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Hassan MT, Tayara H, Chong KT. NaII-Pred: An ensemble-learning framework for the identification and interpretation of sodium ion inhibitors by fusing multiple feature representation. Comput Biol Med 2024; 178:108737. [PMID: 38879934 DOI: 10.1016/j.compbiomed.2024.108737] [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: 01/29/2024] [Revised: 04/21/2024] [Accepted: 06/08/2024] [Indexed: 06/18/2024]
Abstract
High-affinity ligand peptides for ion channels are essential for controlling the flow of ions across the plasma membrane. These peptides are now being investigated as possible therapeutic possibilities for a variety of illnesses, including cancer and cardiovascular disease. So, the identification and interpretation of ligand peptide inhibitors to control ion flow across cells become pivotal for exploration. In this work, we developed an ensemble-based model, NaII-Pred, for the identification of sodium ion inhibitors. The ensemble model was trained, tested, and evaluated on three different datasets. The NaII-Pred method employs six different descriptors and a hybrid feature set in conjunction with five conventional machine learning classifiers to create 35 baseline models. Through an ensemble approach, the top five baseline models trained on the hybrid feature set were integrated to yield the final predictive model, NaII-Pred. Our proposed model, NaII-Pred, outperforms the baseline models and the current predictors on both datasets. We believe NaII-Pred will play a critical role in screening and identifying potential sodium ion inhibitors and will be an invaluable tool.
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Affiliation(s)
- Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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2
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Salam A, Ullah F, Amin F, Ahmad Khan I, Garcia Villena E, Kuc Castilla A, de la Torre I. Efficient prediction of anticancer peptides through deep learning. PeerJ Comput Sci 2024; 10:e2171. [PMID: 39145253 PMCID: PMC11323142 DOI: 10.7717/peerj-cs.2171] [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: 04/29/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Background Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.
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Affiliation(s)
- Abdu Salam
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Faizan Ullah
- Department of Computer Science, Bacha Khan University, Charsadda, Pakistan
| | - Farhan Amin
- School of Computer Science and Engineering, Yeungnam University, Gyeongsan, Republic of Korea
| | - Izaz Ahmad Khan
- Department of Computer Science, Bacha Khan University, Charsadda, Pakistan
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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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4
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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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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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Gaffar S, Tayara H, Chong KT. Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework. Comput Biol Med 2024; 174:108438. [PMID: 38613893 DOI: 10.1016/j.compbiomed.2024.108438] [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: 01/22/2024] [Revised: 04/01/2024] [Accepted: 04/07/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded encouraging outcomes, thereby creating a hopeful therapeutic avenue for the treatment of cancer. Therefore, correctly identifying the anti-angiogenic peptides is extremely important in comprehending their biophysical and biochemical traits, laying the groundwork for uncovering novel drugs to combat cancer. METHODS In this work, we present a novel ensemble-learning-based model, Stack-AAgP, specifically designed for the accurate identification and interpretation of anti-angiogenic peptides (AAPs). Initially, a feature representation approach is employed, generating 24 baseline models through six machine learning algorithms (random forest [RF], extra tree classifier [ETC], extreme gradient boosting [XGB], light gradient boosting machine [LGBM], CatBoost, and SVM) and four feature encodings (pseudo-amino acid composition [PAAC], amphiphilic pseudo-amino acid composition [APAAC], composition of k-spaced amino acid pairs [CKSAAP], and quasi-sequence-order [QSOrder]). Subsequently, the output (predicted probabilities) from 24 baseline models was inputted into the same six machine-learning classifiers to generate their respective meta-classifiers. Finally, the meta-classifiers were stacked together using the ensemble-learning framework to construct the final predictive model. RESULTS Findings from the independent test demonstrate that Stack-AAgP outperforms the state-of-the-art methods by a considerable margin. Systematic experiments were conducted to assess the influence of hyperparameters on the proposed model. Our model, Stack-AAgP, was evaluated on the independent NT15 dataset, revealing superiority over existing predictors with an accuracy improvement ranging from 5% to 7.5% and an increase in Matthews Correlation Coefficient (MCC) from 7.2% to 12.2%.
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Affiliation(s)
- Saima Gaffar
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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Karim T, Shaon MSH, Sultan MF, Hasan MZ, Kafy AA. ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach. Comput Biol Med 2024; 169:107915. [PMID: 38171261 DOI: 10.1016/j.compbiomed.2023.107915] [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: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024]
Abstract
Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.
