Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides
The blood–brain barrier (BBB) poses a significant obstacle to the administration of drugs to the brain for the development of therapies of central nervous system (CNS) disorders. Blood-brain barrier penetrating peptides (BBBPps) are a group of peptides that can traverse the BBB by different processe...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001208 |
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| author | Abhigyan Nath Sneha Pandey Kottakkaran Sooppy Nisar Anoop Kumar Tiwari |
| author_facet | Abhigyan Nath Sneha Pandey Kottakkaran Sooppy Nisar Anoop Kumar Tiwari |
| author_sort | Abhigyan Nath |
| collection | DOAJ |
| description | The blood–brain barrier (BBB) poses a significant obstacle to the administration of drugs to the brain for the development of therapies of central nervous system (CNS) disorders. Blood-brain barrier penetrating peptides (BBBPps) are a group of peptides that can traverse the BBB by different processes without causing harm to the BBB. These peptides show promise as potential drugs for CNS ailments. Nevertheless, the process of identifying BBBPps using experimental approaches is both time-consuming and labour-intensive. To discover additional BBBPps as potential treatments for CNS diseases, it is critical to develop insilico methods that can distinguish BBBPps from non- BBBPps rapidly and precisely. In the current work, machine learning aided models are developed for accurate prediction of BBBPps using physicochemical and evolutionary information. The best model achieved an accuracy of 84.8 % by using 5-fold cross-validation and 79.8 % based on the holdout testing set on a reduced set of features. Further an extensive analysis is carried out for the black box models using model agnostic interpretation approaches to infer the physicochemical and sequence space of BBBPps. Basic amino acids and conservation of Arginine and Lysine are found to be more favoured in BBBPps. Further, basic amino acid property group and its interactions with other features are found to be prominent important interactions. |
| format | Article |
| id | doaj-art-bb09b178661d4ff18b98f9bf79bb4dd9 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-bb09b178661d4ff18b98f9bf79bb4dd92024-12-15T06:14:46ZengElsevierEgyptian Informatics Journal1110-86652024-12-0128100557Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptidesAbhigyan Nath0Sneha Pandey1Kottakkaran Sooppy Nisar2Anoop Kumar Tiwari3Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur 492001, IndiaDepartment of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur 492001, IndiaDepartment of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaDepartment of Computer Science and Information Technology, Central University of Haryana, Mahendergarh 123031, India; Corresponding author.The blood–brain barrier (BBB) poses a significant obstacle to the administration of drugs to the brain for the development of therapies of central nervous system (CNS) disorders. Blood-brain barrier penetrating peptides (BBBPps) are a group of peptides that can traverse the BBB by different processes without causing harm to the BBB. These peptides show promise as potential drugs for CNS ailments. Nevertheless, the process of identifying BBBPps using experimental approaches is both time-consuming and labour-intensive. To discover additional BBBPps as potential treatments for CNS diseases, it is critical to develop insilico methods that can distinguish BBBPps from non- BBBPps rapidly and precisely. In the current work, machine learning aided models are developed for accurate prediction of BBBPps using physicochemical and evolutionary information. The best model achieved an accuracy of 84.8 % by using 5-fold cross-validation and 79.8 % based on the holdout testing set on a reduced set of features. Further an extensive analysis is carried out for the black box models using model agnostic interpretation approaches to infer the physicochemical and sequence space of BBBPps. Basic amino acids and conservation of Arginine and Lysine are found to be more favoured in BBBPps. Further, basic amino acid property group and its interactions with other features are found to be prominent important interactions.http://www.sciencedirect.com/science/article/pii/S1110866524001208Blood brain barrier penetrating peptidesSequence propertiesPhysicochemical spaceGradient boosting machinesXGboostModel agnostic interpretation methods |
| spellingShingle | Abhigyan Nath Sneha Pandey Kottakkaran Sooppy Nisar Anoop Kumar Tiwari Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides Egyptian Informatics Journal Blood brain barrier penetrating peptides Sequence properties Physicochemical space Gradient boosting machines XGboost Model agnostic interpretation methods |
| title | Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides |
| title_full | Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides |
| title_fullStr | Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides |
| title_full_unstemmed | Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides |
| title_short | Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides |
| title_sort | gaining insights into the physicochemical properties and sequence space of blood brain barrier penetrating peptides |
| topic | Blood brain barrier penetrating peptides Sequence properties Physicochemical space Gradient boosting machines XGboost Model agnostic interpretation methods |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001208 |
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