Prediction of cell penetrating peptides by support vector machines.
Prediction of cell penetrating peptides by support vector machines.
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DOI：
10.1371/journal.pcbi.1002101
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发表时间：
2011-07
影响因子：
4.3
通讯作者：
Willeford KO
中科院分区：
文献类型：
--
作者：
Sanders WS;Johnston CI;Bridges SM;Burgess SC;Willeford KO
细胞穿透肽是一类能穿过细胞膜进入细胞的多肽。一旦进入细胞，不同的CPP可以定位于不同的细胞成分并发挥不同的作用。一些产生孔形成复合物，导致细胞的破坏，而另一些定位于各种细胞器。使用机器学习方法来预测潜在的新CPP将能够更快速地筛选药物输送等应用。我们已经研究了训练数据集的组成对使用支持向量机（SVM）将肽分类为细胞穿透的能力的影响。我们从文献和商业供应商中确定了111种已知的CPP和34种已知的非穿透肽，并使用了几种方法来构建分类器的训练数据集。使用一组基本生物化学性质结合文献中确定与CPP预测相关的特征，从数据集中计算特征。我们使用不同训练数据集的结果证实了具有近似相等数量的正面和负面示例的平衡训练集的重要性。基于SVM的分类器具有比先前报道的用于预测CPP的方法更高的分类精度，并且由于它们使用肽的主要生化性质作为特征，这些分类器提供了对细胞穿透所需的性质的洞察。为了确认我们的SVM分类，选择分类为穿透性或非穿透性的肽的子集用于合成和实验验证。在预测为CPP的合成肽中，100%的这些肽显示为渗透性的。细胞穿透肽（CPP）是可以潜在地将其他功能分子转运穿过细胞膜并因此充当药物递送载体的肽。给定肽的使其穿透细胞的特性尚不清楚，并且快速筛选潜在的CPP有助于研究人员关注那些最有可能用于治疗能力的肽。本文表明，代表这些肽的主要生化特性的基本特征可以用于训练分类器，该分类器可以准确地预测肽的细胞渗透潜力，并提供与细胞渗透相关的生化特性的见解。
Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating. Cell penetrating peptides (CPPs) are peptides that can potentially transport other functional molecules across cellular membranes and therefore serve a role as drug delivery vehicles. The properties of a given peptide that make it cell penetrating are unclear, and the rapid screening of potential CPPs aids researchers by allowing focus on those peptides most likely to be utilized in a therapeutic capacity. This paper shows that basic features representing primary biochemical properties of these peptides can be used to train a classifier that can accurately predict cell penetrating potential of peptides and provide insight into the biochemical properties associated with cell penetration.
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