Active Semisupervised Model for Improving the Identification of Anticancer Peptides

Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.

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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190122151634 ◽

2019 ◽

Vol 19 (1) ◽

pp. 4-16 ◽

Cited By ~ 6

Author(s):

Qihui Wu ◽

Hanzhong Ke ◽

Dongli Li ◽

Qi Wang ◽

Jiansong Fang ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Large Scale ◽

Recent Progress ◽

High Specificity ◽

Learning Approaches ◽

Anticancer Peptides ◽

The Past ◽

Traditional Approaches ◽

Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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Genomic data mining approaches for the discovery of anticancer peptides from Ganoderma sinense

Phytochemistry ◽

10.1016/j.phytochem.2020.112466 ◽

2020 ◽

Vol 179 ◽

pp. 112466

Author(s):

Sheng Zheng ◽

Ning Zhu ◽

Cheng Shi ◽

Heng Zheng

Keyword(s):

Data Mining ◽

Genomic Data ◽

Anticancer Peptides ◽

Genomic Data Mining ◽

Ganoderma Sinense

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Cationicity and hydrophobicity enhance the cytotoxic potency of Phoratoxin C anticancer peptide analogues against triple negative breast cancer cells

Current Bioactive Compounds ◽

10.2174/1573407217999210112180404 ◽

2021 ◽

Vol 17 ◽

Author(s):

Chu Xin Ng ◽

Cheng Foh Le ◽

Sau Har Lee

Keyword(s):

Cancer Cells ◽

Anticancer Agents ◽

Breast Cancer Cells ◽

Critical Role ◽

Three Dimensional ◽

Vero Cells ◽

Anticancer Peptides ◽

Α Helix ◽

Peptide Analogues

Background: Anticancer peptides (ACPs) have received increasing attention as a promising class of novel anticancer agents owing to its potent and rapid cytotoxic properties. In this study, we aim to investigate the effects of cationicity and hydrophobicity in modulating the cytotoxicity of PtxC, a class of ACP from the leafy mistletoe Phoradendron tomentosum against the MDA-MB-231 and Vero cells. Method: We designed a series of four PtxC analogues (PA1 – PA4) by residual substitutions with specific amino acids to introduce the specific charge and hydrophobicity alterations to the analogues. The cytotoxicity strength of the PtxC analogues on MDA-MB-231 and Vero cells were tested by using MTT assay at 24 hours post treatment. Results: PA1, PA2 and PA4 displayed marked increases in cytotoxicity against both MDA-MB-231 and Vero cells and can be ranked in the order of PA2 > PA4 > PA1 > PtxC > PA3. Sequence-activity relationship analyses of the designed analogues showed that an increase in the level of cationicity and hydrophobicity correlated well with the enhanced cytotoxic activity of PtxC analogues. This was observed with PA1 (netC +8) and PA2 (netC +10) in comparison to PtxC (netC +7). Similar finding was observed for PA4 (GRAVY +0.070) in contrast to PtxC (GRAVY -0.339). Three-dimensional modelling predicted a double α-helix structure in PtxC class of ACP. The larger first helix in PA2 and PA4 was suggested to be responsible for the enhanced cytotoxicity observed. Conclusion: The critical role of cationicity and hydrophobicity in enhancing cytotoxicity of PtxC class of ACPs were clearly demonstrated in our study. The current findings could be extrapolated to benefit peptide design strategy in other classes of ACPs toward the discovery of highly potent ACPs against cancer cells as potential novel therapeutic agents.

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Design and Modification of Anticancer Peptides

Drug Designing Open Access ◽

10.4172/2169-0138.1000138 ◽

2016 ◽

Vol 05 (03) ◽

Cited By ~ 6

Author(s):

Cuihua Hu ◽

Xiaolong Chen ◽

Wencai Zhao

Keyword(s):

Anticancer Peptides

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ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

10.1101/2021.11.22.469543 ◽

2021 ◽

Author(s):

Guanwen Feng ◽

Hang Yao ◽

Chaoneng Li ◽

Ruyi Liu ◽

Rungen Huang ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Network ◽

Protein Sequences ◽

Residue Level ◽

Ensemble Model ◽

Cancer Treatments ◽

Anticancer Peptides ◽

Peptide Level ◽

Independent Test ◽

Promising Perspective

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

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Front Cover: Designing Anticancer Peptides by Constructive Machine Learning (ChemMedChem 13/2018)

ChemMedChem ◽

10.1002/cmdc.201800415 ◽

2018 ◽

Vol 13 (13) ◽

pp. 1260-1260

Author(s):

Francesca Grisoni ◽

Claudia S. Neuhaus ◽

Gisela Gabernet ◽

Alex T. Müller ◽

Jan A. Hiss ◽

...

Keyword(s):

Machine Learning ◽

Front Cover ◽

Anticancer Peptides

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Computational Design of Biologically Active Anticancer Peptides and Their Interactions with Heterogeneous POPC/POPS Lipid Membranes

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.9b00348 ◽

2019 ◽

Vol 60 (1) ◽

pp. 332-341 ◽

Cited By ~ 2

Author(s):

Maninder Singh ◽

Vikash Kumar ◽

Kamakshi Sikka ◽

Ravi Thakur ◽

Munesh Kumar Harioudh ◽

...

Keyword(s):

Lipid Membranes ◽

Computational Design ◽

Biologically Active ◽

Anticancer Peptides

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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Molecules ◽

10.3390/molecules24101973 ◽

2019 ◽

Vol 24 (10) ◽

pp. 1973 ◽

Cited By ~ 32

Author(s):

Nalini Schaduangrat ◽

Chanin Nantasenamat ◽

Virapong Prachayasittikul ◽

Watshara Shoombuatong

Keyword(s):

Computational Models ◽

Helical Structure ◽

Support Vector ◽

Disulfide Bridges ◽

Cell Penetration ◽

Anticancer Activities ◽

New Class ◽

Anticancer Peptides ◽

Small Molecule Drugs

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

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CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides

Pharmaceutics ◽

10.3390/pharmaceutics12111045 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1045

Author(s):

Michał Burdukiewicz ◽

Katarzyna Sidorczuk ◽

Dominik Rafacz ◽

Filip Pietluch ◽

Mateusz Bąkała ◽

...

Keyword(s):

Antimicrobial Peptides ◽

Cancer Cells ◽

Plasma Membranes ◽

Therapeutic Potential ◽

Kappa Statistic ◽

R Package ◽

Bioactive Molecules ◽

Membrane Composition ◽

Anticancer Peptides ◽

Multicellular Organisms

Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from α-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.

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