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Arif M, Musleh S, Fida H, Alam T. PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation. Sci Rep 2024; 14:16992. [PMID: 39043738 PMCID: PMC11266708 DOI: 10.1038/s41598-024-67433-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 07/11/2024] [Indexed: 07/25/2024] Open
Abstract
Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.
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Affiliation(s)
- Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Saleh Musleh
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Huma Fida
- Department of Microbiology, Abdul Wali Khan University, Mardan, KPK, Pakistan
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
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2
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Rathore AS, Choudhury S, Arora A, Tijare P, Raghava GPS. ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Comput Biol Med 2024; 179:108926. [PMID: 39038391 DOI: 10.1016/j.compbiomed.2024.108926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 05/17/2024] [Accepted: 07/17/2024] [Indexed: 07/24/2024]
Abstract
Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).
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Affiliation(s)
- Anand Singh Rathore
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Purva Tijare
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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3
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Cheong HH, Zuo W, Chen J, Un CW, Si YW, Wong KH, Kwok HF, Siu SWI. Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations. J Chem Inf Model 2024. [PMID: 39008832 DOI: 10.1021/acs.jcim.4c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.
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Affiliation(s)
- Hong-Hin Cheong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Weimin Zuo
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Jiarui Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Chon-Wai Un
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Yain-Whar Si
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Koon Ho Wong
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Hang Fai Kwok
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Shirley W I Siu
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macau SAR 999078, China
- Institute of Science and Environment, University of Saint Joseph, Estrada Marginal da Ilha Verde 14-17, Macau SAR 999078, China
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Bhattarai S, Tayara H, Chong KT. Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models. J Chem Inf Model 2024; 64:4941-4957. [PMID: 38874445 DOI: 10.1021/acs.jcim.4c00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.
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Affiliation(s)
- Sadik Bhattarai
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
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Kuzderová G, Sovová S, Rendošová M, Gyepes R, Sabolová D, Kožárová I, Balážová Ľ, Vilková M, Kello M, Liška A, Vargová Z. Influence of proline and hydroxyproline as antimicrobial and anticancer peptide components on the silver(I) ion activity: structural and biological evaluation with a new theoretical and experimental SAR approach. Dalton Trans 2024; 53:10834-10850. [PMID: 38661536 DOI: 10.1039/d4dt00389f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Silver(I) complexes with proline and hydroxyproline were synthesized and structurally characterized and crystal structure analysis shows that the formulas of the compounds are {[Ag2(Pro)2(NO3)]NO3}n (AgPro) (Pro = L-proline) and {[Ag2(Hyp)2(NO3)]NO3}n (AgHyp) (Hyp = trans-4-hydroxy-L-proline). Both complexes crystallize in the monoclinic lattice with space group P21 with a carboxylate bidentate-bridging coordination mode of the organic ligands Pro and Hyp (with NH2+ and COO- groups in zwitterionic form). Both complexes have a distorted seesaw (C2v) geometry around one silver(I) ion with τ4 values of 58% (AgPro) and 51% (AgHyp). Moreover, the results of spectral and thermal analyses correlate with the structural ones. 1H and 13C NMR spectra confirm the complexes species' presence in the DMSO biological testing medium and their stability in the time range of the bioassays. In addition, molar conductivity measurements indicate complexes' behaviour like 1 : 1 electrolytes. Both complexes showed higher or the same antibacterial activity against Bacillus cereus, Pseudomonas aeruginosa and Staphylococcus aureus as AgNO3 (MIC = 0.063 mM) and higher than silver(I) sulfadiazine (AgSD) (MIC > 0.5 mM) against Pseudomonas aeruginosa. In addition, complex AgPro exerted a strong cytotoxic effect against the tested MDA-MB-231 and Jurkat cancer cell lines (IC50 values equal to 3.7 and 3.0 μM, respectively) compared with AgNO3 (IC50 = 6.1 (5.7) μM) and even significantly higher selectivity than cisplatin (cisPt) against MDA-MB-231 cancer cell lines (SI = 3.05 (AgPro); 1.16 (cisPt), SI - selectivity index). The binding constants and the number of binding sites (n) of AgPro and AgHyp complexes with bovine serum albumin (BSA) were determined at four different temperatures, and the zeta potential of BSA in the presence of silver(I) complexes was also measured. The in ovo method shows the safety of the topical and intravenous application of AgPro and AgHyp. Moreover, the complexes' bioavailability was verified by lipophilicity evaluation from the experimental and theoretical points of view.
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Affiliation(s)
- Gabriela Kuzderová
- Department of Inorganic Chemistry, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic.
| | - Simona Sovová
- Department of Biochemistry, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic
- Department of Food Hygiene, Technology and Safety, University of Veterinary Medicine and Pharmacy, Komenského 73, 041 81 Košice, Slovak Republic
| | - Michaela Rendošová
- Department of Inorganic Chemistry, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic.
| | - Róbert Gyepes
- Department of Inorganic Chemistry, Faculty of Science, Charles University, Hlavova 2030, 128 00 Prague, Czech Republic
| | - Danica Sabolová
- Department of Biochemistry, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic
| | - Ivona Kožárová
- Department of Food Hygiene, Technology and Safety, University of Veterinary Medicine and Pharmacy, Komenského 73, 041 81 Košice, Slovak Republic
| | - Ľudmila Balážová
- Department of Pharmaceutical Technology, Pharmacognosy and Botany, University of Veterinary Medicine and Pharmacy, Komenského 73, 041 81 Košice, Slovak Republic
| | - Mária Vilková
- NMR laboratory, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic
| | - Martin Kello
- Department of Pharmacology, Faculty of Medicine, P.J.Šafárik University, Trieda SNP 1, 040 11 Košice, Slovak Republic
| | - Alan Liška
- Department of Molecular Electrochemistry and Catalysis, J. Heyrovský Institute of Physical Chemistry of the CAS, Dolejškova 3/2155, 182 23 Praha 8, Czech Republic
| | - Zuzana Vargová
- Department of Inorganic Chemistry, Faculty of Science, P.J.Šafárik University, Moyzesova 11, 041 54 Košice, Slovak Republic.
