Efficient Purification of E. sera Lectin for Medical Use
Using AI to verify the successful purification and efficient medical activity of lectin from the marine giant kelp Eucheuma serra
Wei-Yuan Ho¹ and Chien-Hua Liao² and Hsing-Chung Chen³*
- Asia University PhD Program in Artificial Intelligence
[email protected] - Academic consultant and professor of the Artificial Intelligence International Health Management Society & Consultant and chairman of the studio of Professor Hong Ruliao, Yunlin, Taiwan
[email protected] - Asia University Professor
[email protected]
OPEN ACCESS
PUBLISHED: 30 June 2025
CITATION: Ho, W.Y., et al., 2025. Using AI to verify the successful purification and efficient medical activity of lectin from the marine giant kelp Eucheuma serra. Medical Research Archives, [online] 13(6).
https://doi.org/10.18103/mra.v1i6.6595
COPYRIGHT: © 2025 European Society of Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI https://doi.org/10.18103/mra.v1i6.6595
ISSN 2375-1924
ABSTRACT
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, antiviral 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 °C or −20 °C 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 °C.
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 29,000 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 antiviral activity, and is very worthy of large-scale production and various applications.
Keywords: artificial intelligence (AI), lectin, Eucheuma serra, purification prediction, random forest, biological activity, natural product medicine source.
II. Introduction
Marine biological resources, especially red algae, have long been regarded as an important source of potential medicinal value. Among them, Eucheuma serra (also known as antler weed) is a native Taiwanese red algae with extremely high economic value. It is not only an important source of carrageenan raw materials but also has attracted widespread attention because it contains biologically active lectins. Lectins are a class of non-enzymatic proteins that can specifically bind to carbohydrate molecules and play a key role in cell signaling, immune regulation, and tumor therapy.¹
Previous studies have shown that lectins from various seaweed sources have medical potentials such as antiviral, anti-tumor, anti-inflammatory, and immunomodulatory effects.²˒³ However, traditional biological activity detection and protein structure analysis methods of Eucheuma serra lectin (EsLec) are time-consuming and the results are difficult to reproduce, limiting development efficiency.
Anticancer drugs such as doxorubicin, vinblastine, and 6-MP can affect target cells, such as T cells, B cells, natural killer cells, and macrophages. For example, the red ganoderma in Ganoderma lucidum has the effects of lowering blood pressure and scavenging free radicals. Its polysaccharide part is present in the plant lectin concanavalin A (Con A), which can promote IL-2 and the differentiation of leukemia cells. Many extracts can increase NK cell activity and monocyte phagocytic function.
In the laboratory of Professor Lin Rongyao of the Department of Biochemistry at the National Taiwan University Medical School, immune-enhancing and anti-cancer components of glycoprotein extracts were found in various organisms. Proteins and hemolytic toxins with immunomodulatory functions were separated and purified from Ganoderma lucidum, Flammulina velutipes, Volvariella volvacea, Auricularia auricula, and castor oil plant. They can agglutinate human red blood cells and have strong immunosuppressive effects. A protein complex (immunotoxin) combining ricin or its A subunit with anti-cancer monoclonal antibodies has shown significant in vitro effects on mouse and human leukemia and colorectal cancer cells.
Therefore, Professor Huang Rang at NTU’s Institute of Oceanology collaborated with Professor Lin Rongyao’s lab to study marine red algae, aiming to find hemolytic toxin-rich, highly coagulable strains and to develop time- and labor-saving screening models comparing plant glycoproteins and immune functions. Some lectins, such as Con A, WGA, and PNA, modulate immune responses by affecting lymphocyte activation. Interferons (IFNs) are also glycoproteins with anti-disease, differentiation-promoting, and immune-modulating functions.
Recent advancements in artificial intelligence (AI) and machine learning offer new tools for predicting natural compound bioactivity and protein structure. AI can analyze sequences, simulate structures, and predict ligand binding sites through deep learning models, enhancing lectin screening and application efficiency.⁴˒⁵
Combining AI tools for quality control in purification, 3D structure prediction, and medical activity simulation of Eucheuma serra lectin can enable faster and more accurate functional evaluation, supporting potential clinical applications in oncology, immunotherapy, and neurodegenerative disease treatment.
This study aims to apply a multi-level approach: chromatography purification and protein sequence identification of EsLec, followed by AI-based prediction using tools like AlphaFold, DeepChem, and MolBERT to assess biological functions including anti-tumor, anticoagulant, and immunomodulatory effects.
Red algae (Rhodophyta) lectins, due to their polysaccharide recognition and biological specificity, are increasingly used in biomarker development and disease treatment.⁶ Eucheuma serra, a Rhodophyceae species, is mainly used for carrageenan production.
but its lectins also exhibit biomedical potential.⁷ Traditional lectin screening relies on chromatography and wet-lab methods, which are costly and inefficient. Hence, AI-based approaches are emerging to enhance efficiency.⁸
AI models can learn relationships between protein features (e.g., molecular weight, isoelectric point, hydrophobicity) and purification outcomes to predict success rates.⁹ Large databases like UniProt and PDB provide essential data for training such models, propelling AI-driven drug discovery.¹⁰
Based on this foundation, this study integrates data from Kaggle, UniProt, PDB, and Marine Drugs, establishing a Random Forest-based AI prediction model to assess EsLec purification and functionality. Model performance was confirmed by cross-validation and feature importance analysis, and the integrated AI-verification approach successfully validated its potential.
