Wei Leyi, Xing PengWei, Su Ran, Shi Gaotao, Ma Zhanshan Sam, Zou Quan
School of Computer Science and Technology, Tianjin University , Tianjin 300072, China.
School of Software, Tianjin University , Tianjin 300354, China.
J Proteome Res. 2017 May 5;16(5):2044-2053. doi: 10.1021/acs.jproteome.7b00019. Epub 2017 Apr 26.
Cell-penetrating peptides (CPPs), have been proven as important drug-delivery vehicles, demonstrating the potential as therapeutic candidates. The past decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine-learning-based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and, for the first time, construct a two-layer prediction framework based on the random forest algorithm. The jackknife results on benchmark data sets show that the proposed CPPred-RF is at least competitive with the state-of-the-art predictors. Moreover, we establish the first online Web server in terms of predicting CPPs and their uptake efficiency simultaneously. It is freely available at http://server.malab.cn/CPPred-RF .
细胞穿透肽（CPPs）已被证明是重要的药物递送载体，显示出作为治疗候选物的潜力。在过去十年中，基于CPP的研究迅速发展。最近，人们进行了许多计算工作来开发基于机器学习的CPP识别方法。尽管已经取得了很大进展，但现有方法的特征表示能力仍然较低，这限制了性能的进一步提高。在本研究中，我们提出了一种名为CPPred-RF的新型预测器，其中我们整合了多个基于序列的特征描述符，以充分探索CPPs中嵌入的不同信息，采用成熟的特征选择技术来改善特征表示，并首次基于随机森林算法构建了两层预测框架。在基准数据集上的留一法结果表明，所提出的CPPred-RF至少与最先进的预测器具有竞争力。此外，我们建立了第一个能够同时预测CPPs及其摄取效率的在线网络服务器。它可在http://server.malab.cn/CPPred-RF上免费获得。
