Seixas Feio Juliana Auzier, de Oliveira Ewerton Cristhian Lima, de Sales Claudomiro de Souza, da Costa Kauê Santana, E Lima Anderson Henrique Lima
Laboratório de Inteligência Computacional e Pesquisa Operacional, Campus Belém, Instituto de Tecnologia, Universidade Federal do Pará, Pará, Brazil.
Instituto Tecnológico Vale, Belém, Pará, Brazil.
PLoS One. 2024 Jun 13;19(6):e0305253. doi: 10.1371/journal.pone.0305253. eCollection 2024.
Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.
细胞穿透肽是一类能够自然穿过保护细胞的脂质双分子层膜的分子，它们具有共同的物理化学和结构特性，并具有多种药物应用，特别是在药物递送方面。对分子描述符的研究不仅提高了分类器的性能，还降低了计算复杂度，并增强了对膜通透性的理解。此外，采用新技术，如使用过拟合处理构建深度学习模型，在解决这个问题上具有优势。在本研究中，使用由经过过拟合处理的改进卷积神经网络和调整了超参数的XGBoost模型组成的ConvBoost-CPP，研究了艾森伯格标度上的氮、氧和疏水性描述符。结果表明使用ConvBoost-CPP是有利的，将氮、氧和疏水性与此前在该研究领域中研究过的其他十个描述符一起作为输入，交叉验证中的准确率从88%提高到91.2%，独立测试中的准确率从82.6%提高到91.3%。