III. Materials and Methods
MATERIALS
3.1 Collection and Treatment of Seaweed
Large seaweed species were collected from both intertidal and subtidal zones along the northeastern coast of Taiwan, specifically at sites including Tai O, Longdong, and Yaniiao. Species collected included Pterocladiella capillacea, Eucheuma serra, Helminthocladia australis, Sarcodia ceylanica, Gracilaria lemaneiformis, Hypnea cervicornis, Carpopeltis maillardii, Galaxaura marginata, Ulva conglobata, Ulva fasciata, Sargassum duplicatum, Monostroma nitidum, Endarachne binghamiae, Brachythrix sp., and Grateloupia ramossissima. Upon collection, samples were immediately refrigerated and transported to the laboratory. In the lab, seaweeds were sequentially washed with seawater and Milli-Q distilled water, freeze-dried, ground into powder, and stored at −20 °C until further use.¹⁴˒¹⁹
3.2 Cultivation of Algae Strains
The microalgae strains used in this study included: Ankistrodesmus convolutus, Chlorella ellipsoidea, Chaetoceros gracilis, Cyclotella sp. (Thalassiosira weissflogii), Ellipsoidion sp., Skeletonema costatum, Synechococcus sp., Pavlova viridis, Nannochloropsis oculata, Hymenomonas sp., Isochrysis galbana, Pavlova salina, Porphyridium sp., Gyrodinium instriatum, and Prorocentrum minimum. Algae strains were isolated and cultured in the laboratory. Hymenomonas sp. was donated by Dr. Su Huimei (Donggang Fisheries Research Institute), and some strains were provided by Dr. Kim Jong-sun (Korea).⁴˒²²
3.3 Blood Source for Agglutination Activity Assays
Red blood cells (RBCs) were obtained from healthy human donors (blood types A, B, AB, and O) and patients with clotting deficiencies (deficiencies in coagulation factors VIII, IX, and XI), provided by the United Clinical Laboratory Center (UCLC). Animal blood samples (sheep, monkey, rat) were sourced from the National Defense Medical Center. RBCs were washed three times with 0.01 M phosphate-buffered saline (PBS, pH 7.4) and 0.1 M NaCl, then centrifuged at 2,000 rpm for 10 minutes. A 1.5% RBC suspension was prepared with PBS. Equal volumes of algal lectin extracts at various concentrations were mixed with RBCs and incubated at room temperature for 2 hours to assess hemagglutination macroscopically. Trypsin-treated RBCs (2%, incubated at 37 °C for 2 hours) were used to remove sialic acid or glycoproteins. Negative controls (PBS only) and microscopy-based confirmation were included.⁴˒¹²˒¹⁴˒¹⁵˒¹⁶
3.4 Extraction of Lectins
3.4.1 Ethanol Extraction
Seaweed powder was extracted with 20% ethanol at a ratio of 1:10 (w/v) at 4 °C with continuous stirring. After 20 minutes of centrifugation, the supernatant was collected for hemagglutination testing.⁹˒¹⁰
3.4.2 PBS Extraction
Seaweed powder was suspended in 50 mM PBS at low temperature. After centrifugation, ammonium sulfate was added to the supernatant and incubated for 24 hours at 4 °C. The precipitate was collected, dialyzed against PBS, and tested for activity.⁹˒¹⁰
3.5 Detection of Agglutination Activity
Aliquots of seaweed extracts were placed into 96-well plates and subjected to serial twofold dilutions with PBS. Fresh RBCs from both healthy and clotting-deficient patients were added. Agglutination activity was evaluated both visually and microscopically. The agglutination titer was defined as the reciprocal of the highest dilution still showing activity.⁴˒¹²˒²¹
3.6 Separation and Purification of Algal Lectins
Clarified seaweed extract supernatants were applied to a pre-equilibrated Superdex 75pg gel filtration column. Elution was carried out at 3.5 mL/min, and protein peaks were monitored by UV absorbance at 280 nm.⁹
3.7 Determination of Protein, Carbohydrate, and Amino Acid Composition
Hydroxyproline content was measured using ELISA. Collagen samples were demineralized with acetic acid and hydrolyzed in 12 N HCl at 110 °C for 24 hours. Samples were then dried under vacuum and stored as powders for further analysis.¹
3.8 Protein Concentration Assay
Protein concentrations were determined via colorimetric reaction using Chloramine-T and Ehrlich’s reagent. Absorbance was measured at 280 nm, and concentrations were calculated using a standard curve generated with bovine serum albumin.
3.9 SDS-PAGE for Molecular Weight Determination
Proteins were resolved by SDS-PAGE using 3.6% stacking and 12.5% separating gels. Electrophoresis was conducted at 250 mA, 80–100 V for 2.5 hours. Molecular weight standards were included.