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Patnaik SK, Ayyamperumal S, Jade D, Palathoti N, Akey KS, Jupudi S, Harrison MA, Ponnambalam S, Mj N, Mjn C. Virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential inhibitors for cancer chemotherapy. J Biomol Struct Dyn 2024; 42:5551-5574. [PMID: 37387589 DOI: 10.1080/07391102.2023.2226744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 06/13/2023] [Indexed: 07/01/2023]
Abstract
Human epidermal growth factor receptors (EGFR), namely ErbB1/HER1, ErbB2/HER2/neu, ErbB3/HER3, and ErbB4/HER4, the trans-membrane family of tyrosine kinase receptors, are overexpressed in many types of cancers. These receptors play an important role in cell proliferation, differentiation, invasion, metastasis and angiogenesis including unregulated activation of cancer cells. Overexpression of ErbB1 and ErbB2 that occurs in several types of cancers is associated with poor prognosis leading to resistance to ErbB1-directed therapies. In this connection, promising strategy to overcome the disadvantages of the existing chemotherapeutic drugs is the use of short peptides as anticancer agents. In the present study, we have performed virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential dual inhibitors and identified five inhibitors based on their binding affinities, ADMET analysis, MD simulation studies and calculation of free energy of binding. These natural peptides could be further exploited for developing drugs for treating cancer.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sunil Kumar Patnaik
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Selvaraj Ayyamperumal
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Dhananjay Jade
- School of Biomedical Sciences, University of Leeds, Leeds, UK
| | - Nagarjuna Palathoti
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Srikanth Jupudi
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | | | | | - Nanjan Mj
- JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Chandrasekar Mjn
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
- School of Life Sciences, JSS Academy of Higher Education & Research(Ooty Campus), Ooty, Tamil Nadu, India
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7
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Zhang L, Hu X, Xiao K, Kong L. Effective identification and differential analysis of anticancer peptides. Biosystems 2024; 241:105246. [PMID: 38848816 DOI: 10.1016/j.biosystems.2024.105246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/27/2024] [Accepted: 06/04/2024] [Indexed: 06/09/2024]
Abstract
Anticancer peptides (ACPs) have recently emerged as promising cancer therapeutics due to their selectivity and lower toxicity. However, the number of experimentally validated ACPs is limited, and identifying ACPs from large-scale sequence data is time-consuming and expensive. Therefore, it is critical to develop and improve upon existing computational models for identifying ACPs. In this study, a computational method named ACP_DA was proposed based on peptide residue composition and physiochemical properties information. To curtail overfitting and reduce computational costs, a sequential forward selection method was utilized to construct the optimal feature groups. Subsequently, the feature vectors were fed into light gradient boosting machine classifier for model construction. It was observed by an independent set test that ACP_DA achieved the highest Matthew's correlation coefficient of 0.63 and accuracy of 0.8129, displaying at least a 2% enhancement compared to state-of-the-art methods. The satisfactory results demonstrate the effectiveness of ACP_DA as a powerful tool for identifying ACPs, with the potential to significantly contribute to the development and optimization of promising therapies. The data and resource codes are available at https://github.com/Zlclab/ACP_DA.
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Affiliation(s)
- Lichao Zhang
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, PR China; Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Qinhuangdao, PR China
| | - Xueli Hu
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, PR China
| | - Kang Xiao
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, PR China
| | - Liang Kong
- Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Qinhuangdao, PR China; School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, PR China.
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Jain S, Gupta S, Patiyal S, Raghava GPS. THPdb2: compilation of FDA approved therapeutic peptides and proteins. Drug Discov Today 2024; 29:104047. [PMID: 38830503 DOI: 10.1016/j.drudis.2024.104047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/05/2024]
Abstract
During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.
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Affiliation(s)
- Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Srijanee Gupta
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Sumeet Patiyal
- Cancer and Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India.
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Rodríguez Longarela N, Paredes Ramos M, López Vilariño JM. Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives. Crit Rev Food Sci Nutr 2024:1-20. [PMID: 38907628 DOI: 10.1080/10408398.2024.2367571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.
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Kao HJ, Weng TH, Chen CH, Chen YC, Chi YH, Huang KY, Weng SL. Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells. Int J Mol Sci 2024; 25:6848. [PMID: 38999958 PMCID: PMC11240926 DOI: 10.3390/ijms25136848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024] Open
Abstract
Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.
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Affiliation(s)
- Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Tzu-Han Weng
- Department of Dermatology, MacKay Memorial Hospital, Taipei City 104, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Yu-Chi Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Yu-Hsiang Chi
- National Center for High-Performance Computing, Hsinchu City 300, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei City 252, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
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Cui C, Huo Q, Xiong X, Na S, Mitsuda M, Minami K, Li B, Yokota H. P18: Novel Anticancer Peptide from Induced Tumor-Suppressing Cells Targeting Breast Cancer and Bone Metastasis. Cancers (Basel) 2024; 16:2230. [PMID: 38927935 PMCID: PMC11202002 DOI: 10.3390/cancers16122230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND The skeletal system is a common site for metastasis from breast cancer. In our prior work, we developed induced tumor-suppressing cells (iTSCs) capable of secreting a set of tumor-suppressing proteins. In this study, we examined the possibility of identifying anticancer peptides (ACPs) from trypsin-digested protein fragments derived from iTSC proteomes. METHODS The efficacy of ACPs was examined using an MTT-based cell viability assay, a Scratch-based motility assay, an EdU-based proliferation assay, and a transwell invasion assay. To evaluate the mechanism of inhibitory action, a fluorescence resonance energy transfer (FRET)-based GTPase activity assay and a molecular docking analysis were conducted. The efficacy of ACPs was also tested using an ex vivo cancer tissue assay and a bone microenvironment assay. RESULTS Among the 12 ACP candidates, P18 (TDYMVGSYGPR) demonstrated the most effective anticancer activity. P18 was derived from Arhgdia, a Rho GDP dissociation inhibitor alpha, and exhibited inhibitory effects on the viability, migration, and invasion of breast cancer cells. It also hindered the GTPase activity of RhoA and Cdc42 and downregulated the expression of oncoproteins such as Snail and Src. The inhibitory impact of P18 was additive when it was combined with chemotherapeutic drugs such as Cisplatin and Taxol in both breast cancer cells and patient-derived tissues. P18 had no inhibitory effect on mesenchymal stem cells but suppressed the maturation of RANKL-stimulated osteoclasts and mitigated the bone loss associated with breast cancer. Furthermore, the P18 analog modified by N-terminal acetylation and C-terminal amidation (Ac-P18-NH2) exhibited stronger tumor-suppressor effects. CONCLUSIONS This study introduced a unique methodology for selecting an effective ACP from the iTSC secretome. P18 holds promise for the treatment of breast cancer and the prevention of bone destruction by regulating GTPase signaling.
