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Systematic analysis of global features and model building for recognition of antimicrobial peptides

机译：识别抗菌肽的全局特征和模型构建的系统分析

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With growing bacterial resistance to antibiotics, it is becoming paramount to seek out new antibacterials. Antimicrobial peptides (AMPs) provide interesting templates for antibacterial drug research. Our understanding of what it is that confers to these peptides their antimicrobial activity is currently poor. Yet, such understanding is the first step towards modification or design of novel AMPs for treatment. Research in machine learning is beginning to focus on recognition of AMPs from non-AMPs as a means of understanding what features confer to an AMP its activity. Methods either seek new features and test them in the context of classification or measure the classification power of features provided by biologists. In this paper, we provide a rigorous evaluation of features provided by a biologist or resulting from a combination of experimental and computational research. We present a statistics-based approach to carefully measure the significance of each feature and use this knowledge to construct predictive models. We present here logistic regression models, which are capable of associating probabilities on whether a peptide is antimicrobial or not with the feature values of the peptide. We provide access to the proposed methodology through a web server. The server allows users to replicate the findings in this paper or evaluate their own features.We believe research in this direction will allow the community to make further progress and elucidate features that capture antimicrobial activity. This is an important first step towards assisting modification and/or de novo design of AMPs in the wet laboratory.
机译：随着细菌对抗生素的耐药性不断提高，寻找新的抗菌剂变得至关重要。抗菌肽（AMPs）为抗菌药物研究提供了有趣的模板。我们对赋予这些肽什么是抗菌活性的理解目前很差。然而，这种理解是朝着用于治疗的新型AMP的修饰或设计的第一步。机器学习的研究开始侧重于从非AMP识别AMP，以此来理解赋予AMP活动的特征。方法要么寻找新特征并在分类的上下文中对其进行测试，要么测量生物学家提供的特征的分类能力。在本文中，我们对生物学家提供的功能或通过实验和计算研究相结合的功能进行了严格的评估。我们提出了一种基于统计的方法来仔细衡量每个功能的重要性，并使用此知识来构建预测模型。我们在这里提出逻辑回归模型，该模型能够将某肽是否具有抗菌性的概率与该肽的特征值相关联。我们通过网络服务器提供对建议方法的访问。服务器允许用户复制本文中的发现或评估他们自己的功能。我们相信，朝着这个方向进行的研究将使社区取得更大的进步，并阐明捕获抗菌活性的功能。这是在湿实验室中协助AMP的修改和/或从头设计的重要的第一步。

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