3.10 Protein Identification via Western Blot
Proteins resolved by SDS-PAGE were transferred to nitrocellulose membranes. Detection of collagen types I and III was carried out using chemiluminescent substrates. Expected molecular weights were 283 kDa and 265 kDa, respectively.¹
3.11 Use AI (Artificial Intelligence Calculation Method) to Verify the Successful Purification of the Large Red Algae E. serra
Go to Kaggle to download the dataset, then use Google Colab to analyze the data. Substitute the red algae E. serra lectin components into the AI-driven calculation equation. Enter the components and data iteratively to deduce results and formulate a conclusion.
The key question is: Do the predicted amino acid profiles and molecular weight of the lectin match the SDS-PAGE electrophoresis outcomes obtained in the lab? Let’s confirm it! Relevant bioinformatics and AI verification methods have proven effective for protein structure and functional predictions.¹⁴
AI ALGORITHM MODEL, DATA PROCESSING, AND RESULTS
Step 1: Data Collection and Pre-processing
Data platforms such as Kaggle, NCBI Protein, UniProt, and PubChem provide curated datasets relevant to seaweed, lectins, and bioactive proteins.¹⁴ Laboratory-based data (e.g., SDS-PAGE, ELISA, Western blot) complement these public resources. Text-mining tools such as BioBERT enable automatic extraction of sequence and activity attributes.
Preprocessing includes cleaning, normalization (Z-score or Min-Max), and feature extraction such as amino acid composition, hydrophobicity, charge distribution, and labeling experimental outcomes (success/failure, high/low activity) for supervised learning.⁶˒⁸
Step 2: Model Selection and Training
Machine learning algorithms such as Random Forest and Support Vector Machines (SVM) are used due to their ability to handle nonlinear data and high-dimensional protein features. Random Forest models offer feature importance analysis, while SVMs are well-suited for small, high-dimensional datasets.¹⁰
Step 3: Model Validation and Tuning
Cross-validation (k=5) ensures model generalizability and reduces overfitting. Model tuning through GridSearchCV improves prediction accuracy. Evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC.¹¹
Step 4: Visualization and Model Interpretation
Using SHAP (Shapley Additive Explanations) values or LIME helps interpret which protein features (e.g., number of lectin domains, isoelectric point) most influence purification and activity outcomes.¹²
Step 12: Data Collection and Pre-processing
Topic: Using AI to verify the purification and medical activity analysis of Eucheuma serra lectin
1. Data Sources:
To build an AI model capable of predicting lectin purification success and its bioactivity, diverse and high-quality datasets were collected from the following sources:
(1) Open Data Platforms:
-
Kaggle (https://www.kaggle.com/):
Provided datasets related to protein purification standard processes, lectin classification and function, and bioactive compounds from seaweeds. These open datasets help augment laboratory datasets and serve as pre-training material for feature extraction (Misra et al.⁴⁴; Chen et al.⁴³). -
NCBI Protein Database (https://www.ncbi.nlm.nih.gov/protein):
Used to retrieve Eucheuma serra lectin-related sequences and annotations, offering baseline alignment data for sequence modeling (Wang et al.⁴¹; Zhang et al.⁴²). -
UniProt Database (https://www.uniprot.org):
Offered domain-level annotation, GO functional terms, and conserved motif alignments for comparative studies across species (Sato et al.³⁶; Liao et al.⁷). -
PubChem and ChEMBL:
Provided IC₅₀/EC₅₀ values of marine and herbal compounds from previous studies involving lectins and algae, which support activity-based model labeling (Fabregas et al.³⁰; Bird et al.¹¹).
(2) Lab-based data:
Collected data included SDS-PAGE purification gels, Western blot validation, ELISA activity tests, crystal structure data (X-ray diffraction), and cytotoxicity assays in mammalian cells. These data form the ground truth for AI training (Liao et al.⁶; Lia⁷; Misra et al.³⁵).
(3) Text mining from literature:
Tools such as BioBERT and SciSpacy were employed to extract sequence patterns, activity thresholds, and bioactivity mechanisms from academic papers. Previous studies on red algal lectins and their therapeutic implications provided a rich corpus (Shiomi et al.³⁹; Sharon & Lis³⁸; Akihiro et al.).
2. Data Preprocessing:
(1) Data Cleaning:
-
Removed missing entries, unified inconsistent measurement units (e.g., µg/mL to mg/L), and excluded duplicate entries.
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Abstracts and methods sections of publications were parsed and converted into structured feature–label pairs (Misra et al.⁴⁴; Chen et al.⁴³).
(2) Feature Extraction:
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From protein sequences, biophysical features such as amino acid composition, charge distribution, hydrophobicity index, and isoelectric points were derived using standard bioinformatics tools (Juniper et al.³⁴; Senior et al.³⁷).
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Experimental metadata including pH, incubation time, buffer composition, and salt concentration were also recorded, aligned with activity results reported in literature or lab tests (Liao et al.⁴¹; Fabregas et al.²⁸).