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Affiliation(s)
- Changpeng Cui
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA;
| | - Qingji Huo
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA;
| | - Xue Xiong
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA;
| | - Sungsoo Na
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA;
| | - Masaru Mitsuda
- Frontier Research Institute, Chubu University, Aichi 487-8501, Japan;
| | - Kazumasa Minami
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan;
| | - Baiyan Li
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.)
| | - Hiroki Yokota
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA;
- Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN 46202, USA
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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12
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Iglesias V, Bárcenas O, Pintado-Grima C, Burdukiewicz M, Ventura S. Structural information in therapeutic peptides: Emerging applications in biomedicine. FEBS Open Bio 2024. [PMID: 38877295 DOI: 10.1002/2211-5463.13847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024] Open
Abstract
Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.
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Affiliation(s)
- Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Institute of Advanced Chemistry of Catalonia (IQAC), CSIC, Barcelona, Spain
| | - Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
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13
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Ghafoor H, Asim MN, Ibrahim MA, Ahmed S, Dengel A. CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder. Comput Biol Med 2024; 176:108538. [PMID: 38759585 DOI: 10.1016/j.compbiomed.2024.108538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/19/2024]
Abstract
Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.
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Affiliation(s)
- Hina Ghafoor
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany.
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
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14
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Song H, Lin X, Zhang H, Yin H. ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier. Comput Biol Chem 2024; 110:108091. [PMID: 38735271 DOI: 10.1016/j.compbiolchem.2024.108091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/07/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024]
Abstract
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.
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Affiliation(s)
- Huijia Song
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Xiaozhu Lin
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
| | - Huainian Zhang
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Huijuan Yin
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
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15
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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024; 24:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
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Affiliation(s)
- Luis Pedro Coelho
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Laboratory of Microbial Processes & Biodiversity - LMPB, Hydrobiology Department, Federal University of São Carlos - UFSCar, São Paulo, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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16
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Chen Z, Wang R, Guo J, Wang X. The role and future prospects of artificial intelligence algorithms in peptide drug development. Biomed Pharmacother 2024; 175:116709. [PMID: 38713945 DOI: 10.1016/j.biopha.2024.116709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024] Open
Abstract
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.
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Affiliation(s)
- Zhiheng Chen
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Ruoxi Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Junqi Guo
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Xiaogang Wang
- Guangdong Provincial Key Laboratory of Bone and Joint Degenerative Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
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17
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Sun X, Liu Y, Ma T, Zhu N, Lao X, Zheng H. DCTPep, the data of cancer therapy peptides. Sci Data 2024; 11:541. [PMID: 38796630 PMCID: PMC11128002 DOI: 10.1038/s41597-024-03388-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.
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Affiliation(s)
- Xin Sun
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Yanchao Liu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Tianyue Ma
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Ning Zhu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Xingzhen Lao
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
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18
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Tan X, Liu Q, Fang Y, Yang S, Chen F, Wang J, Ouyang D, Dong J, Zeng W. Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs. Brief Bioinform 2024; 25:bbae350. [PMID: 39038937 PMCID: PMC11262833 DOI: 10.1093/bib/bbae350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 06/05/2024] [Accepted: 07/05/2024] [Indexed: 07/24/2024] Open
Abstract
Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.
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Affiliation(s)
- Xiaorong Tan
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Qianhui Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Yanpeng Fang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Sen Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Fei Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, 214, Veritas A Hall, Yonsei Univeristy, 85 Songdogwahak-ro, Incheon 21983, Republic of Korea
| | - Defang Ouyang
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
| | - Wenbin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China
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19
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Damare R, Engle K, Kumar G. Targeting epidermal growth factor receptor and its downstream signaling pathways by natural products: A mechanistic insight. Phytother Res 2024; 38:2406-2447. [PMID: 38433568 DOI: 10.1002/ptr.8166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/30/2024] [Accepted: 02/03/2024] [Indexed: 03/05/2024]
Abstract
The epidermal growth factor receptor (EGFR) is a transmembrane receptor tyrosine kinase (RTK) that maintains normal tissues and cell signaling pathways. EGFR is overactivated and overexpressed in many malignancies, including breast, lung, pancreatic, and kidney. Further, the EGFR gene mutations and protein overexpression activate downstream signaling pathways in cancerous cells, stimulating the growth, survival, resistance to apoptosis, and progression of tumors. Anti-EGFR therapy is the potential approach for treating malignancies and has demonstrated clinical success in treating specific cancers. The recent report suggests most of the clinically used EGFR tyrosine kinase inhibitors developed resistance to the cancer cells. This perspective provides a brief overview of EGFR and its implications in cancer. We have summarized natural products-derived anticancer compounds with the mechanistic basis of tumor inhibition via the EGFR pathway. We propose that developing natural lead molecules into new anticancer agents has a bright future after clinical investigation.
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Affiliation(s)
- Rutuja Damare
- Department of Natural Products, Chemical Sciences, National Institute of Pharmaceutical Education and Research-Hyderabad, Hyderabad, India
| | - Kritika Engle
- Department of Natural Products, Chemical Sciences, National Institute of Pharmaceutical Education and Research-Hyderabad, Hyderabad, India
| | - Gautam Kumar
- Department of Natural Products, Chemical Sciences, National Institute of Pharmaceutical Education and Research-Hyderabad, Hyderabad, India
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20
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Xu X, Li C, Yuan X, Zhang Q, Liu Y, Zhu Y, Chen T. ACP-DRL: an anticancer peptides recognition method based on deep representation learning. Front Genet 2024; 15:1376486. [PMID: 38655048 PMCID: PMC11035771 DOI: 10.3389/fgene.2024.1376486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.
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Affiliation(s)
- Xiaofang Xu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Chaoran Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Xinpu Yuan
- Department of General Surgery, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qiangjian Zhang
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei, China
| | - Yi Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Yunping Zhu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Tao Chen
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China
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21
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Azad H, Akbar MY, Sarfraz J, Haider W, Riaz MN, Ali GM, Ghazanfar S. G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer. J Biomol Struct Dyn 2024:1-14. [PMID: 38450672 DOI: 10.1080/07391102.2024.2323141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/15/2024] [Indexed: 03/08/2024]
Abstract
Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Humera Azad
- Department of Biosciences (Bioinformatics) Islamabad, Comsats University Islamabad, Pakistan
| | - Muhammad Yasir Akbar
- National Institute for Genomics and Advanced Biotechnology (NIGAB), National Agricultural Research Center (NARC), Pakistan
| | | | - Waseem Haider
- Department of Biosciences (Bioinformatics) Islamabad, Comsats University Islamabad, Pakistan
| | - Muhammad Naeem Riaz
- National Institute for Genomics and Advanced Biotechnology (NIGAB), National Agricultural Research Center (NARC), Pakistan
| | - Ghulam Muhammad Ali
- Department of Biosciences (Bioinformatics) Islamabad, Comsats University Islamabad, Pakistan
| | - Shakira Ghazanfar
- National Institute for Genomics and Advanced Biotechnology (NIGAB), National Agricultural Research Center (NARC), Pakistan
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22
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Bian J, Liu X, Dong G, Hou C, Huang S, Zhang D. ACP-ML: A sequence-based method for anticancer peptide prediction. Comput Biol Med 2024; 170:108063. [PMID: 38301519 DOI: 10.1016/j.compbiomed.2024.108063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.
