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Bizzotto E, Zampieri G, Treu L, Filannino P, Di Cagno R, Campanaro S. Classification of bioactive peptides: A systematic benchmark of models and encodings. Comput Struct Biotechnol J 2024; 23:2442-2452. [PMID: 38867723 PMCID: PMC11168199 DOI: 10.1016/j.csbj.2024.05.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/10/2024] [Accepted: 05/22/2024] [Indexed: 06/14/2024] Open
Abstract
Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.
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Affiliation(s)
- Edoardo Bizzotto
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Guido Zampieri
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Laura Treu
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Pasquale Filannino
- Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Via G. Amendola 165/a, Bari 70126, Italy
| | - Raffaella Di Cagno
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Universita, 5, Bolzano 39100, Italy
| | - Stefano Campanaro
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
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2
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Rukh G, Akbar S, Rehman G, Alarfaj FK, Zou Q. StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning. BMC Bioinformatics 2024; 25:256. [PMID: 39098908 PMCID: PMC11298090 DOI: 10.1186/s12859-024-05884-6] [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: 04/19/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins. METHODS In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model. RESULTS Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98. CONCLUSION Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.
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Affiliation(s)
- Gul Rukh
- Department of Zoology, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Gauhar Rehman
- Department of Zoology, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Fawaz Khaled Alarfaj
- Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), 31982, Al-Ahsa, Saudi Arabia
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, People's Republic of China.
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Chen Q, Ge Y, He X, Li S, Fang Z, Li C, Chen H. Virtual-screening of xanthine oxidase inhibitory peptides: Inhibition mechanisms and prediction of activity using machine-learning. Food Chem 2024; 460:140741. [PMID: 39128372 DOI: 10.1016/j.foodchem.2024.140741] [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: 03/20/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 08/13/2024]
Abstract
Xanthine oxidase (XO) inhibitory peptides can prevent XO-mediated hyperuricemia. Currently, QSAR about XO inhibitory peptides with different lengths remains to be enriched. Here, XO inhibitory peptides were obtained from porcine visceral proteins through virtual-screening. A prediction model was established by machine-learning. Virtual-screening retained four lengths of peptides, including 3-6. Molecular-docking recognized their binding sites with XO and showed residues W, F, and G were the key amino acids. Datasets of XO inhibitory peptides therewith were established. The optimal model was used to generalize the peptides reported. Results showed that the R2 of the tripeptide, tetrapeptide, pentapeptide and hexapeptide in the generalisation test were R2 = 0.81, R2 = 0.82, R2 = 0.83 and R2 = 0.83, respectively. Overall, this work can serve as a reference for explaining the activity mechanism of XO inhibitory peptides and predicting the activity of XO inhibitory peptides.
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Affiliation(s)
- Qian Chen
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Yuxi Ge
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Xiaoyu He
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Shanshan Li
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Zhengfeng Fang
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Cheng Li
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China
| | - Hong Chen
- College of Food Science, Sichuan Agricultural University, Yaan, Sichuan 625014, China.
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4
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Weston M, Hu H, Li X. PSPI: A deep learning approach for prokaryotic small protein identification. Front Genet 2024; 15:1439423. [PMID: 39050248 PMCID: PMC11266045 DOI: 10.3389/fgene.2024.1439423] [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: 05/27/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.
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Affiliation(s)
- Matthew Weston
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, United States
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Du A, Jia W, Zhang R. Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion. J Dairy Sci 2024:S0022-0302(24)00970-6. [PMID: 38945266 DOI: 10.3168/jds.2024-24887] [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: 03/09/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
Milk serves as an important dietary source of bioactive peptides, offering notable benefits to individuals. Among the antioxidant short peptides (di- and tripeptides) generated from gastrointestinal digestion are characterized by enhanced bioavailability and bioaccessibility, while assessing them individually presents a labor-intensive and expensive challenge. Based on 4 distinct types of amino acid descriptors (physicochemical, 3D structural, quantum, and topological attributes) and genetic algorithms for feature selection, 1 and 4 machine learning predicted models separately for di- and tripeptides with ABTS radical scavenging capacity exhibited excellent fitting and prediction ability with random forest regression as machine learning algorithm. Intriguingly, the electronic properties of N-terminal amino acid were considered as only factor affecting the antioxidant capacity of dipeptides containing both tyrosine and tryptophan. Four peptides from the potential di- and tripeptides exhibited highly predicted values by the constructed predicted models. Subsequently, a total of 45 dipeptides and 52 tripeptides were screened by a customized workflow in goat milk during in vitro simulated digestion. In addition to 5 known antioxidant dipeptides, 9 peptides were quantified during digestion, falling within the range of 0.04 to 1.78 mg L-1. Particularly noteworthy was the promising in vivo functionality of antioxidant dipeptides with N-terminal tyrosine, supported by in silico assays. Overall, this investigation explored crucial molecular properties influencing antioxidant short peptides and high-throughput screening potential peptides with antioxidant activity from goat milk aided by machine learning, thereby facilitating the identification of novel bioactive peptides from milk-derived proteins and paving the way for understanding their metabolites during digestion.