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Affiliation(s)
- Tasmin Karim
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Shazzad Hossain Shaon
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Fahim Sultan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Zahid Hasan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Abdulla-Al Kafy
- Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh.
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Ma C, Wolfinger R. A prediction model for blood-brain barrier penetrating peptides based on masked peptide transformers with dynamic routing. Brief Bioinform 2023; 24:bbad399. [PMID: 37985456 DOI: 10.1093/bib/bbad399] [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: 07/20/2023] [Revised: 09/26/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Blood-brain barrier penetrating peptides (BBBPs) are short peptide sequences that possess the ability to traverse the selective blood-brain interface, making them valuable drug candidates or carriers for various payloads. However, the in vivo or in vitro validation of BBBPs is resource-intensive and time-consuming, driving the need for accurate in silico prediction methods. Unfortunately, the scarcity of experimentally validated BBBPs hinders the efficacy of current machine-learning approaches in generating reliable predictions. In this paper, we present DeepB3P3, a novel framework for BBBPs prediction. Our contribution encompasses four key aspects. Firstly, we propose a novel deep learning model consisting of a transformer encoder layer, a convolutional network backbone, and a capsule network classification head. This integrated architecture effectively learns representative features from peptide sequences. Secondly, we introduce masked peptides as a powerful data augmentation technique to compensate for small training set sizes in BBBP prediction. Thirdly, we develop a novel threshold-tuning method to handle imbalanced data by approximating the optimal decision threshold using the training set. Lastly, DeepB3P3 provides an accurate estimation of the uncertainty level associated with each prediction. Through extensive experiments, we demonstrate that DeepB3P3 achieves state-of-the-art accuracy of up to 98.31% on a benchmarking dataset, solidifying its potential as a promising computational tool for the prediction and discovery of BBBPs.
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Affiliation(s)
- Chunwei Ma
- JMP Statistical Discovery, LLC, Cary, 27513, NC, USA
- Department of Computer Science and Engineering, University at Buffalo, Buffalo, 14260, NY, USA
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Tao H, Shan S, Fu H, Zhu C, Liu B. An Augmented Sample Selection Framework for Prediction of Anticancer Peptides. Molecules 2023; 28:6680. [PMID: 37764455 PMCID: PMC10535447 DOI: 10.3390/molecules28186680] [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: 08/09/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method.
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Affiliation(s)
- Huawei Tao
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Shuai Shan
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Hongliang Fu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Chunhua Zhu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Boye Liu
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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Dhakal P, Tayara H, Chong KT. An ensemble of stacking classifiers for improved prediction of miRNA-mRNA interactions. Comput Biol Med 2023; 164:107242. [PMID: 37473564 DOI: 10.1016/j.compbiomed.2023.107242] [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: 05/01/2023] [Revised: 06/21/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in regulating gene expression at the post-transcriptional level by binding to potential target sites of messenger RNAs (mRNAs), facilitated by the Argonaute family of proteins. Selecting the conservative candidate target sites (CTS) is a challenging step, considering that most of the existing computational algorithms primarily focus on canonical site types, which is a time-consuming and inefficient utilization of miRNA target site interactions. We developed a stacking classifier algorithm that addresses the CTS selection criteria using feature-encoding techniques that generates feature vectors, including k-mer nucleotide composition, dinucleotide composition, pseudo-nucleotide composition, and sequence order coupling. This innovative stacking classifier algorithm surpassed previous state-of-the-art algorithms in predicting functional miRNA targets. We evaluated the performance of the proposed model on 10 independent test datasets and obtained an average accuracy of 79.77%, which is a significant improvement of 7.26 % over previous models. This improvement shows that the proposed method has great potential for distinguishing highly functional miRNA targets and can serve as a valuable tool in biomedical and drug development research.
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Affiliation(s)
- Priyash Dhakal
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea.
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Bioactive Peptides in Human Health and Disease. Int J Mol Sci 2023; 24:ijms24065837. [PMID: 36982911 PMCID: PMC10055948 DOI: 10.3390/ijms24065837] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023] Open
Abstract
Bioactive peptides are defined as short amino acid sequences that may have specific physiological functions, ultimately affecting human health and protecting against the development of several diseases [...]
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