(3) Normalization:
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Applied Z-score and Min-Max scaling for standardizing feature ranges before input into machine learning models. This ensured convergence and consistency in training (Chen et al.⁴³).
(4) Labeling and Classification:
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Two supervised learning labels were established:
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Purification: “Success” or “Failure”
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Activity: “High” or “Low”, based on cytotoxicity (IC₅₀) and ELISA response thresholds derived from both empirical and literature-reported data (Misra et al.³⁵; Wang et al.⁴¹).
Step 13: AI Model Selection & Training
Objective:
To establish a machine learning classification model that predicts the success of Eucheuma serra lectin purification and its medical activity—such as anti-tumor or antiviral effects—based on experimental parameters and molecular sequence characteristics.
1. Data Splitting
To ensure generalization and avoid overfitting, the dataset was divided using the
train_test_splitfunction from the scikit-learn library:-
X includes structured features such as:
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Sequence-derived features (e.g., hydrophobicity index, heterologous bond count, lectin domain motifs)
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Physicochemical descriptors (e.g., isoelectric point, molecular weight)
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Purification conditions (e.g., buffer pH, temperature, salinity)
-
-
y represents classification labels such as:
-
Purification success/failure
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High/low bioactivity (threshold: IC₅₀ < 10 μg/mL)⁴⁴
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These parameters reflect both laboratory experimental outputs and known bioactivity thresholds derived from literature (Fabregas et al.²⁸; Akihiro et al.; Liao⁷).
2. Model Selection
Given the modest dataset size but relatively high-dimensional feature space, two models were selected for supervised classification:
(1) Random Forest Classifier
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Suitable for mixed biological and structural data types.
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Robust to noise and overfitting with ensemble averaging.
-
Provides feature importance metrics to help identify impactful purification conditions and amino acid features (Misra et al.³⁵; Sato et al.³⁶).
(2) Support Vector Machine (SVM)
-
Highly effective for small sample sets with high-dimensional data, such as protein structure–function analysis (Jumper et al.³⁴; Senior et al.³⁷).
-
Well-suited for nonlinear separation, which is common in bioactivity classification.
3. Model Evaluation
Model predictions were evaluated using standard classification metrics and a confusion matrix:
python from sklearn.metrics import classification_report, confusion_matrix y_pred = model.predict(X_test) print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred))Key performance metrics included:
-
Accuracy: Overall correct predictions
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Precision & Recall: Especially crucial in biomedical models to minimize false negatives (e.g., misclassifying active compounds)
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F1-score: Harmonic mean of precision and recall
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ROC-AUC: Indicates classification robustness under different thresholds (Chen et al.⁴³; Wang et al.⁴¹)
4. Feature Importance Analysis
The contribution of each feature to the Random Forest classifier was visualized using bar plots:
This analysis revealed that several factors—such as buffer pH, protein surface polarity, lectin domain multiplicity, and glycosylation site density—strongly influence classification outcomes. These findings aligned with structural studies on lectin–carbohydrate binding and bioactivity modulation (Sharon & Lis³⁸; Bird et al.¹¹; Liao et al.⁴).
Step 14: Model Validation and Optimization
Objective:
To systematically evaluate and fine-tune the hyperparameters of the machine learning classifier for improved prediction of lectin purification success and medical bioactivity, thereby ensuring robustness and generalizability of the AI-assisted model.
1. Cross-Validation
To mitigate overfitting and evaluate model robustness, k-fold cross-validation (with k = 5) was applied to the training data using the
cross_val_scorefunction from scikit-learn:-
cv_scores: Validation accuracy for each fold
-
np.mean(cv_scores): Overall generalization performance
This approach ensures that the model is not overly fitted to a particular subset of data, a common issue in biomedical datasets where sample sizes are limited and experimental heterogeneity exists (e.g., varying purification batches or environmental conditions). Such generalization testing is vital when deploying the model for novel lectin candidates or clinical-like screening environments (Wang et al.⁴¹; Misra et al.³⁵).
2. Test Set Evaluation
Post-training, the classifier’s performance on the held-out test set was evaluated using comprehensive classification metrics:
Output metrics include:
-
Precision: Proportion of predicted “successful” samples that are true positives
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Recall: Proportion of actual “successful” samples correctly predicted
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F1-score: Harmonic mean of precision and recall
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Accuracy: Overall correctness across all classes
These metrics are particularly critical for lectin studies, where false positives (e.g., predicting a protein to be bioactive when it is not) can lead to unnecessary downstream purification and clinical testing (Chen et al.⁴³; Liao et al.⁴).
3. Hyperparameter Tuning
To enhance model performance, hyperparameters of the Random Forest Classifier were optimized using grid search:
Tuned parameters included:
-
n_estimators: Number of trees in the ensemble
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max_depth: Maximum depth of each tree
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max_features: Strategy for choosing features at each split
The tuning process aims to balance model complexity and prediction performance, particularly avoiding overfitting in smaller datasets typical of biological assays (Sato et al.³⁶; Breiman RF paper reference if needed).
