PTML modeling for peptide discovery : in silico design of non-hemolytic peptides with antihypertensive activity
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG..
Hypertension is a medical condition that affects millions of people worldwide. Despite the high efficacy of the current antihypertensive drugs, they are associated with serious side effects. Peptides constitute attractive options for chemical therapy against hypertension, and computational models can accelerate the design of antihypertensive peptides. Yet, to the best of our knowledge, all the in silico models predict only the antihypertensive activity of peptides while neglecting their inherent toxic potential to red blood cells. In this work, we report the first sequence-based model that combines perturbation theory and machine learning through multilayer perceptron networks (SB-PTML-MLP) to enable the simultaneous screening of antihypertensive activity and hemotoxicity of peptides. We have interpreted the molecular descriptors present in the model from a physicochemical and structural point of view. By strictly following such interpretations as guidelines, we performed two tasks. First, we selected amino acids with favorable contributions to both the increase of the antihypertensive activity and the diminution of hemotoxicity. Then, we assembled those suitable amino acids, virtually designing peptides that were predicted by the SB-PTML-MLP model as antihypertensive agents exhibiting low hemotoxicity. The potentiality of the SB-PTML-MLP model as a tool for designing potent and safe antihypertensive peptides was confirmed by predictions performed by online computational tools reported in the scientific literature. The methodology presented here can be extended to other pharmacological applications of peptides.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Molecular diversity - 26(2022), 5 vom: 20. Okt., Seite 2523-2534 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kleandrova, Valeria V [VerfasserIn] |
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| Links: |
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| Themen: |
Amino Acids |
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| Anmerkungen: |
Date Completed 06.10.2022 Date Revised 06.10.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11030-021-10350-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Hypertension is a medical condition that affects millions of people worldwide. Despite the high efficacy of the current antihypertensive drugs, they are associated with serious side effects. Peptides constitute attractive options for chemical therapy against hypertension, and computational models can accelerate the design of antihypertensive peptides. Yet, to the best of our knowledge, all the in silico models predict only the antihypertensive activity of peptides while neglecting their inherent toxic potential to red blood cells. In this work, we report the first sequence-based model that combines perturbation theory and machine learning through multilayer perceptron networks (SB-PTML-MLP) to enable the simultaneous screening of antihypertensive activity and hemotoxicity of peptides. We have interpreted the molecular descriptors present in the model from a physicochemical and structural point of view. By strictly following such interpretations as guidelines, we performed two tasks. First, we selected amino acids with favorable contributions to both the increase of the antihypertensive activity and the diminution of hemotoxicity. Then, we assembled those suitable amino acids, virtually designing peptides that were predicted by the SB-PTML-MLP model as antihypertensive agents exhibiting low hemotoxicity. The potentiality of the SB-PTML-MLP model as a tool for designing potent and safe antihypertensive peptides was confirmed by predictions performed by online computational tools reported in the scientific literature. The methodology presented here can be extended to other pharmacological applications of peptides | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Antihypertensive | |
| 650 | 4 | |a Artificial neural network | |
| 650 | 4 | |a Hemotoxicity | |
| 650 | 4 | |a Multilayer perceptron | |
| 650 | 4 | |a PTML | |
| 650 | 4 | |a Peptide | |
| 650 | 4 | |a Topological indices | |
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| 700 | 1 | |a Speck-Planche, Alejandro |e verfasserin |4 aut | |
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