StarPepToolbox：一个开源软件，用于使用复杂网络辅助生物活性肽的化学空间分析及其功能研究。

StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks.

机构信息

Grupo de Medicina Molecular y Translacional (MeM&T), Facultad de Medicina, Universidad San Francisco de Quito (USFQ), Quito, Ecuador.

Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California 22860, México.

出版信息

Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad506.

DOI:10.1093/bioinformatics/btad506

PMID:37603724

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10469104/

Abstract

MOTIVATION

Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs.

RESULTS

This report highlights StarPep Toolbox, an open-source and user-friendly software to study the bioactive chemical space of AMPs using complex network-based representations, clustering, and similarity-searching models. The novelty of this research lies in the combination of network science and similarity-searching techniques, distinguishing it from conventional methods based on machine learning and other computational approaches. The network-based representation of the AMP chemical space presents promising opportunities for peptide drug repurposing, development, and optimization. This approach could serve as a baseline for the discovery of a new generation of therapeutics peptides.

AVAILABILITY AND IMPLEMENTATION

All underlying code and installation files are accessible through GitHub (https://github.com/Grupo-Medicina-Molecular-y-Traslacional/StarPep) under the Apache 2.0 license.

摘要

动机

抗菌肽（AMPs）是一种很有前途的分子，可以治疗由多药耐药病原体、某些类型的癌症和其他疾病引起的传染病。计算机辅助策略是 AMPs 高通量筛选的有效工具。

结果

本报告重点介绍了 StarPep Toolbox，这是一个开源且用户友好的软件，用于使用基于复杂网络的表示、聚类和相似性搜索模型研究 AMPs 的生物活性化学空间。这项研究的新颖之处在于将网络科学和相似性搜索技术相结合，使其有别于基于机器学习和其他计算方法的传统方法。AMP 化学空间的网络表示为肽类药物重新定位、开发和优化提供了有前景的机会。这种方法可以作为发现新一代治疗性肽的基准。