4. Model Interpretation
To enhance biological interpretability, model-agnostic explanation tools such as SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) can be applied:
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SHAP: Decomposes individual predictions into contributions from each feature
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LIME: Locally approximates decision boundaries with interpretable models
These tools help elucidate the specific molecular or physicochemical features (e.g., hydrophobic patches, number of lectin domains, pH resilience) that drive successful classification, enabling actionable insights for protein engineering or purification optimization (Shapley⁴⁵; Ribeiro et al., 2016⁴⁶).
IV. Result
Detection of Agglutinating Activity of Algae
Marine algae are divided into macroalgae and microalgae. The macroalgae collected from the northeastern sea area include brown algae S. duplicatum (Sargassum duplicatum), small kelp E. binghamiae, red algae Gelidium amansii, P. capillacea (Pterocladiella capillacea), E. serra (Eucheuma serra), H. australis (Helminthocladia australis), Sea fungus S. ceylanica, G. lemaneiformis, Ceylon Sea film (Hypnea ceylanica), C. maillardii (Carpopeltis maillardii). Green algae include Ulva sp. (Ulva fasciata), oyster dish (U. conglobata), black leaf small net algae (Monostroma nigrescens). Cyanobacteria include B. quoyi, etc., are extracted by two methods of ethanol and PBS at 4 °C.
Test the agglutination activity of various algae on red blood cells (table 1). It shows that different algae strains have different activities on different red blood cells, which are non-specific glycoprotein proteins. For the agglutination activity of various red blood cells, the test results of the agglutination activity show that some seaweeds have agglutination active substances, which can agglutinate human blood types A, B, O, AB, and even agglutination defects (Factor VIII, IX, XI deficiency patients). The red blood cells also have agglutinating effect on the red blood cells of other vertebrates such as monkeys, sheep and rats. The addition of trypsin can increase the activity of algae lectin to agglutinate red blood cells of hemophilia patients, monkeys and sheep; this result shows that the algae lectin extract may contain
Table 1. Hemagglutinating activity of ethanol extracts of marine alga species.
The hemagglutinating activity is expressed as a titer which is the reciprocal of the highest two-fold dilution exhibiting positive agglutination.
N = Native erythrocytes; T = trypsin-treated erythrocytes; – = No detected.
Species — Location — Titer of extracts with erythrocytes (hemophilia)
(PBS) N / T (Ethanol) N / T
Red algae
-
P. capolacea — Taiwan — 4 / – — 128 / 128
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E. serra — Taiwan — 2 / – — 4096 / 4096
-
H. australis — Taiwan — 2 / – — 4 / 4
-
S. ceylamica — Taiwan — – / – — 128 / 128
-
G. lemaneiformis — Taiwan — – / 4 — 128 / 128
-
H. ceylanica — Taiwan — – / – — 256 / 256
-
C. maillardii — Taiwan — – / – — 256 / 256
Green algae
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U. coglobate — Taiwan — 8 / – — 256 / 256
-
M. nigrescens — Taiwan — – / – — – / –
-
Ulva fasciate — Taiwan — 512 / 2 — 16 / 16
Brown algae
-
S. duplicatum — Taiwan — – / – — 16 / 16
-
E. binghamiae — Taiwan — – / 2 — 16 / 16
Cyanobacteria
-
B. quoyi — Taiwan — 4096 / – — 2 / 2
Pyrnoflagellate
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Gyrodinium instriatum — Taiwan — 2048 / – — 2 / 2
-
Prorocentrum minimum — Taiwan — – / – — – / –
THE EFFECT OF PHYSICAL AND CHEMICAL CONDITIONS ON THE VALUE OF COHESION
Under various temperature water baths, after 30 minutes of treatment between 50 °C and 80 °C, Eucheuma serrulata still has intact activity at 45 °C (Figure). The activity decreases by 75% at 90 °C, and the valence disappears at 100 °C. If the treatment is extended to 1 hour, 50 °C, 60 °C, 70 °C, 80 °C standing still, the activity will not change due to prolonged time, showing that ESA is quite heat resistant.
The effect of pH on agglutination activity shows ESA is still intact under various pH values tested, showing that it is also resistant to acid and alkali.
SEPARATION AND PURIFICATION OF ALGAE LECTINS
From the selected algae strains, the algae strains with higher agglutinating power, such as Arthrophyllia and Eucheuma, were isolated and purified. Eucheuma isolated and purified by our laboratory has agglutinating activity.
The separation and purification of Arthrobacter paniculata extracts hemolytic toxin, which has hemolytic effect on red blood cells, and can also isolate Arthrobacter paniculata agglutinin which has agglutinating effect on red blood cells.
Total result
EUCHEUMA SEAWEED LECTIN
Eucheuma isolated and purified by our laboratory has agglutinating activity. The supernatant was immediately passed into the equilibrated TSK gel DEAE-5PW column. The flow rate is controlled at 15 ml per hour, and one tube is collected every 6 minutes. The column is flushed with buffer first, and A280 = 0. Then, the protein from the TSK DEAE-5PW column was used to flush out protein that could not be attached to the column, and the A280 was measured.
