Cancer is currently ineffectively treated using therapeutic drugs, and is also able to resist drug action, resulting in increased side effects following drug treatment. A novel therapeutic strategy against cancer cells is the use of anticancer peptides (ACPs). The physicochemical properties, amino acid composition and the addition of chemical groups on the ACP sequence influences their conformation, net charge and orientation of the secondary structure, leading to an effect on targeting specificity and ACP-cell interaction, as well as peptide penetrating capability, stability and efficacy. ACPs have been developed from both naturally occurring and modified peptides by substituting neutral or anionic amino acid residues with cationic amino acid residues, or by adding a chemical group. The modified peptides lead to an increase in the effectiveness of cancer therapy. Due to this effectiveness, ACPs have recently been improved to form drugs and vaccines, which have sequentially been evaluated in various phases of clinical trials. The development of the ACPs remains focused on generating newly modified ACPs for clinical application in order to decrease the incidence of new cancer cases and decrease the mortality rate. The present review could further facilitate the design of ACPs and increase efficacious ACP therapy in the near future.

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The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.