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Affiliation(s)
- Jilong Bian
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China.
| | - Xuan Liu
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Guanghui Dong
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Chang Hou
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China.
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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23
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Liu M, Wu T, Li X, Zhu Y, Chen S, Huang J, Zhou F, Liu H. ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization. Front Genet 2024; 15:1352504. [PMID: 38487252 PMCID: PMC10937565 DOI: 10.3389/fgene.2024.1352504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Tao Wu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Xue Li
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Yingxue Zhu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Sen Chen
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Fengfeng Zhou
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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24
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Baothman O, Ali EMM, Hosawi S, Hassan E. Konozy E, Abu Zeid IM, Ahmad A, Altayb HN. Prediction of anticancer peptides derived from the true lectins of Phoenix dactylifera and their synergetic effect with mitotane. Front Pharmacol 2024; 15:1322865. [PMID: 38464729 PMCID: PMC10920327 DOI: 10.3389/fphar.2024.1322865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/18/2024] [Indexed: 03/12/2024] Open
Abstract
Background and aims: Cancer continues to be a significant source of both illness and death on a global scale, traditional medicinal plants continue to serve as a fundamental resource of natural bioactive compounds as an alternative source of remedies. Although there have been numerous studies on the therapeutic role of Phoenix dactylifera, the study of the role of peptides has not been thoroughly investigated. This study aimed to investigate the anticancer activity of lectin peptides from P. dactylifera using in silico and in vivo analysis. Methods: Different computational tools were used to extract and predict anticancer peptides from the true lectins of P. dactylifera. Nine peptides that are bioactive substances have been investigated for their anticancer activity against MCF-7 and T47D (two forms of breast cancer). To counteract the unfavorable effects of mitotane, the most potent peptides (U3 and U7) were combined with it and assessed for anticancer activity against MCF-7 and HepG2. Results: In silico analysis revealed that nine peptides were predicted with anticancer activity. In cell lines, the lowest IC50 values were measured in U3 and U7 against MCF-7 and T47D cells. U3 or U7 in combination with mitotane demonstrated the lowest IC50 against MCF-7 and HepG2. The maximum level of cell proliferation inhibition was 22% when U3 (500 µg/mL) and 25 µg/mL mitotane were combined, compared to 41% when 25 µg/mL mitotane was used alone. When mitotane and U3 or U7 were combined, it was shown that these bioactive substances worked synergistically with mitotane to lessen its negative effects. The combination of peptides and mitotane could be regarded as an efficient chemotherapeutic medication having these bioactive properties for treating a variety of tumors while enhancing the reduction of side effects.
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Affiliation(s)
- Othman Baothman
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi
| | - Ehab M. M. Ali
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Division of Biochemistry, Chemistry Department, Faculty of Science Tanta University, Tanta, Egypt
| | - Salman Hosawi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Emadeldin Hassan E. Konozy
- Laboratory of Proteomics and Glycoproteins, Biotechnology Park, Africa City of Technology, Khartoum, Sudan
- Pharmaceutical Research and Development Centre, Faculty of Pharmacy, Karary University, Omdurman, Sudan
| | - Isam M. Abu Zeid
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abrar Ahmad
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hisham N. Altayb
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi
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25
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Karakaya O, Kilimci ZH. An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM. PeerJ Comput Sci 2024; 10:e1831. [PMID: 38435607 PMCID: PMC10909209 DOI: 10.7717/peerj-cs.1831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/31/2023] [Indexed: 03/05/2024]
Abstract
Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.
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Affiliation(s)
- Onur Karakaya
- Research and Development Inc., Turkcell Technology, İstanbul, Turkey
| | - Zeynep Hilal Kilimci
- Department of Information Systems Engineering, Kocaeli University, Kocaeli, Turkey
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26
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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27
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Zhong G, Deng L. ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies. J Chem Inf Model 2024; 64:1092-1104. [PMID: 38277774 DOI: 10.1021/acs.jcim.3c01860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticancer peptides (ACPs) have emerged as promising candidates for highly specific anticancer drugs, and a number of computational approaches have been developed to identify ACPs. However, existing methods do not recognize specific types of anticancer function. In this article, we propose ACPScanner, an integrated approach to predict ACPs and non-ACPs at first and then predict several specific activity types for potential ACPs. We incorporate sequential, physicochemical properties, secondary structural information, and deep representation learning embeddings which are generated from artificial intelligence methods to build feature space. Customized deep learning and statistical learning methods are combined to form an integral architecture for the comprehensive two-level prediction task. To the best of our knowledge, ACPScanner is the first approach for specific ACP activity prediction. The comparative evaluation illustrates that ACPScanner achieves competitive prediction performance in both prediction phases in independent testings. We establish a web server at http://acpscanner.denglab.org to provide convenient usage of ACPScanner and make the predictive framework, source code, and data sets publicly available.
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Affiliation(s)
- Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
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28
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Vincenzi M, Mercurio FA, Leone M. Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools. Int J Mol Sci 2024; 25:1798. [PMID: 38339078 PMCID: PMC10855943 DOI: 10.3390/ijms25031798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).
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Affiliation(s)
| | | | - Marilisa Leone
- Institute of Biostructures and Bioimaging, Via Pietro Castellino 111, 80131 Naples, Italy; (M.V.); (F.A.M.)
