BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors
BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors
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DOI：
10.1021/acsomega.9b04119
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发表时间：
2020-04-07
期刊：
影响因子：
4.1
通讯作者：
Kavousi, Kaveh
中科院分区：
文献类型：
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作者：
Atanaki, Fereshteh Fallah;Behrouzi, Saman;Kavousi, Kaveh
生物膜是由微生物群落形成的生物系统，其中微生物细胞在细胞外聚合物自身产生的基质内的表面上连接。在某些情况下，微生物利用生物膜来保护自己免受宿主免疫系统和周围环境的有害影响，从而增加它们在各种抗微生物剂的作用下生存的机会。生物膜因其引起的问题而在医学和工业中发挥着至关重要的作用。设计抑制细菌生物膜形成的试剂非常昂贵，并且需要在实验室花费太多时间来发现和验证。因此，开发用于预测生物膜抑制肽的计算工具是必然且重要的。在这里，我们提出了一种计算预测工具来筛选大量的肽序列并选择潜在的候选肽以进行进一步的实验室实验和验证。在此学习模型中，利用从肽一级结构中提取的不同特征向量来学习生物膜抑制肽序列的模式。各种分类算法（包括 SVM、随机森林和 k-近邻）都经过了检验，以评估其性能。总体而言，与其他预测方法相比，我们的方法显示出更好的预测。在这项研究中，我们首次应用从氨基酸核磁共振谱中提取的特征以及理化特征。尽管每组特征单独表现出良好的区分潜力，但我们使用特征组合来增强我们方法的性能。我们的预测工具是免费提供的。
Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.