Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

中文翻译：

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DeepCPPred：用于区分细胞穿透肽及其摄取效率的深度学习框架

细胞穿透肽 (CPPs) 是一种特殊的肽，能够将遗传物质、短干扰 RNA 和纳米颗粒等多种生物活性分子携带到细胞中。最近，对 CPP 的研究引起了研究人员的极大兴趣，并且已经在安全的药物递送剂和治疗应用的背景下评估了 CPPS 的生物学机制。使用传统生化方法正确识别和合成 CPP 是一项极其缓慢、昂贵和费力的任务，特别是由于世界银行存储库中积累了大量未注释的肽序列。因此，迫切需要一种强大的生物信息学预测器，以快速识别具有高识别率的 CPP。迄今为止，已经开发了许多用于 CPP 预测的计算方法。然而，可用的机器学习 (ML) 工具无法区分 CPP 及其吸收效率。本研究旨在开发一个名为 DeepCPPred 的双层深度学习框架，以识别第一阶段的 CPP 和第二阶段的肽摄取效率。DeepCPPred 预测器首先使用四种类型的描述符，包括进化、能量估计、简化序列和氨基酸接触信息。然后，提取的特征通过弹性网络算法进行优化，并输入级联深度森林算法，构建最终的 CPP 模型。所提出的方法在第一层使用 CPP924 基准数据集实现了 99.45% 的总体准确度，使用 5 折交叉验证测试在第二层使用 CPPSite3 数据集实现了 95.43% 的准确度。因此，