CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

Abstract

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP ().

A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification

“…In the feature generation process, we used iFeature (Chen et al, 2018) and Pfeature (Pande et al, 2019), two publicly available Python‐based toolkits that are capable of generating a comprehensive spectrum of descriptors, to facilitate the numerical representation of biological sequences for ML purposes. These sequence‐based and length‐independent features incorporate various properties of proteins such as the composition of atoms, bonds, amino acids, and dipeptides, as well as physico‐chemical properties, repeats, distribution, etc., and have been used in previous studies (Ariaeenejad et al, 2018; Nasiri et al, 2021). Many of the generated descriptors were duplicates and thus were removed.…”

“…Recently, data science revolutions have a great effect on conventional enzyme research and could speed up enzyme discovery procedure. ML may play a central role in the paradigm change away from using traditional methods in different applications such as peptide function prediction (Atanaki et al, 2020;Kavousi et al, 2020;Nasiri et al, 2021), enzyme characteristics prediction (Ariaeenejad et al, 2018(Ariaeenejad et al, , 2021Shahraki et al, 2019aShahraki et al, , 2020, and so on. In a review done by Toyao et al (Toyao et al, 2020), recent developments have been reported in the use of ML to build homogeneous and heterogeneous catalysts.…”

The role of cell‐penetrating peptides in potential anti‐cancer therapy

“…123 The safety, side effects and effectiveness of carrier peptides that target cancer cells directly and/or by activating immune responses have been widely confirmed in clinical trials. 124,125 Vaccine peptide…”

“…Compared with conventional chemotherapy drugs, ACPs show better performances in inhibiting the proliferation and migration of tumour cells and tumour blood vessels 123 . The safety, side effects and effectiveness of carrier peptides that target cancer cells directly and/or by activating immune responses have been widely confirmed in clinical trials 124,125 …”

Endocytosis Involved d-Oligopeptide of Tryptophan and Arginine Displays Ordered Nanostructures and Cancer Cell Stereoselective Toxicity by Autophagy

“…Owing to the failure of current chemotherapeutic mechanisms and their associated health risks, hopes were given up with regard to the treatment of resistant cancer cells . The amphipathic anticancer peptides (ACPs) that selectively target the cell survival signaling are potentially attractive for clinical use. − However, the hydrolytic instability of ACPs along with the altered composition of the cell membrane and defects in the apoptotic machinery may result in peptide ineffectiveness. , In this regard, the redox imbalance or impaired metabolic homeostasis induced by the in vitro self-assembling of ACPs and their accumulation in the endoplasmic reticulum (ER) or inside the mitochondria are considered suitable to treat the resistant cancer cells. ,, Alternatively, the caspase-independent necroptotic or autophagic deaths promoted by the ACP nanostructures via targeting the Golgi apparatus and ER membrane systems are privileged for the selective toxicity. − …”