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29
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Ghadiri N, Javidan M, Sheikhi S, Taştan Ö, Parodi A, Liao Z, Tayybi Azar M, Ganjalıkhani-Hakemi M. Bioactive peptides: an alternative therapeutic approach for cancer management. Front Immunol 2024; 15:1310443. [PMID: 38327525 PMCID: PMC10847386 DOI: 10.3389/fimmu.2024.1310443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Cancer is still considered a lethal disease worldwide and the patients' quality of life is affected by major side effects of the treatments including post-surgery complications, chemo-, and radiation therapy. Recently, new therapeutic approaches were considered globally for increasing conventional cancer therapy efficacy and decreasing the adverse effects. Bioactive peptides obtained from plant and animal sources have drawn increased attention because of their potential as complementary therapy. This review presents a contemporary examination of bioactive peptides derived from natural origins with demonstrated anticancer, ant invasion, and immunomodulation properties. For example, peptides derived from common beans, chickpeas, wheat germ, and mung beans exhibited antiproliferative and toxic effects on cancer cells, favoring cell cycle arrest and apoptosis. On the other hand, peptides from marine sources showed the potential for inhibiting tumor growth and metastasis. In this review we will discuss these data highlighting the potential befits of these approaches and the need of further investigations to fully characterize their potential in clinics.
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Affiliation(s)
- Nooshin Ghadiri
- Department of Immunology, Faculty of Medicine, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Moslem Javidan
- Department of Immunology, Faculty of Medicine, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Shima Sheikhi
- Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Özge Taştan
- Department of Food Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Türkiye
| | - Alessandro Parodi
- Scientific Center for Translation Medicine, Sirius University of Science and Technology, Sochi, Russia
| | - Ziwei Liao
- Department of Hematology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Mehdi Tayybi Azar
- Department of Biophysics, Faculty of Medicine, Yeditepe University, Istanbul, Türkiye
| | - Mazdak Ganjalıkhani-Hakemi
- Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
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30
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Ur Rahim J, Faheem MM, Nawaz S, Goswami A, Rai R. Synthesis and characterization of piperic acid conjugates with homochiral and heterochiral dipeptides containing phenylalanine and their application in skin cancer. Peptides 2023; 170:171113. [PMID: 37923167 DOI: 10.1016/j.peptides.2023.171113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/05/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
The current work demonstrates the synthesis and characterization of piperic acid conjugates with homochiral/heterochiral dipeptides containing phenylalanine as anti-skin cancer agents. The conjugates PA-DPhe-LPhe-OH, FC-1; PA-LPhe-DPhe-OH, FC-2; PA-DPhe-DPhe-OH, FC-3; and PA-DPhe-DPhe-OH, FC-4 were synthesized, characterized and assessed for cytotoxicity against melanoma cell lines of human and murine origin. Among all, PA-DPhe-DPhe-OH (FC-3) conjugate was identified as a potential cytotoxic lead against melanoma cells by delineating the anti-proliferative and anti-migratory potential together with its anti-inflammatory potential against pro-inflammatory interleukins (IL-1β, IL-6, and IL-8). Evidences from western blotting, fractionation, and immunocytochemistry experiments suggest that Stat-3 is a critical signaling molecule involved in the FC-3 mechanism of action. The results denote that FC-3 profoundly ablates Stat-3 expression, phosphorylation, and nuclear translocation. Stat-3 mRNA analysis revealed that FC-3 did not alter the transcription of Stat-3. However, in cells where proteasome mediated degradation was inhibited, FC-3 failed to check the Stat-3 expression implying that FC-3 augments the proteasomal degradation of Stat-3. Of note, FC-3 failed to reverse the IL-6 mediated hyperactivation of Stat-3 in A375 cells. Critically, in Stat-3 deficient cancer cells, the anti-clonogenic and anti-migratory potential of FC-3 was significantly subdued. Further, the in vivo efficacy of FC-3 was validated in the two-step (DMBA/TPA) chemically induced mouse skin cancer model. The FC-3-treated cohorts of mice unveiled a significant decrease in the cumulative number of tumors besides attenuation of tumor growth with respect to the vehicle-treated mice. Lastly, in corroboration with our in vitro findings, serum collected from mice groups at various intervals during the treatment regimen demonstrated decrement in IL-1β and IL-6 levels in FC-3 treated groups compared to the vehicle-treated group.
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Affiliation(s)
- Junaid Ur Rahim
- Natural Products and Medicinal Chemistry Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, Jammu and Kashmir 180001, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mir Mohd Faheem
- Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, Jammu and Kashmir 180001, India; School of Biotechnology, University of Jammu, Jammu 180006, India
| | - Shah Nawaz
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India; Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, Jammu and Kashmir 180001, India
| | - Anindya Goswami
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India; Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, Jammu and Kashmir 180001, India.
| | - Rajkishor Rai
- Natural Products and Medicinal Chemistry Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, Jammu and Kashmir 180001, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India.
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31
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Abd El-Aal AAA, Jayakumar FA, Reginald K. Dual-action potential of cationic cryptides against infections and cancers. Drug Discov Today 2023; 28:103764. [PMID: 37689179 DOI: 10.1016/j.drudis.2023.103764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/18/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Cryptides are a subfamily of bioactive peptides embedded latently in their parent proteins and have multiple biological functions. Cationic cryptides could be used as modern drugs in both infectious diseases and cancers because their mechanism of action is less likely to be affected by genetic mutations in the treated cells, therefore addressing a current unmet need in these two areas of medicine. In this review, we present the current understanding of cryptides, methods to mine them sustainably using available online databases and prediction tools, with a particular focus on their antimicrobial and anticancer potential, and their potential applicability in a clinical setting.
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Affiliation(s)
- Amr A A Abd El-Aal
- Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500 Selangor, Malaysia
| | - Fairen A Jayakumar
- Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500 Selangor, Malaysia
| | - Kavita Reginald
- Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500 Selangor, Malaysia.
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32
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Kordzadeh A, Ramazani Sa A, Mashayekhan S. Adsorption and encapsulation of melittin on covalently functionalized carbon nanotubes; a molecular dynamics simulation study. Comput Biol Med 2023; 166:107393. [PMID: 37741226 DOI: 10.1016/j.compbiomed.2023.107393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/11/2023] [Accepted: 08/26/2023] [Indexed: 09/25/2023]
Abstract
For the first time, molecular dynamics (MD) simulation was used to examine melittin's adsorption and encapsulation on covalently functionalized carbon nanotubes (fCNTs). The CNT wall and terminals were functionalized with carboxy, hydroxyl, and amine functional groups. The findings demonstrated that the melittin would be adsorbed on the fCNT's outer surface when just the CNT terminal is functionalized. On the other hand, melittin is encapsulated inside the nanotube space when the CNTs' walls and terminals are functionalized. Encapsulated melittin has an alpha-helix structure similar to melittin in a water medium. With the use of parameters like root mean square fluctuations (RMSF) and radius of gyration (Rg), the melittin conformational changes were evaluated. According to the findings, the amine functional group significantly alters the melittin's conformation. The wall and terminals fCNTs with hydroxyl and carboxyl could encapsulate melittin inside them with a stable structure. This result will be useful for the design of peptide carriers.