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Affiliation(s)
- An Du
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
| | - Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
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Arif R, Kanwal S, Ahmed S, Kabir M. A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features. Interdiscip Sci 2024; 16:503-518. [PMID: 38733473 DOI: 10.1007/s12539-024-00628-9] [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: 10/11/2023] [Revised: 03/16/2024] [Accepted: 03/27/2024] [Indexed: 05/13/2024]
Abstract
Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both [Formula: see text] and [Formula: see text] datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals.
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Affiliation(s)
- Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan.
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Chen L, Hu Z, Rong Y, Lou B. Deep2Pep: A deep learning method in multi-label classification of bioactive peptide. Comput Biol Chem 2024; 109:108021. [PMID: 38308955 DOI: 10.1016/j.compbiolchem.2024.108021] [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: 09/22/2023] [Revised: 12/27/2023] [Accepted: 01/18/2024] [Indexed: 02/05/2024]
Abstract
Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the functional peptides from a large number of peptides with unknown functions. With the development of machine learning and Deep learning, the combination of computational methods and biological information provides an effective method for identifying peptide functions. To explore the value of multi-functional active peptides, a new deep learning method named Deep2Pep (Deep learning to Peptides) was constructed, which was based on sequence encoding, embedding, and language tokenizer. It can achieve predictions of peptides on antimicrobial, antihypertensive, antioxidant and antihyperglycemic by converting sequence information into digital vectors, combined BiLSTM, attention-residual algorithm, and BERT Encoder. The results showed that Deep2Pep had a Hamming Loss of 0.095, subset Accuracy of 0.737, and Macro F1-Score of 0.734. which outperformed other models. BiLSTM played a primary role in Deep2Pep, which BERT encoder was in an auxiliary position. Deep learning algorithms was used in this study to accurately predict the four active functions of peptides, and it was expected to provide effective references for predicting multi-functional peptides.
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Affiliation(s)
- Lihua Chen
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Zhenkang Hu
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Yuzhi Rong
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China.
| | - Bao Lou
- Institute of Hydrobiology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
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8
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Song C, Zhou Z, Yu Y, Shi M, Zhang J. An improved Bi-LSTM method based on heterogeneous features fusion and attention mechanism for ECG recognition. Comput Biol Med 2024; 169:107903. [PMID: 38171263 DOI: 10.1016/j.compbiomed.2023.107903] [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: 08/20/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/05/2024]
Abstract
Electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. However, extracting powerful deep features from ECG signal for recognition is still a challenging problem today due to the variable abnormal rhythms and noise distribution. This work proposes a Bi-LSTM algorithm based on heterogeneous features fusion and attention mechanism (HFFAM + Bi-LSTM). Combining the empirical features and the features learned by the deep learning network, HFFAM + Bi-LSTM can comprehensively extract the temporal frequency information and spatial structure information of the ECG signal. Meanwhile, a novel attention mechanism based on improved DTW (AM-DTW) is designed to analyze and control the fusion process of features. The role of AM-DTW in HFFAM + Bi-LSTM is twofold, one is to measure the feature similarity between ECG signal sets with different labels using the improved DTW, and the other is to distinguish the features into isomorphic and heterogeneous features as well as adaptive weighting of the features. It is worth mentioning that overly similar isomorphic features are filtered out to further optimize the algorithm. Thus, HFFAM + Bi-LSTM has the advantage of strengthening the heterogeneous information in the feature subspace while accounting for the isomorphic features. The accuracy of HFFAM + Bi-LSTM reaches up to 98.1 % and 97.1 % on the simulated and real datasets, respectively. Compared to the all benchmark models, the classification accuracy of HFFAM + Bi-LSTM is 1.3 % higher than the best. The experiments also demonstrate that HFFAM + Bi-LSTM has better performance compared with existing methods, which provides a new scheme for automatic detection of ECG signal.
