Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin 050010, Colombia.
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60348 Frankfurt am Main, Germany.
Molecules. 2021 Mar 16;26(6):1664. doi: 10.3390/molecules26061664.
Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.
近年来，由于肽类在生物标志物、治疗替代品或疫苗抗原亚单位中的应用，其研究有所增加。计算资源的应用促进了新序列的识别、性质的预测和结构的建模。然而，仍然缺乏能够实现其直接分析的开源协议。在这里，我们介绍 PepFun，这是一个生物信息学和化学信息学功能的集合，易于实现和定制，可用于在不同水平上研究肽：序列、结构及其与蛋白质的相互作用。PepFun 能够为大量肽序列集计算多个特征，并获得源自蛋白-肽复合物的不同结构可观察值。此外，可以为筛选活动定制随机或有指导的肽序列文库设计。该软件包是在 Python 语言下创建的，基于内置函数和开源项目 BioPython 和 RDKit 中的可用方法。我们展示了两个教程，其中我们测试了 MHC 类 II 和 Granzyme B 蛋白酶的肽结合物。
