Sun Mingwei, Yang Sen, Hu Xuemei, Zhou You
Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
College of Computer Science and Technology, Tonghua Normal University, Tonghua 134000, China.
Molecules. 2022 Feb 24;27(5):1544. doi: 10.3390/molecules27051544.
Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.
癌症是对人类健康最危险的威胁之一。其中一个问题是耐药作用，这会导致药物治疗后出现副作用。许多疗法都致力于缓解耐药作用。最近，抗癌肽可能是一种新颖且有前景的抗癌候选物，它可以抑制肿瘤细胞增殖、迁移，并抑制肿瘤血管形成，且副作用较少。然而，通过高通量生物学实验鉴定抗癌肽成本高、费力且耗时。因此，准确鉴定抗癌肽成为抗癌肽治疗的关键且不可或缺的一步。尽管已经开发了一些现有的计算机方法来预测抗癌肽，但其准确性仍有待提高。因此，在本研究中，我们提出了一种基于深度学习的模型，称为ACPNet，用于区分抗癌肽和非抗癌肽（非ACPs）。ACPNet采用三种不同类型的肽序列信息、肽理化性质以及在训练过程中连接的自动编码特征。ACPNet是一种混合深度学习网络，它融合了全连接网络和循环神经网络。在ACPs82数据集上与其他现有方法的比较表明，ACPNet不仅在准确率上提高了1.2%，F1分数提高了2.0%，召回率提高了7.2%，而且在马修斯相关系数上也取得了平衡的性能。同时，ACPNet在一个独立数据集上得到验证，该数据集有20种已证实的抗癌肽，只有一种抗癌肽被预测为非ACPs。比较和独立验证实验表明，ACPNet可以准确区分抗癌肽和非ACPs。
