Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.
Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.
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DOI：
10.1038/s41598-020-76161-8
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发表时间：
2020-11-06
影响因子：
4.6
通讯作者：
Webb-Robertson BM
中科院分区：
文献类型：
--
作者：
Chowdhury AS;Reehl SM;Kehn-Hall K;Bishop B;Webb-Robertson BM
由于缺乏有效的抗病毒疗法，世界各地病毒流行的出现令人担忧。新的抗病毒疗法的发现对于应对这一挑战至关重要，而抗病毒肽（AVP）是开发对抗病毒感染的新疗法的宝贵资源。我们提出了一种新的机器学习模型，利用源自氨基酸序列的理化和结构特性的最具信息性的特征来区分 AVP 和非 AVP。为了关注那些最有可能有助于抗病毒性能的特征，我们根据潜在特征对分类的重要性来过滤它们。这些特征选择分析表明二级结构是预测 AVP 最重要的肽序列特征。我们的基于特征的简化机器学习抗病毒肽预测 (FIRM-AVP) 方法比具有所有特征的模型或当前最先进的单一分类器具有更高的准确性。了解与 AVP 活动相关的功能是在新颖系统中识别和设计新 AVP 的核心需求。 FIRM-AVP 代码和独立软件包可在 https://github.com/pmartR/FIRM-AVP 上获取，附带的 Web 应用程序可在 https://msc-viz.emsl.pnnl.gov/AVPR 上获取。
The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.
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影响因子：
4.8
作者：
Gleenberg, IO;Avidan, O;Hizi, A
通讯作者：
Hizi, A
影响因子：
2.9
作者：
Louis, JM;Dyda, F;Davies, DR
通讯作者：
Davies, DR
影响因子：
5.8
作者：
Chou, KC
通讯作者：
Chou, KC
DOI：
10.3390/molecules16053499
发表时间：
2011-04-26
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Castel G;Chtéoui M;Heyd B;Tordo N
通讯作者：
Tordo N
DOI：
10.1073/pnas.92.19.8700
发表时间：
1995-09-12
影响因子：
11.1
作者：
DUBCHAK, I;MUCHNIK, I;KIM, SH
通讯作者：
KIM, SH