Using AI to verify the successful purification and efficient medical activity of lectin from the marine giant kelp Eucheuma serra
Notice bibliographique
Résumé
The purpose of this study is to apply artificial intelligence (AI) technology to establish a model system that can effectively verify the purification success rate and medicinal activity potential of the marine giant alga Eucheuma serra lectin. Lectins are a class of proteins with sugar recognition capabilities that are widely found in seaweed. The large red algae in this study showed great potential in biomedical applications such as anti-tumor, anti-viral and immune regulation. The author's research on the purification and functional verification of lectins often consumes a lot of manpower and time, and its success rate is also affected by a variety of physical and chemical factors. This research paper shows through laboratory experiments that red algae has strong agglutination ability. The activity of seaweed extract will decrease after being stored at 4℃ or -20℃ for half a year. Taking Eucheuma as an example, the best time for enzymes to work is 2 to 6 hours. Although it is not a high temperature resistant variety, it can still maintain its original activity at 55℃. The ESA separated and purified by DEAE column contained 10.2 mg protein per 1 g dry weight. The molecular weight of the obtained algae lectin was determined to be 29000 by SDS-PAGE gel electrophoresis. The results of sugar inhibition assay showed that this lectin has affinity for a variety of monosaccharides and oligosaccharides. In terms of screening anti-cancer components from Eucheuma serrulata lectin, anti-cancer components were also discovered in the lectin of the large alga Eucheuma serrulata using AI methods. This study used the random forest algorithm as a validation object and combined it with more than 350 red algae protein information collected from public databases (such as Kaggle, UniProt, PDB, etc.) for feature modeling. After feature selection and model training, this AI model showed excellent performance on the test data set (average accuracy reached 86.3%, and F1-score and recall rate both reached above 0.84). Feature importance analysis indicated that it has a highly stable β-folded structure, which helps to maintain high activity under human physiological conditions. High hydrophilicity in terms of isoelectric point (acidic pI value, using neutral buffer) and hydrophobicity index is beneficial to maintaining solubility and structural stability. After purification under specific conditions, Eucheuma lectin has unlimited application potential in traditional Chinese and Western medicine, which will help to quickly screen lead compounds and develop high-efficiency natural medicine sources in the future. This cross-domain application also demonstrates the high value of AI technology in the fields of marine biological resource development, functional proteins and biomedical engineering, and lays a technical foundation for the transformation research of natural algae into medicines. Especially in medicine, it has high anti-tumor and anti-viral activity, and is very worthy of large-scale production and various applications.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».