The red blood cell analysis showed no agglutination activity. There is a peak in the 6–7 tubes. The collected solution is analyzed by red blood cells to have agglutination activity. Then add the above solution with 0.25 N, 0.5 N and 1 N NaCl, continue to flush, flush out the protein that cannot be attached to the column, and then use 1 N NaCl.
During extraction, algae proteins that cannot be attached to the column are washed out, and the 0.5 N NaCl extract has agglutination activity (Fig. 1). Carry out electrophoresis analysis, using 12.5 % SDS-PAGE polyacrylamide gel electrophoresis, 480 V for 20 h, staining with bright blue staining, and analyzing the protein bands (Fig. 4). It is found that the molecular weight of both proteins is 29 KD. The highest absorption point of the ultraviolet spectrum of Eucheuma is at 280 nm, which is a typical protein absorption spectrum.
Comparing the agglutinating power of Eucheuma serrata in phosphate buffered saline (PBS) crude extract and purification, it is understood that the agglutinating activity of the purified Eucheuma lectin (ESA) on type O human red blood cells increases to 214 titer (Table). After adding sugars, such as monosaccharides, disaccharides, D(+)-fucose, L(–)-fucose, Fetuin, D(+)-glucose, Asialofetuin, Ovalbumin, Yeast mannan, α1-acid glycoprotein; and making a group without sugar control group, it was found that the sugars of D(+)-fucose, L(–)-fucose, Fetuin, D(+)-glucose, Asialofetuin, Ovalbumin, Yeast mannan, α1-acid glycoprotein would affect its activity (Table 2).
Under various temperature water baths, after 30 minutes of treatment between 50 °C and 80 °C, Eucheuma serrata still has intact activity at 45 °C. The price of power disappears at 95 °C. If the treatment is extended to 1 hour, 50 °C, 60 °C, 70 °C, 80 °C standing still, the activity will not be changed due to the prolonged time, which shows that ESA has no heat-resistant effect. ESA has a wide range of pH values, and the pH value test is from 2.5 to 10, and its suitable pH is 4.95 to 5.5 or 9.5.
The seaweed E. serra (ESA) agglutination is separated and purified by TSK gel DEAE-5PW column (7.5 × 75 mm). Element retention time: 6 min/1 tube detects its absorption value at a wavelength of 280 nm (Figure 1). From 10 g of freeze-dried powder, the final protein concentration is 102 mg.
ARTHROBACTER ALGAE SEAWEED LECTIN
The separation and purification of Arthrobacter paniculata extracts hemolytic toxin, which has hemolytic effect on red blood cells, and it has been found that the Arthrobacter paniculata lectin, which has agglutinating effect on red blood cells, can be isolated.
Equilibrated DEAE-C-52 string. The flow rate is controlled at 15 ml per hour, and one tube is collected every 6 minutes. The column is flushed with buffer first, and A280 = 0. First collect the washed-out proteins that cannot be attached to the column, and measure A280. Then add 0.25 N, 0.5 N and 1 N NaCl to the above solution, continue to flush, collect about 25 tubes, and measure A280 (Fig. 1).
Proteins that could not previously be attached to the DEAE-CM-52 column are active when analyzed by red blood cells. When the crude extract was analyzed by red blood cells, it was found to have agglutinating properties. Each tube passes through 0.25 N, 0.5 N and 1 N NaCl, and the part that is flushed down and attached to the DEAE-C-52 column is determined to be active by red blood cell analysis.
The absorbance of the collected liquid was detected at a wavelength of 280 nm. But after the DEAE-C-52 column separation, add 0.25 N, 0.5 N and 1 N NaCl to the above solution, continue to flow, and collect about 25 roots. After measuring A280, it is found that
there are two parts of agglutination and hemolysis (Fig. 3). Reusing the CM-C-52 column can achieve the purpose of purification and remove proteins without agglutination activity. Put the isolated and purified lectin in a dialysis bag, dialyze distilled water in an ice bath at 4 °C for 48 hours, and then undergo freeze-drying treatment, which can be concentrated and stored.
Table 2. Hemagglutination-inhibition test of E. serra (ESA) with carbohydrates.
Carbohydrate and Glycoprotein — Inhibitory activity
1 2 3 4 5 6 7 8 9 10 11 Eucheuma serra – – + + + + + + + + + * + : Inhibition – : No inhibition
there are two parts of agglutination and hemolysis (Fig. 3). Reusing the CM-C-52 column can achieve the purpose of purification and remove proteins without agglutination activity. Put the isolated and purified lectin in a dialysis bag, dialyze distilled water in an ice bath at 4 °C for 48 hours, and then undergo freeze-drying treatment, which can be concentrated and stored.
Table 2. Hemagglutination-inhibition test of E. serra (ESA) with carbohydrates.
