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Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method. [ABSTRACT FROM AUTHOR]

[Display omitted] Anticancer peptides (ACPs) are promising antitumor drugs owning to their great cancer cell targeting and anticancer effects as well as low drug resistance. However, many of the ACPs have non-specific toxicity and can be easily degraded by the enzymes after administration. Therefore, drug delivery systems (DDSs) are required to shield these peptides from degradation and induce targeted delivery. In this paper, a high performance microfluidic device was used to fabricate the zeolitic imidazolate framework (ZIF-8) encapsulating an ACP (At3) recently developed by our group. The microfluidic device allowed for efficient and rapid mixing to generate ACP loaded nanoparticles (NPs) with controllable properties at high production rate (120 mL/min) and high encapsulation efficiency. The ZIF-8 NPs synthesised by microfluidic processing showed lower polydispersity index (PDI) than the conventional method, demonstrating an improved size uniformity. Encapsulating At3 into the ZIF-8 (At3@ZIF-8) significantly reduced the hemolytic effect and provided a pH-controlled release of At3 peptide. At3@ZIF-8 showed higher anticancer effect than the unloaded peptide at the same concentration due to the enhanced cell uptake by the ZIF-8 NPs. The NPs were able to inhibit the growth of the multicellular tumour spheroids (MCTSs) and damage the mitochondrial membrane of the MCF-7 breast cancer cells. In vivo experiments demonstrated that the At3@ZIF-8 NPs inhibited the growth of MCF-7 tumours in nude mice without changing the biochemical properties of the blood or the histopathological properties of vital organs. Therefore, the development of At3 loaded NPs provides an alternative approach in ACP delivery which can broaden the application of ACP-based cancer therapy. [ABSTRACT FROM AUTHOR]

Cancer is one of the main diseases threatening human life, accounting for millions of deaths around the world each year. Traditional physical and chemical methods for cancer treatment are extremely time-consuming, lab-intensive, expensive, inefficient and difficult to be applied in a high-throughput way. Hence, it is an urgent task to develop automated computational methods to enable fast and accurate identification of anticancer peptides (ACPs). In this paper, we develop a novel model named iACP-GE to identify ACPs. Multi-features are extracted by using binary encoding, enhanced grouped amino acid composition and BLOSUM62 encoding based on the N5C5 sequence, as well as detrended forward moving-average auto-cross correlation analysis based on physicochemical properties of 20 natural amino acids. Thus, 835 features are obtained for each sample, in order to avoid information redundancy, gradient boosting decision tree was adopted as the feature selection strategy. Then, the optimal feature subset is input to the extra tree classifier. The accuracies of ACP740 and ACP240 datasets with the 5-fold cross-validation were 90.54% and 91.25%, respectively. Experimental results indicate that iACP-GE significantly outperforms several existing models on ACP740 and ACP240 datasets and can be used as an effective tool for the identification of ACPs. The datasets and source codes for iACP-GE are available at . [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-trained classifier model, ESM2-GRU, for ACP prediction to make it easier to predict ACPs, gain a better understanding of the structural and functional differences of anti-cancer peptides, and optimize the design for the development of more effective anti-cancer treatment strategies. The model is made up of the ESM2 pre-trained model, a bidirectional GRU recurrent neural network, and a fully connected layer. ACP sequences are first fed into the ESM2 model, which then expands the dimensions before feeding the findings back into the bidirectional GRU recurrent neural network. Finally, the fully connected layer generates the ultimate output. Experimental validation demonstrates that the ESM2-GRU model greatly improves classification performance on the benchmark dataset ACP606, with AUC, ACC, and MCC values of 0.975, 0.852, and 0.738, respectively. This exceptional prediction potential helps to identify specific types of anti-cancer peptides, improving their targeting and selectivity and, therefore, furthering the development of tailored medicine and treatments. [Display omitted] • The ACP predictor model ESM2-GRU consists of ESM2,a two-way GRU recurrent neural network and a fully connected layer. • ESM2-GRU helps understand ACP's structural and functional differences and develop anti-cancer therapeutic strategies. • ESM2-GRU improved the classification performance with AUC,ACC and MCC values of 0.975,0.852 and 0.738,respectively,on ACP606. • ESM2-GRU helps to identify specific anticancer peptides and facilitates the development of customized drugs and therapeutics. [ABSTRACT FROM AUTHOR]

Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP). In our proposed SAP, the peptide is represented by 400D features or 400D features with g-gap dipeptide features, and then the unrelated features are pruned using the maximum relevance-maximum distance method. The experimental results demonstrate that our model performs better than some existing methods. Furthermore, our model has also been extended to other classifiers, and the performance is stable compared with some state-of-the-art works. [ABSTRACT FROM AUTHOR]

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects. According to recent advancements, peptide-based therapies have attracted the attention of scientists because of their high selectivity. Peptide therapy can efficiently treat the targeted cells, without affecting the normal cells. Due to the rapid increase of peptide sequences, an accurate prediction model has become a challenging task. Keeping the significance of anticancer peptides (ACPs) in cancer treatment, an intelligent and reliable prediction model is highly indispensable. In this paper, a FastText-based word embedding strategy has been employed to represent each peptide sample via a skip-gram model. After extracting the peptide embedding descriptors, the deep neural network (DNN) model was applied to accurately discriminate the ACPs. The optimized parameters of DNN achieved an accuracy of 96.94 %, 93.41 %, and 94.02 % using training, alternate, and independent samples, respectively. It was observed that our proposed cACP-DeepGram model outperformed and reported ~10 % highest prediction accuracy than existing predictors. It is suggested that the cACP-DeepGram model will be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and the datasets are publicly available at https://github.com/shahidakbarcs/cACP-DeepGram. [ABSTRACT FROM AUTHOR]

World widely, cancer is considered a fatal disease and remains the major cause of death. Conventional medication approaches using therapies and anticancer drugs are deemed ineffective due to its high cost and harmful impacts on the normal cells. However, the innovation of anticancer peptides (ACPs) provides an effective way how to deals with cancer affected cells. Due to the rapid increases in peptide sequences, truly characterization of ACPs has become a challenging task for investigators. In this paper, an effort has been carried out to develop a reliable and intelligent computational method for the accurate discrimination of anticancer peptides. Three statistical feature representation schemes namely: Quasi-sequence order (QSO), conjoint triad feature, and Geary autocorrelation descriptor are applied to express motif of the target class. In order to eradicate irrelevant and noisy features, while select salient, profound and high variated features, principal component analysis is employed. Furthermore, the diverse nature of learning algorithms is utilized in order to select the best operational engine for the proposed model. After examining the empirical outcomes, support vector machine obtained quite encouraging results in combination with QSO feature space. It has achieved an accuracy of 96.91% and 89.54% using the main dataset and alternative dataset, respectively. It is observed that our proposed model shows an outstanding improvement compared to literature methods. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. • Computational model is developed for prediction of anticancer peptides. • Quasi-sequence order, conjoint triad feature, and Geary autocorrelation descriptor are used as feature extraction scheme. • PCA is applied as feature selection scheme. • Various classification algorithms are utilized. • Proposed model achieved quite promising results. [ABSTRACT FROM AUTHOR]