ACP-check: An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy

Abstract

The anticancer peptide is an emerging anticancer drug that has become an effective alternative to chemotherapy and targeted therapy due to fewer side effects and resistance. The traditional biological experimental method for identifying anticancer peptides is a time-consuming and complicated process that hinders large-scale, rapid, and effective identification. In this paper, we propose a model based on a bidirectional long short-term memory network and multi-features fusion, called ACP-check, which employs a bidirectional long short-term memory network to extract time-dependent information features from peptide sequences, and combines them with amino acid sequence features including binary profile feature, dipeptide composition, the composition of k-spaced amino acid group pairs, amino acid composition, and sequence-order-coupling number. To verify the performance of the model, six benchmark datasets are selected, including ACPred-Fuse, ACPred-FL, ACP240, ACP740, main and alternate datasets of AntiCP2.0. In terms of Matthews correlation coefficients, ACP-check obtains 0.37, 0.82, 0.80, 0.75, 0.56, and 0.86 on six datasets respectively, which is an improvement by 2%–86% than existing state-of-the-art anticancer peptides prediction methods. Furthermore, ACP-check achieves prediction accuracy with 0.91, 0.91, 0.90, 0.87, 0.78, and 0.93 respectively, which increases range from 1%–49%. Overall, the comparison experiment shows that ACP-check can accurately identify anticancer peptides by sequence-level information. The code and data are available at http://www.cczubio.top/ACP-check/.