An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion
Copyright © 2024 Elsevier B.V. All rights reserved..
BACKGROUND: As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs.
RESULTS: A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models.
SIGNIFICANCE: The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:1298 |
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| Enthalten in: |
Analytica chimica acta - 1298(2024) vom: 15. März, Seite 342419 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Like [VerfasserIn] |
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| Links: |
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| Themen: |
Biologically active peptides |
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| Anmerkungen: |
Date Completed 12.03.2024 Date Revised 12.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.aca.2024.342419 |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 3 | |a An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion |
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| 500 | |a Date Revised 12.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs | ||
| 520 | |a RESULTS: A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models | ||
| 520 | |a SIGNIFICANCE: The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biologically active peptides | |
| 650 | 4 | |a Long short-term memory | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Sparrow search algorithm | |
| 650 | 4 | |a Virtual digested | |
| 650 | 7 | |a Trypsin |2 NLM | |
| 650 | 7 | |a EC 3.4.21.4 |2 NLM | |
| 650 | 7 | |a Peptides |2 NLM | |
| 650 | 7 | |a Peptide Hydrolases |2 NLM | |
| 650 | 7 | |a EC 3.4.- |2 NLM | |
| 700 | 1 | |a Li, Cong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tianlong |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Chaoshuang |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Qiuhong |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Lihua |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Yehua |e verfasserin |4 aut | |
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