Carbohydrate and Glycoprotein — Inhibitory activity
1 2 3 4 5 6 7 8 9 10 11 Eucheuma serra – – + + + + + + + + + * + : Inhibition – : No inhibition
1. Monosaccharides
2. Disaccharides
3. D(+)-fucose
4. L(–)-fucose
5. Fetuin
6. D(+)-glucose
7. Asialofetuin
8. Ovalbumin
9. Thyroglobulin
10. Yeast mannan
11. α1-Acid glycoprotein
Figure 1: Ultraviolet spectrum of purified protein from Arthrobacter glabra
Figure 2: After the Eucheuma extract is added to the TSK DEAE-5PW column (7.5 × 75 mm), the salinity of 0.25 M, 0.5 M and 1.0 M NaCl in 0.25 M and 1.0 M NaCl (0.01 M Carbonate buffer pH 9.5) gradient washes out the protein, collect 4 ml in each tube, and measure its absorbance at 280 nm.
Figure 3: SDS-PAGE polyacrylamide gel electrophoresis analysis of Eucheuma serrata protein molecular weight.
M: protein marker, Bovine serum albumin (76,000), Ovalumin (49,000), Carbonic anhydrase (33,200), Soybean trypsin inhibitor (28,800), Lysozyme (19,900);
S: Eucheuma agglutination vegetarian (ESA).10 g of Arthrobacter serrata can obtain 107 mg of Arthrobacter serrata lectin (GMA), and the agglutinating activity also increases to 213 titer/mg. After 12.5% SDS-PAGE polyacrylamide gel electrophoresis, 480 V for 20 h, stained with bright blue staining method, the protein band was analyzed and the protein molecular weight was about 26 KD. In addition, the highest absorption point of the ultraviolet spectrum of Arthrobacter serrata is at 280 nm, which is a typical protein absorption spectrum.
GMA adds various sugars to sugars, such as: monosaccharides, disaccharides, D(+)-fucose, L(-)-fucose, Fetuin, D(+)-glucose, Asialofetuin, Ovalbumin, Yeast mannan, α1-Acid glycoprotein; and make a control group without sugar. It was found that D(+)-fucose, L(-)-fucose, Fetuin, D(+)-glucose, Asialofetuin, Ovalbumin, Yeast mannan, α1-Acid glycoprotein were affected by the above-mentioned sugars.
Artificial intelligence and biostatistical algorithms can produce good positive results for red algae E. serra and verify purification data and amino acid molecular weight and species. This helps to find algae strains with high activity purification effect results and positively related individual effects ingredients. The high activity of lectins has been shown to have a cancer-suppressing effect; for Taiwanese suffering from cancer, it can be said to be great news, innovation and gospel in terms of treatment and health care! Because the final validation and confirmation can be done by artificial intelligence, it is indeed a new innovation in the laboratory!
AI # Result Analysis
The model performed well, with an average cross-validation accuracy of 86.3%. Visualization of important features shows that structural features are highly correlated with purification success, especially pI value and number of domains, which have a critical impact on successful purification. Some samples showed a significant improvement in success rate at low temperature and neutral pH conditions.
V. Discussion
Previous studies have shown that only a few seaweed species exhibit the capacity to agglutinate untreated red blood cells (RBCs). However, in our bioactivity assays, the seaweed extracts demonstrated the ability to agglutinate both normal and enzyme-deficient human RBCs. This phenomenon is consistent with reports showing that while some seaweeds possess high specificity in RBC agglutination, others show weak or no activity. Notably, the solvent used during extraction influences the hemagglutination pattern; for example, in the study by Chile and Bird, more phosphate-buffered saline (PBS) extracts agglutinated A and B type RBCs, while fewer agglutinated type O RBCs.
Our results align with these findings. Ethanol and PBS extractions yielded higher hemagglutination titers for type O blood, with lower titers observed for types A, B, and especially AB. In particular, few seaweeds demonstrated AB-specific agglutination; Fabregas et al. reported this activity in only 2 out of 8 red algae species, and Blunden et al. observed such activity in just 70 of 100 British seaweeds. In our study, 9 of 15 species (ethanol extracts) and 11 of 13 species (PBS extracts) exhibited this activity, suggesting a relatively high incidence rate.
Geographic origin and ecological variables are hypothesized to influence metabolite (e.g., lectin)
production in algae, possibly explaining these discrepancies. Genetic variation among algal species may further contribute. Blunden et al.¹⁸ and Fabregas et al.²³ proposed that lectins with specific affinities may be used in seaweed chemotaxonomy.
Despite limited documentation, some seaweeds in our study exhibited agglutinating activity against RBCs from patients with coagulopathies, including hemophilia. Notably, ethanol extracts of Eucheuma serra had the highest titers, although generally lower than in normal RBCs. Enzymatic pretreatment with agents such as papain, neuraminidase, trypsin, and pronase has been shown to enhance lectin detection.²⁰˒²⁶˒³⁰˒³² However, these effects are species-dependent. In our assays, trypsin improved agglutination in PBS extracts but had limited effects in ethanol extracts.
Inter-species variation was also observed in animal RBC agglutination. Our results indicate that murine RBCs are more sensitive to algal lectins than those of macaques or sheep.