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Affiliation(s)
- Chaoyang Song
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Zilong Zhou
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yue Yu
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Manman Shi
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Jingxiang Zhang
- School of Science, Jiangnan University, Wuxi, 214122, China.
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9
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Jeong S, Jung JH, Jung KW, Ryu S, Lim S. From microbes to molecules: a review of microbial-driven antioxidant peptide generation. World J Microbiol Biotechnol 2023; 40:29. [PMID: 38057638 DOI: 10.1007/s11274-023-03826-7] [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: 09/18/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023]
Abstract
Oxidative stress, arising from excess reactive oxygen species (ROS) or insufficient antioxidant defenses, can damage cellular components, such as lipids, proteins, and nucleic acids, resulting in cellular dysfunction. The relationship between oxidative stress and various health disorders has prompted investigations into potent antioxidants that counteract ROS's detrimental impacts. In this context, antioxidant peptides, composed of two to twenty amino acids, have emerged as a unique group of antioxidants and have found applications in food, nutraceuticals, and pharmaceuticals. Antioxidant peptides are sourced from natural ingredients, mainly proteins derived from foods like milk, eggs, meat, fish, and plants. These peptides can be freed from their precursor proteins through enzymatic hydrolysis, fermentation, or gastrointestinal digestion. Previously published studies focused on the origin and production methods of antioxidant peptides, describing their structure-activity relationship and the mechanisms of food-derived antioxidant peptides. Yet, the role of microorganisms hasn't been sufficiently explored, even though the production of antioxidant peptides frequently employs a variety of microorganisms, such as bacteria, fungi, and yeasts, which are recognized for producing specific proteases. This review aims to provide a comprehensive overview of microorganisms and their proteases participating in enzymatic hydrolysis and microbial fermentation to produce antioxidant peptides. This review also covers endogenous peptides originating from microorganisms. The information obtained from this review might guide the discovery of novel organisms adept at generating antioxidant peptides.
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Affiliation(s)
- Soyoung Jeong
- Radiation Biotechnology Division, Korea Atomic Energy Research Institute, Jeongeup, 56212, Republic of Korea
- Department of Food and Animal Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jong-Hyun Jung
- Radiation Biotechnology Division, Korea Atomic Energy Research Institute, Jeongeup, 56212, Republic of Korea
| | - Kwang-Woo Jung
- Radiation Biotechnology Division, Korea Atomic Energy Research Institute, Jeongeup, 56212, Republic of Korea
| | - Sangryeol Ryu
- Department of Food and Animal Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangyong Lim
- Radiation Biotechnology Division, Korea Atomic Energy Research Institute, Jeongeup, 56212, Republic of Korea.
- Department of Radiation Science, University of Science and Technology, Daejeon, 34113, Republic of Korea.
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Zhang Y, Li Y, Ren T, Xiao P, Duan JA. Novel and efficient techniques in the discovery of antioxidant peptides. Crit Rev Food Sci Nutr 2023:1-15. [PMID: 37585700 DOI: 10.1080/10408398.2023.2245052] [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: 08/18/2023]
Abstract
As a research hotspot in food science and nutrition, antioxidant peptides can function by scavenging free radicals, inhibiting peroxides, and chelating metal ions. Therefore, how to efficiently discover and screen antioxidant peptides has become a key issue in research and production. Traditional discovery methods are time-consuming and costly, but also challenging to resolve the quantitative structure-activity relationship of antioxidant peptides. Several novel techniques, including artificial intelligence, molecular docking, bioinformatics, quantum chemistry, phage display, switchSENSE, surface plasmon resonance, and fluorescence polarization, are emerging rapidly as solutions. These techniques possess efficient capability for the discovery of antioxidant peptides, even with the potential for high-throughput screening. In addition, the quantitative structure-activity relationship can be resolved. Notably, combining these novel techniques can overcome the drawbacks of a single one, thus improving efficiency and expanding the discovery horizon. This review has summarized eight novel and efficient techniques for discovering antioxidant peptides and the combination of techniques. This review aims to provide scientific evidence and perspectives for antioxidant peptide research.
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Affiliation(s)
- Yuhao Zhang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yun Li
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tianyi Ren
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, China
| | - Ping Xiao
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jin-Ao Duan
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, China
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