A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides
Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders,...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1497307/full |
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| author | Maduravani Ramasundaram Honglae Sohn Thirumurthy Madhavan |
| author_facet | Maduravani Ramasundaram Honglae Sohn Thirumurthy Madhavan |
| author_sort | Maduravani Ramasundaram |
| collection | DOAJ |
| description | Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics. |
| format | Article |
| id | doaj-art-6ed56007575f4e7fa1af93e42f1627eb |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-6ed56007575f4e7fa1af93e42f1627eb2025-01-07T06:49:03ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.14973071497307A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptidesMaduravani Ramasundaram0Honglae Sohn1Thirumurthy Madhavan2Department of Genetic Engineering, Computational Biology Lab, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Chennai, IndiaDepartment of Chemistry and Department of Carbon Materials, Chosun University, Gwangju, Republic of KoreaDepartment of Genetic Engineering, Computational Biology Lab, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Chennai, IndiaCell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.https://www.frontiersin.org/articles/10.3389/frai.2024.1497307/fullcell-penetrating peptidesmechanismmachine learningrandom forestsupport vector machineartificial neural network |
| spellingShingle | Maduravani Ramasundaram Honglae Sohn Thirumurthy Madhavan A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides Frontiers in Artificial Intelligence cell-penetrating peptides mechanism machine learning random forest support vector machine artificial neural network |
| title | A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides |
| title_full | A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides |
| title_fullStr | A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides |
| title_full_unstemmed | A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides |
| title_short | A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides |
| title_sort | bird s eye view of the biological mechanism and machine learning prediction approaches for cell penetrating peptides |
| topic | cell-penetrating peptides mechanism machine learning random forest support vector machine artificial neural network |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1497307/full |
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