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Affiliation(s)
- Azadeh Kordzadeh
- Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Ahmad Ramazani Sa
- Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran; Institute for Convergence Science & Technology, Center for Bioscience & Technology, Sharif University of Technology, Tehran, 1458889694, Iran.
| | - Shohreh Mashayekhan
- Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran
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33
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Alamdari-Palangi V, Jaberi KR, Shahverdi M, Naeimzadeh Y, Tajbakhsh A, Khajeh S, Razban V, Fallahi J. Recent advances and applications of peptide-agent conjugates for targeting tumor cells. J Cancer Res Clin Oncol 2023; 149:15249-15273. [PMID: 37581648 DOI: 10.1007/s00432-023-05144-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/08/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Cancer, being a complex disease, presents a major challenge for the scientific and medical communities. Peptide therapeutics have played a significant role in different medical practices, including cancer treatment. METHOD This review provides an overview of the current situation and potential development prospects of anticancer peptides (ACPs), with a particular focus on peptide vaccines and peptide-drug conjugates for cancer treatment. RESULTS ACPs can be used directly as cytotoxic agents (molecularly targeted peptides) or can act as carriers (guiding missile) of chemotherapeutic agents and radionuclides by specifically targeting cancer cells. More than 60 natural and synthetic cationic peptides are approved in the USA and other major markets for the treatment of cancer and other diseases. Compared to traditional cancer treatments, peptides exhibit anticancer activity with high specificity and the ability to rapidly kill target cancer cells. ACP's target and kill cancer cells via different mechanisms, including membrane disruption, pore formation, induction of apoptosis, necrosis, autophagy, and regulation of the immune system. Modified peptides have been developed as carriers for drugs, vaccines, and peptide-drug conjugates, which have been evaluated in various phases of clinical trials for the treatment of different types of solid and leukemia cancer. CONCLUSIONS This review highlights the potential of ACPs as a promising therapeutic option for cancer treatment, particularly through the use of peptide vaccines and peptide-drug conjugates. Despite the limitations of peptides, such as poor metabolic stability and low bioavailability, modified peptides show promise in addressing these challenges. Various mechanism of action of anticancer peptides. Modes of action against cancer cells including: inducing apoptosis by cytochrome c release, direct cell membrane lysis (necrosis), inhibiting angiogenesis, inducing autophagy-mediated cell death and immune cell regulation.
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Affiliation(s)
- Vahab Alamdari-Palangi
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 7133654361, Iran
| | - Khojaste Rahimi Jaberi
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahshid Shahverdi
- Medical Biotechnology Research Center, Arak University of Medical Sciences, Arak, Iran
| | - Yasaman Naeimzadeh
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 7133654361, Iran
| | - Amir Tajbakhsh
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 7133654361, Iran
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Sahar Khajeh
- Bone and Joint Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Vahid Razban
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 7133654361, Iran.
| | - Jafar Fallahi
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 7133654361, Iran.
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Sun M, Hu H, Pang W, Zhou Y. ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information. Int J Mol Sci 2023; 24:15447. [PMID: 37895128 PMCID: PMC10607064 DOI: 10.3390/ijms242015447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/10/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.
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Affiliation(s)
- Mingwei Sun
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
| | - Haoyuan Hu
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
| | - Wei Pang
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK;
| | - You Zhou
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
- College of Software, Jilin University, Changchun 130012, China
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Yang Y, Wu H, Gao Y, Tong W, Li K. MFPPDB: a comprehensive multi-functional plant peptide database. FRONTIERS IN PLANT SCIENCE 2023; 14:1224394. [PMID: 37908832 PMCID: PMC10613858 DOI: 10.3389/fpls.2023.1224394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/29/2023] [Indexed: 11/02/2023]
Abstract
Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.
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Affiliation(s)
- Yaozu Yang
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Hongwei Wu
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Yu Gao
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Wei Tong
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, China
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Cui C, Huo Q, Xiong X, Li K, Fishel ML, Li B, Yokota H. Anticancer Peptides Derived from Aldolase A and Induced Tumor-Suppressing Cells Inhibit Pancreatic Ductal Adenocarcinoma Cells. Pharmaceutics 2023; 15:2447. [PMID: 37896207 PMCID: PMC10610494 DOI: 10.3390/pharmaceutics15102447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/29/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
PDAC (pancreatic ductal adenocarcinoma) is a highly aggressive malignant tumor. We have previously developed induced tumor-suppressing cells (iTSCs) that secrete a group of tumor-suppressing proteins. Here, we examined a unique procedure to identify anticancer peptides (ACPs), using trypsin-digested iTSCs-derived protein fragments. Among the 10 ACP candidates, P04 (IGEHTPSALAIMENANVLAR) presented the most efficient anti-PDAC activities. P04 was derived from aldolase A (ALDOA), a glycolytic enzyme. Extracellular ALDOA, as well as P04, was predicted to interact with epidermal growth factor receptor (EGFR), and P04 downregulated oncoproteins such as Snail and Src. Importantly, P04 has no inhibitory effect on mesenchymal stem cells (MSCs). We also generated iTSCs by overexpressing ALDOA in MSCs and peripheral blood mononuclear cells (PBMCs). iTSC-derived conditioned medium (CM) inhibited the progression of PDAC cells as well as PDAC tissue fragments. The inhibitory effect of P04 was additive to that of CM and chemotherapeutic drugs such as 5-Flu and gemcitabine. Notably, applying mechanical vibration to PBMCs elevated ALDOA and converted PBMCs into iTSCs. Collectively, this study presented a unique procedure for selecting anticancer P04 from ALDOA in an iTSCs-derived proteome for the treatment of PDAC.
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Affiliation(s)
- Changpeng Cui
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.); (K.L.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Qingji Huo
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.); (K.L.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Xue Xiong
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.); (K.L.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Kexin Li
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.); (K.L.)