Thermal stability is another critical factor. Most algal lectins lose activity above 40 °C due to protein denaturation. Nonetheless, a few retain activity at 80–90 °C.²⁰˒²⁹˒³⁰ In our study, the only microalga tested, Isochrysis galbana, retained activity at 65 °C, suggesting thermotolerance (Table 3).
Table 3. Agglutination activity of two kinds of marine algae extracts on human and animal red blood cells after mixing (Power price) change; B: human type B red blood cells, PT and APTT: agglutination defect patient’s red blood cells; 0: no agglutination; the number in brackets represents the original agglutination activity of the two algae.
Algal species — Erythrocyte (B, PT, APTT, Sheep)
-
Skeletonema costatum / Isochrysis galbana
B: (0, 2)²⁸
PT: (2¹⁴, 2²⁰)
APTT: (2¹², 2⁷)
Sheep: (0, 0)²⁸ -
S. costatum / Cyclotella sp.
B: (0, 2³)
PT: (2¹⁴, 0)
APTT: (2¹², 2¹²)
Sheep: (0, 0) -
S. costatum / Porphyridium sp.
B: (0, 2⁴)
PT: (2¹⁴, 2¹)
APTT: (2¹², 2¹)
Sheep: (0, 2²) -
I. galbana / Pavlova salina
B: (2⁶, 2⁹)
PT: (2²⁰, 2²⁰)
APTT: (2⁷, 2¹)
Sheep: (0, 0)
The literature lacks comprehensive studies on lectins in microalgae. Some reports have linked lectin expression to gamete recognition in Chlamydomonas and fucoid brown algae. However, these are limited to reproductive roles. Recent studies, including our own preliminary report, suggest that microalgal lectins also possess hemagglutination activity. Extracts from Ankistrodesmus convolutus and Synechococcus sp. reached titers of 224–225, indicating potential biomedical applications.
Unlike macroalgae, microalgal lectins displayed higher affinity for human type O and AB RBCs and exhibited stronger agglutination against coagulopathic cells. Among animal RBCs, microalgae showed strong activity against macaque cells but weak or no activity against sheep RBCs. Agglutination was generally inhibited by specific sugars, indicating that these lectins are glycoprotein-binding (glycophilic).
Because lectins may have differing specificity across species, enzymatic RBC pretreatment is often employed to enhance detection.¹⁸˒²⁰˒²² Proteases such as pronase and trypsin are most commonly used. In our assays, trypsin enhanced type A RBC agglutination by seaweed lectins, with ethanol extracts showing higher titers than PBS. In contrast, enzyme effects on microalgae varied; Synechococcus sp. activity against O RBCs dropped from 225 to 212 after trypsin treatment. Trypsin also inhibited lectin activity in Hymenomonas sp. and Pavlova salina. Pronase, however, was more effective than trypsin in enhancing agglutination of animal RBCs by microalgae.
The pronase used in our study has been well-documented as a broad-spectrum proteolytic enzyme.⁴¹˒⁴² Its actions can be classified as exopeptidase or endopeptidase. Trypsin, a specific serine protease, cleaves peptide bonds on the carboxyl side of lysine and arginine residues.
Notably, some purified lectins lose hemagglutinating activity while retaining carbohydrate-binding ability. These are classified as glycophilic proteins. The presence of sugar-inhibitable hemagglutination further supports this. Boyal et al.² pointed out the glycophilic nature of seaweed lectins, a field still less understood than land plant lectins. Rogers and Hori³³ classified red algal lectins based on their sugar affinity and molecular weight: low-molecular-weight lectins binding only glycoproteins, those binding monosaccharides, and high-molecular-weight lectins binding both. Chlorella lectins, with relatively high molecular weights, bind to monosaccharides such as GalNAc and GlcNAc.²⁹˒²⁴
In conclusion, our results support the diversity and biomedical potential of algal lectins, particularly from microalgae, and highlight the importance of environmental, biochemical, and genetic factors in lectin activity.
VI. Conclusion
This study successfully constructed a seaweed lectin purification platform with artificial intelligence (AI) as the core, and applied it for the first time to the extraction, separation, qualitative analysis and activity verification of lectins contained in the red alga Eucheuma serra. By combining traditional biochemical analysis methods (such as SDS-PAGE, Western blot, ELISA) with AI machine learning models (such as random forest algorithm) for cross-validation, not only the accuracy and efficiency of protein purification and functional identification are improved, but also the potential of AI in the research of marine natural products is demonstrated.
This platform is not only applicable to Eucheuma serra, but also has the feasibility of being expanded to other species of seaweed (such as Gracilaria, Sargassum, Ulva) and microalgae (such as Nannochloropsis, Isochrysis). In the future, it is recommended to integrate this platform into a high-throughput protein screening system and a cloud database architecture to facilitate large-scale lectin activity analysis and structure prediction, and further promote the development and commercialization of marine natural products in medical applications such as anti-tumor, immune regulation, and virus inhibition.
In summary, this research paper lays the foundation for the application of AI in the study of marine bioactive proteins, and opens up new directions for the research and development of algae biotechnology and natural product pharmaceuticals.
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