School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330003, China.
School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330003, China.
Comput Biol Chem. 2022 Aug;99:107711. doi: 10.1016/j.compbiolchem.2022.107711. Epub 2022 Jun 1.
As one of the most terrible diseases, cancer causes millions of deaths worldwide every year. The popular treatment approaches, such as radiotherapy and chemotherapy, have been used in against cancer cells. However, those traditional therapies have side effects on normal cells, time-consuming and expensive. Recent studies showed that anticancer peptides (ACP) may be a potential choice instead of traditional approaches for treating cancer. Therefore, it is desired to develop a computational method to identify anticancer peptides. In this study, a support vector machine (SVM) based computational model was proposed to discriminate anticancer peptides from non-anticancer peptides. In the model, peptide sequences were firstly encoded by amino acids physicochemical (PC) properties and residue pairwise energy content matrix (RECM). Then, Pearson's correlation coefficient, high-order correlation information, and discrete wavelet transform were employed to extract useful information from PC and RECM matrix. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select discriminative features. Finally, these selected features were fed into SVM for distinguishing ACP from non-ACP. Experimental results demonstrated that the proposed method is powerful, it indicates that our proposed method may be a hopeful tool in discriminating anticancer peptides from non-anticancer peptides. The codes and datasets used in current work are available at https://figshare.com/articles/online_resource/iACP/16866232.
作为最可怕的疾病之一，癌症每年在全球导致数百万人死亡。放射疗法和化学疗法等流行的治疗方法已被用于对抗癌细胞。然而，这些传统疗法对正常细胞有副作用，既耗时又昂贵。最近的研究表明，抗癌肽 (ACP) 可能是治疗癌症的传统方法的替代选择。因此，人们希望开发一种计算方法来识别抗癌肽。在这项研究中，提出了一种基于支持向量机 (SVM) 的计算模型，用于区分抗癌肽和非抗癌肽。在该模型中，首先通过氨基酸理化 (PC) 性质和残基对能量含量矩阵 (RECM) 对肽序列进行编码。然后，使用 Pearson 相关系数、高阶相关信息和离散小波变换从 PC 和 RECM 矩阵中提取有用信息。应用最小绝对收缩和选择算子 (LASSO) 算法选择有区别的特征。最后，将这些精选特征输入 SVM 以区分 ACP 和非 ACP。实验结果表明，该方法非常有效，表明我们提出的方法可能是区分抗癌肽和非抗癌肽的有希望的工具。当前工作中使用的代码和数据集可在 https://figshare.com/articles/online_resource/iACP/16866232 上获得。