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Melissa L. Fishel
- Department of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN 46202, USA
| | - Baiyan Li
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China; (C.C.); (Q.H.); (X.X.); (K.L.)
| | - Hiroki Yokota
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
- Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN 46202, USA
- Department of Pediatrics, Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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Ma Y, Liu X, Zhang X, Yu Y, Li Y, Song M, Wang J. Efficient Mining of Anticancer Peptides from Gut Metagenome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300107. [PMID: 37382183 PMCID: PMC10477861 DOI: 10.1002/advs.202300107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/03/2023] [Indexed: 06/30/2023]
Abstract
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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Affiliation(s)
- Yue Ma
- CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of Microbiology, Chinese Academy of Sciences100101BeijingP. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
- Max Planck Institute for Evolutionary Biology24306PlönGermany
| | - Xiaolin Liu
- CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of Microbiology, Chinese Academy of Sciences100101BeijingP. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
- Max Planck Institute for Evolutionary Biology24306PlönGermany
| | - Xuan Zhang
- CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of Microbiology, Chinese Academy of Sciences100101BeijingP. R. China
| | - Ying Yu
- CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of Microbiology, Chinese Academy of Sciences100101BeijingP. R. China
| | - Yujing Li
- State Key Laboratory of Membrane BiologyInstitute of ZoologyChinese Academy of Sciences100101BeijingP. R. China
- Institute for Stem Cell and RegenerationChinese Academy of Sciences100101BeijingP. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine100101BeijingP. R. China
| | - Moshi Song
- State Key Laboratory of Membrane BiologyInstitute of ZoologyChinese Academy of Sciences100101BeijingP. R. China
- Institute for Stem Cell and RegenerationChinese Academy of Sciences100101BeijingP. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine100101BeijingP. R. China
| | - Jun Wang
- CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of Microbiology, Chinese Academy of Sciences100101BeijingP. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
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Saini S, Rathore A, Sharma S, Saini A. Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides. BIOIMPACTS : BI 2023; 14:26438. [PMID: 38327633 PMCID: PMC10844588 DOI: 10.34172/bi.2023.26438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/04/2022] [Accepted: 12/04/2022] [Indexed: 02/09/2024]
Abstract
Introduction Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends. Methods Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs. Results EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis. Conclusion The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.
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Affiliation(s)
- Sandeep Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
- Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32-C, Chandigarh 160030, India
| | - Aayushi Rathore
- Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Bengaluru 560100, India
| | - Sheetal Sharma
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
| | - Avneet Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
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Nhàn NTT, Yamada T, Yamada KH. Peptide-Based Agents for Cancer Treatment: Current Applications and Future Directions. Int J Mol Sci 2023; 24:12931. [PMID: 37629112 PMCID: PMC10454368 DOI: 10.3390/ijms241612931] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Peptide-based strategies have received an enormous amount of attention because of their specificity and applicability. Their specificity and tumor-targeting ability are applied to diagnosis and treatment for cancer patients. In this review, we will summarize recent advancements and future perspectives on peptide-based strategies for cancer treatment. The literature search was conducted to identify relevant articles for peptide-based strategies for cancer treatment. It was performed using PubMed for articles in English until June 2023. Information on clinical trials was also obtained from ClinicalTrial.gov. Given that peptide-based strategies have several advantages such as targeted delivery to the diseased area, personalized designs, relatively small sizes, and simple production process, bioactive peptides having anti-cancer activities (anti-cancer peptides or ACPs) have been tested in pre-clinical settings and clinical trials. The capability of peptides for tumor targeting is essentially useful for peptide-drug conjugates (PDCs), diagnosis, and image-guided surgery. Immunomodulation with peptide vaccines has been extensively tested in clinical trials. Despite such advantages, FDA-approved peptide agents for solid cancer are still limited. This review will provide a detailed overview of current approaches, design strategies, routes of administration, and new technological advancements. We will highlight the success and limitations of peptide-based therapies for cancer treatment.
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Affiliation(s)
- Nguyễn Thị Thanh Nhàn
- Department of Pharmacology & Regenerative Medicine, University of Illinois College of Medicine, Chicago, IL 60612, USA;
| | - Tohru Yamada
- Department of Surgery, Division of Surgical Oncology, University of Illinois College of Medicine, Chicago, IL 60612, USA;
- Richard & Loan Hill Department of Biomedical Engineering, University of Illinois College of Engineering, Chicago, IL 60607, USA
| | - Kaori H. Yamada
- Department of Pharmacology & Regenerative Medicine, University of Illinois College of Medicine, Chicago, IL 60612, USA;
- Department of Ophthalmology & Visual Sciences, University of Illinois College of Medicine, Chicago, IL 60612, USA
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Xu J, Li F, Li C, Guo X, Landersdorfer C, Shen HH, Peleg AY, Li J, Imoto S, Yao J, Akutsu T, Song J. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Brief Bioinform 2023; 24:bbad240. [PMID: 37369638 PMCID: PMC10359087 DOI: 10.1093/bib/bbad240] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/30/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
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Affiliation(s)
- Jing Xu
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3800, Australia
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Cornelia Landersdorfer
- Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Hsin-Hui Shen
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Clayton, VIC, 3800, Australia
| | - Anton Y Peleg
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Infectious Diseases, Alfred Hospital, Alfred Health, Melbourne, Victoria, Australia
| | - Jian Li
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | | | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Deng Y, Ma S, Li J, Zheng B, Lv Z. Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides. Int J Mol Sci 2023; 24:10854. [PMID: 37446031 DOI: 10.3390/ijms241310854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/17/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Anticancer peptides (ACPs) represent a promising new therapeutic approach in cancer treatment. They can target cancer cells without affecting healthy tissues or altering normal physiological functions. Machine learning algorithms have increasingly been utilized for predicting peptide sequences with potential ACP effects. This study analyzed four benchmark datasets based on a well-established random forest (RF) algorithm. The peptide sequences were converted into 566 physicochemical features extracted from the amino acid index (AAindex) library, which were then subjected to feature selection using four methods: light gradient-boosting machine (LGBM), analysis of variance (ANOVA), chi-squared test (Chi2), and mutual information (MI). Presenting and merging the identified features using Venn diagrams, 19 key amino acid physicochemical properties were identified that can be used to predict the likelihood of a peptide sequence functioning as an ACP. The results were quantified by performance evaluation metrics to determine the accuracy of predictions. This study aims to enhance the efficiency of designing peptide sequences for cancer treatment.
