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Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides
Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-05-12 , DOI: 10.1007/s12539-021-00435-6
Michela Chiara Caprani 1 , John Healy 1 , Orla Slattery 1 , Joan O'Keeffe 1
Affiliation  

The rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary structure, and function. However, despite recent advancements in rapid computational methods for AMP prediction from various mammalian, aquatic, and terrestrial species, there is limited information regarding their presence, functional roles, and family type from marine macroalgae. In this paper, we present a promising two-tier ensemble of heterogeneous machine learning models that integrates seven well-known machine learning classifiers to predict AMPs from macroalgae. The first tier of the ensemble consists of a suite of binary classifiers that identify AMPs from protein sequence data which are then forwarded to a second-tier multi-class ensemble to characterise their functional family type. The two-tier ensemble was successfully used to identify 39 putative AMP sequences in 12 macroalgae species from three different phyla groups. The approach we describe is not limited to AMPs and can also be applied to search sequence data for other types of proteins.



中文翻译:

使用集合来识别和分类巨藻抗菌肽

多重耐药微生物的快速传播促使研究人员发现了天然的替代疗法,例如抗菌肽 (AMP)。在第一道防线中,AMP 对多重耐药性病原细菌、病毒、真菌甚至癌症显示出广泛的有效活性。AMP 可以根据氨基酸组成、二级结构和功能进一步分为家族。然而,尽管最近在用于来自各种哺乳动物、水生和陆生物种的 AMP 预测的快速计算方法方面取得了进展,但关于它们的存在、功能作用和来自海洋大型藻类的家族类型的信息有限。在本文中,我们提出了一个很有前途的异构机器学习模型的两层集成,它集成了七个著名的机器学习分类器来预测来自大型藻类的 AMP。集合的第一层由一组二元分类器组成,这些分类器从蛋白质序列数据中识别 AMP,然后将其转发到第二层多类集合以表征其功能家族类型。两层集合成功地用于识别来自三个不同门群的 12 种大型藻类中的 39 个假定的 AMP 序列。我们描述的方法不仅限于 AMP,还可以应用于搜索其他类型蛋白质的序列数据。集合的第一层由一组二元分类器组成,这些分类器从蛋白质序列数据中识别 AMP,然后将其转发到第二层多类集合以表征其功能家族类型。两层集合成功地用于识别来自三个不同门群的 12 种大型藻类中的 39 个假定的 AMP 序列。我们描述的方法不仅限于 AMP,还可以应用于搜索其他类型蛋白质的序列数据。集合的第一层由一套二元分类器组成,这些分类器从蛋白质序列数据中识别 AMP,然后将其转发到第二层多类集合以表征其功能家族类型。两层集合成功地用于识别来自三个不同门群的 12 种大型藻类中的 39 个假定的 AMP 序列。我们描述的方法不仅限于 AMP,还可以应用于搜索其他类型蛋白质的序列数据。

更新日期:2021-05-12
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