Two Novel Anticancer Peptides from Aurein1.2

To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.

A frog-derived bionic peptide with discriminative inhibition of tumors based on integrin αvβ3 identification

Aureins, natural active peptides extracted from skin secretions of Australian bell frogs, have become a research focus due to the antitumor effects caused by lysing cell membranes. However, clinical translation of Aureins is still limited by non-selective toxicity between normal and cancer cells. Herein, by structure-activity relationship analysis and rational linker design, a dual-function fusion peptide RA3 is designed by tactically fusing Aurein peptide A1 with strong anticancer activity, with a tri-peptide with integrin αvβ3-binding ability which was screened in our previous work. Rational design and selection of fusion linkers ensures α-helical conformation and active functions of this novel fusion peptide, inducing effective membrane rupture and selective apoptosis of cancer cells. The integrin binding and tumor recognition ability of the fusion peptide is further validated by fluorescence imaging in cell and mouse models, in comparison with the non-selective A1 peptide. Meanwhile, increased stability and superior therapeutic efficacy are achieved in vivo for the RA3 fusion peptide. Our study highlights that aided by computational simulation technologies, the biomimetic fusion RA3 peptide has been successfully designed, surmounting the poor tumor-selectivity of the natural defensive peptide, serving as a promising therapeutic agent for cancer treatment.

Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides

Peptides are naturally produced by all organisms and exhibit a wide range of physiological, immunomodulatory, and wound healing functions. Furthermore, they can provide with protection against microorganisms and tumor cells. Their multifaceted performance, high selectivity, and reduced toxicity have positioned them as effective therapeutic agents, representing a positive economic impact for pharmaceutical companies. Currently, efforts have been made to invest in the development of new peptides with antimicrobial and anticancer properties, but the poor stability of these molecules in physiological environments has triggered a bottleneck. Therefore, some tools, such as nanotechnology and in silico approaches can be applied as alternatives to try to overcome these obstacles. In silico studies provide a priori knowledge that can lead to the development of new anticancer peptides with enhanced biological activity and improved stability. This review focuses on the current status of research in peptides with dual antimicrobial-anticancer activity, including advances in computational biology using in silico analyses as a powerful tool for the study and rational design of these types of peptides.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.

Host-defense peptides of Australian anurans. Part 2. Structure, activity, mechanism of action, and evolutionary significance

A previous review summarized research prior to 2004 carried out on the bioactive host-defense peptides contained in the skin secretions of Australian anurans (frogs and toads). This review covers the extension of that research from 2004 to 2012, and includes membrane-active peptides (including antibacterial, anticancer, antifungal and antiviral peptides) together with the mechanisms by which these peptides interact with model membranes, peptides that may be classified as "neuropeptides" (including smooth muscle active peptides, opioids and immunomodulators) and peptides which inhibit the formation of nitric oxide from neuronal nitric oxide synthase. The review discusses the outcome of cDNA sequencing of signal-spacer-active peptides from an evolutionary viewpoint, and also lists those peptides for which activities have not been found to this time.