AntiCP 2.0: an updated model for predicting anticancer peptides

Agrawal, Piyush; Bhagat, Dhruv; Mahalwal, Manish; Sharma, Neelam; Raghava, Gajendra P. S.

doi:10.1093/bib/bbaa153

Cited by 148 publications

(87 citation statements)

References 54 publications

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“…To verify the effectiveness of our proposed method, we compared our method ACP-DA with ACP-DL ( Yi et al, 2019 ), AntiCP 2.0 ( Agrawal et al, 2020 ), and DeepACP ( Yu et al, 2020 ). The results on ACP740 and ACP240 are shown in Figure 5 .…”

Section: Resultsmentioning

confidence: 99%

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ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

et al. 2021

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Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

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Section: Resultsmentioning

confidence: 99%

“… Yi et al, 2019 proposed a deep learning long short-term memory (LSTM) neural network model called ACP-DL to predict novel ACPs. Agrawal et al, 2020 used various features and different machine learning classifiers on two datasets for the prediction of ACPs.…”

Section: Introductionmentioning

confidence: 99%

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

et al. 2021

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“…To prioritize the peptides for experimental screening and validation, we hypothesized that the consensus results from the physicochemical property (positive charge), secondary peptide structure (alpha helix) and ACP-predicted results from different machine learning models would provide the best chance for the identification of ACP from the peptide library. This hypothesis was grounded on previous evidence as following: (i) the net positively charged peptides are attracted to the net negatively charged cancer cell membranes [45]; (ii) most known oncolytic peptides share an alpha helical structure [42]; (iii) different machine learning models were trained and tested upon various datasets of known ACPs [33][34][35][36], so these models have varied predictive performance against the new unknown peptide dataset. More positive predictions would provide more confidence in the predicted candidates.…”

Section: Discussionmentioning

confidence: 99%

“…study, including ACPpred-FL [33], antiCP 2.0 [34], MLACP [35], and mACPpred [36] (Fig-ure 3c and Table S2). From the data shown in Table S2, eight human milk peptides were selected as having ACP potential, based on net charge, secondary structure, and predicted anticancer property, and used for further analysis.…”

Section: Peptide Identification Library Construction and In Silico Anticancer Peptide Screeningmentioning

confidence: 99%

“…Thirdly, the identified peptides were predicted for ACPs using machine learning prediction software. Since ACP prediction software can provide different results depending on algorithms and training datasets, four kinds of online-accessible software were applied in this study, including ACPpred-FL [33], antiCP 2.0 [34], MLACP [35], and mACPpred [36] (Figure 3c and Table S2). From the data shown in Table S2, eight human milk peptides were selected as having ACP potential, based on net charge, secondary structure, and predicted anticancer property, and used for further analysis.…”

Section: Peptide Identification Library Construction and In Silico Anticancer Peptide Screeningmentioning

confidence: 99%

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HMP-S7 Is a Novel Anti-Leukemic Peptide Discovered from Human Milk

et al. 2021

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Chemotherapy in childhood leukemia is associated with late morbidity in leukemic survivors, while certain patient subsets are relatively resistant to standard chemotherapy. It is therefore important to identify new agents with sensitivity and selectivity towards leukemic cells, while having less systemic toxicity. Peptide-based therapeutics has gained a great deal of attention during the last few years. Here, we used an integrative workflow combining mass spectrometric peptide library construction, in silico anticancer peptide screening, and in vitro leukemic cell studies to discover a novel anti-leukemic peptide having 3+ charges and an alpha helical structure, namely HMP-S7, from human breast milk. HMP-S7 showed cytotoxic activity against four distinct leukemic cell lines in a dose-dependent manner but had no effect on solid malignancies or representative normal cells. HMP-S7 induced leukemic cell death by penetrating the plasma membrane to enter the cytoplasm and cause the leakage of lactate dehydrogenase, thus acting in a membranolytic manner. Importantly, HMP-S7 exhibited anti-leukemic effects against patient-derived leukemic cells ex vivo. In conclusion, HMP-S7 is a selective anti-leukemic peptide with promise, which requires further validation in preclinical and clinical studies.

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Efficient Mining of Anticancer Peptides from Gut Metagenome

Liu

Zhang

et al. 2023

Advanced Science

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The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well‐established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi‐center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types.

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AntiCP 2.0: an updated model for predicting anticancer peptides

Cited by 148 publications

References 54 publications

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

HMP-S7 Is a Novel Anti-Leukemic Peptide Discovered from Human Milk

Efficient Mining of Anticancer Peptides from Gut Metagenome

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