At present, there is no systematic method available to detect them in newly determined molecular structures. This can result in mis-annotations and incorrect modeling.
Here, we present Isopeptor, a computational tool designed to predict the presence of intramolecular isopeptide bonds in experimentally determined structures. Isopeptor utilizes structure-guided template matching via the Jess software, combined with a logistic regression classifier that incorporates root mean square deviation and relative solvent accessible area as key features. The tool demonstrates a precision of 1.0 and a recall of 0.947 when tested on a Protein Data Bank subset of domains known to contain intramolecular isopeptide bonds that have been deposited with incorrectly modeled geometries.
Isopeptor's Python-based implementation supports integration into bioinformatics workflows and can be accessed via the command line, through a Python API or via a Google Colaboratory implementation (https://colab.research.google.com/github/FranceCosta/Isopeptor_development/blob/main/notebooks/Isopeptide_finder.ipynb). Source code is hosted on GitHub (https://github.com/FranceCosta/isopeptor) and can be installed via the Python package installation manager PIP.
分子内异肽键有助于蛋白质的结构稳定性，主要在细菌纤维状粘附素和菌毛的结构域中被发现。目前，尚无系统的方法可在新确定的分子结构中检测它们。这可能导致错误注释和错误建模。
在此，我们展示了Isopeptor，这是一种计算工具，旨在预测实验确定的结构中分子内异肽键的存在。Isopeptor通过Jess软件利用结构引导的模板匹配，并结合了以均方根偏差和相对溶剂可及面积作为关键特征的逻辑回归分类器。在蛋白质数据库中已知含有分子内异肽键且已以错误建模几何结构存入的结构域子集上进行测试时，该工具显示出1.0的精度和0.947的召回率。
基于Python的Isopeptor实现支持集成到生物信息学工作流程中，可通过命令行、Python API或通过Google Colaboratory实现（https://colab.research.google.com/github/FranceCosta/Isopeptor_development/blob/main/notebooks/Isopeptide_finder.ipynb）进行访问。源代码托管在GitHub（https://github.com/FranceCosta/isopeptor）上，可通过Python包安装管理器PIP进行安装。
