1] Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India [2].
Sci Rep. 2013 Oct 18;3:2984. doi: 10.1038/srep02984.
Use of therapeutic peptides in cancer therapy has been receiving considerable attention in the recent years. Present study describes the development of computational models for predicting and discovering novel anticancer peptides. Preliminary analysis revealed that Cys, Gly, Ile, Lys, and Trp are dominated at various positions in anticancer peptides. Support vector machine models were developed using amino acid composition and binary profiles as input features on main dataset that contains experimentally validated anticancer peptides and random peptides derived from SwissProt database. In addition, models were developed on alternate dataset that contains antimicrobial peptides instead of random peptides. Binary profiles-based model achieved maximum accuracy 91.44% with MCC 0.83. We have developed a webserver, which would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides (http://crdd.osdd.net/raghava/anticp/).
近年来，治疗性肽在癌症治疗中的应用受到了相当多的关注。本研究描述了用于预测和发现新型抗癌肽的计算模型的开发。初步分析表明，Cys、Gly、Ile、Lys 和 Trp 在抗癌肽的各个位置占主导地位。支持向量机模型使用氨基酸组成和二进制谱作为输入特征，在包含实验验证的抗癌肽和从 SwissProt 数据库中衍生的随机肽的主要数据集上进行开发。此外，还在包含抗菌肽而不是随机肽的替代数据集上开发了模型。基于二进制谱的模型达到了最高的准确性 91.44%，MCC 为 0.83。我们开发了一个网络服务器，它将有助于：（i）预测提高抗癌效力所需的最小突变；（ii）通过虚拟筛选肽来发现新型抗癌肽，以及（iii）扫描天然蛋白质以鉴定抗癌肽（http://crdd.osdd.net/raghava/anticp/）。