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Affiliation(s)
- Yiting Deng
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Shuhan Ma
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jiayu Li
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Bowen Zheng
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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Barman P, Joshi S, Sharma S, Preet S, Sharma S, Saini A. Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics. Int J Pept Res Ther 2023; 29:61. [PMID: 37251528 PMCID: PMC10206374 DOI: 10.1007/s10989-023-10524-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2023] [Indexed: 05/31/2023]
Abstract
In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications. Graphical Abstract
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Affiliation(s)
- Panchali Barman
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Shubhi Joshi
- Energy Research Centre, Panjab University, Sector 14, Chandigarh, 160014 India
| | - Sheetal Sharma
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Simran Preet
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Shweta Sharma
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Avneet Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
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Pereira KE, Deslouches JT, Deslouches B, Woodley SK. In Vitro Investigation of the Antibacterial Activity of Salamander Skin Peptides. Curr Microbiol 2023; 80:214. [PMID: 37195436 DOI: 10.1007/s00284-023-03320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 05/02/2023] [Indexed: 05/18/2023]
Abstract
Given the current and future costs of antibiotic-resistant bacteria to human health and economic productivity, there is an urgent need to develop new antimicrobial compounds. Antimicrobial peptides are a promising alternative to conventional antibiotics and other antimicrobials. Amphibian skin is a rich source of bioactive compounds, but the antibacterial properties of salamander skin peptides have been neglected. Here, we examined the in vitro ability of skin peptides from 9 species of salamander representing 6 salamander families to inhibit the growth of ESKAPE pathogens, which are bacteria that have developed resistance to conventional antibiotics. We also examined whether the skin peptides caused lysis of human red blood cells. Skin peptides from Amphiuma tridactylum had the greatest antimicrobial properties, completely inhibiting the growth of all bacterial strains except for Enterococcus faecium. Likewise, skin peptides from Cryptobranchus alleganiensis completely inhibited the growth of several of the bacterial strains. In contrast, skin peptide mixtures from Ambystoma maculatum, Desmognathus fuscus, Eurycea bislineata, E. longicauda, Necturus beyeri, N. maculosus, and Siren intermedia did not completely inhibit bacterial growth even at the highest concentrations. Finally, none of the skin peptide mixtures caused lysis of human red blood cells. Together, we demonstrate that salamander skin produces peptides with potent antibacterial properties. It remains to elucidate the peptide sequences and their antibacterial mechanisms.
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Affiliation(s)
- Kenzie E Pereira
- Department of Biological Sciences, Duquesne University, Pittsburgh, PA, USA
| | | | - Berthony Deslouches
- Department of Environmental and Occupational Health, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sarah K Woodley
- Department of Biological Sciences, Duquesne University, Pittsburgh, PA, USA.
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Sowers A, Wang G, Xing M, Li B. Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology. Microorganisms 2023; 11:1129. [PMID: 37317103 PMCID: PMC10223199 DOI: 10.3390/microorganisms11051129] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/16/2023] Open
Abstract
Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.
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Affiliation(s)
- Alexa Sowers
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
- School of Pharmacy, West Virginia University, Morgantown, WV 26506, USA
| | - Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198, USA
| | - Malcolm Xing
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Bingyun Li
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
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Agüero-Chapin G, Antunes A, Mora JR, Pérez N, Contreras-Torres E, Valdes-Martini JR, Martinez-Rios F, Zambrano CH, Marrero-Ponce Y. Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery. Antibiotics (Basel) 2023; 12:antibiotics12040747. [PMID: 37107109 PMCID: PMC10135022 DOI: 10.3390/antibiotics12040747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José R Mora
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Noel Pérez
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Ernesto Contreras-Torres
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | | | - Felix Martinez-Rios
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México 03920, Mexico
| | - Cesar H Zambrano
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada 22860, Baja California, Mexico
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Deng H, Ding M, Wang Y, Li W, Liu G, Tang Y. ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types. Comput Biol Med 2023; 158:106844. [PMID: 37058760 DOI: 10.1016/j.compbiomed.2023.106844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.
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Li Y, Ma D, Chen D, Chen Y. ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree. Front Genet 2023; 14:1165765. [PMID: 37065496 PMCID: PMC10090421 DOI: 10.3389/fgene.2023.1165765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.
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Affiliation(s)
- Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Di Ma
- College of Computer, Hangzhou Dianzi University, Hangzhou, China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
- *Correspondence: Dong Chen, ; Yu Chen,
| | - Yu Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Dong Chen, ; Yu Chen,
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Park J, Moon SK, Lee C. N-methylsansalvamide elicits antitumor effects in colon cancer cells in vitro and in vivo by regulating proliferation, apoptosis, and metastatic capacity. Front Pharmacol 2023; 14:1146966. [PMID: 37007044 PMCID: PMC10060634 DOI: 10.3389/fphar.2023.1146966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
N-methylsansalvamide (MSSV), a cyclic pentadepsipeptide, was obtained from a strain of Fusarium solani f. radicicola. The current study investigated the anti-colorectal cancer effect of MSSV. MSSV exhibited the inhibition of the proliferation in HCT116 cells via induction of G0/G1 cell cycle arrest by downregulating CDK 2, CDK6, cyclin D, and cyclin E, and upregulating p21WAF1 and p27KIP1. Decreased phosphorylation of AKT was observed in MSSV-treated cells. Moreover, MSSV treatment induced caspase-mediated apoptosis through elevating the level of cleaved caspase 3, cleaved PARP, cleaved caspase 9, and pro-apoptotic Bax. MSSV revealed the declined MMP-9 level mediated by reduction in the binding activity of AP-1, Sp-1, and NF-κB motifs, which led to the migration and invasion of HCT116 cells. In vitro metabolism with rat liver S9 fractions was performed to examine the effect of MSSV metabolites. The metabolic process enhanced the inhibitory effect of MSSV on the HCT116 cell proliferation via decline of cyclin D1 expression and AKT phosphorylation. Finally, oral administration of MSSV inhibited the tumor growth of HCT116 xenograft mice. These results suggest that MSSV is a potential anti-tumor agent in colorectal cancer treatment.
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Affiliation(s)
- Juhee Park
- Food Analysis Research Center, Food Industry Research Division, Korea Food Research Institute, Wanju, Republic of Korea
- Department of Food Science and Biotechnology, Chung-Ang University, Anseong, Republic of Korea
| | - Sung-Kwon Moon
- Department of Food and Nutrition, Chung-Ang University, Anseong, Republic of Korea
- *Correspondence: Sung-Kwon Moon, ; Chan Lee,
| | - Chan Lee
- Department of Food Science and Biotechnology, Chung-Ang University, Anseong, Republic of Korea
- *Correspondence: Sung-Kwon Moon, ; Chan Lee,
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50
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Yuan Q, Chen K, Yu Y, Le NQK, Chua MCH. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Brief Bioinform 2023; 24:6987656. [PMID: 36642410 DOI: 10.1093/bib/bbac630] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/01/2022] [Accepted: 12/28/2022] [Indexed: 01/17/2023] Open
Abstract
Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
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Affiliation(s)
- Qitong Yuan
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Keyi Chen
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Yimin Yu
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing St, 110, Taipei, Taiwan
| | - Matthew Chin Heng Chua
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
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