Prediction of effector proteins is of paramount importance due to their crucial role as first-line invaders while establishing a pathogen-host interaction, often leading to infection of the host. Prediction of T6 effector proteins is a new challenge since the discovery of T6 Secretion System and the unique nature of the particular secretion system. In this paper, we have first designed a Python-based standalone tool, called PyPredT6, to predict T6 effector proteins. A total of 873 unique features has been extracted from the peptide and nucleotide sequences of the experimentally verified effector proteins. Based on these features and using machine learning algorithms, we have performed in silico prediction of T6 effector proteins in Vibrio cholerae and Yersinia pestis to establish the applicability of PyPredT6. PyPredT6 is available at http://projectphd.droppages.com/PyPredT6.html .

中文翻译：

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PyPredT6：用于识别 VI 型效应蛋白的基于 python 的预测工具

效应蛋白的预测至关重要，因为它们在建立病原体-宿主相互作用时作为一线入侵者的关键作用，通常会导致宿主感染。自从发现 T6 分泌系统和特定分泌系统的独特性质以来，T6 效应蛋白的预测是一个新的挑战。在本文中，我们首先设计了一个基于 Python 的独立工具，称为 PyPredT6，用于预测 T6 效应蛋白。从实验验证的效应蛋白的肽和核苷酸序列中提取了总共 873 个独特的特征。基于这些特征并使用机器学习算法，我们对霍乱弧菌和鼠疫耶尔森菌中的 T6 效应蛋白进行了计算机预测，以确定 PyPredT6 的适用性。PyPredT6 可在 http: