A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction

Bioactive peptides, as small protein fragments, are essential mediators of diverse physiological activities, such as antimicrobial, anti-inflammatory, anticancer, antioxidant, and immunomodulatory functions. Despite their substantial potential in pharmaceuticals and the food industry, conventional methods for peptide classification and activity prediction are limited by high costs, time-intensive procedures, and extensive data processing requirements. Here, we present BioPepPred-DLEmb, a novel computational model integrating Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs), augmented with natural language processing to encode amino acids into information-dense vectors. Evaluated across nine bioactive peptide datasets, BioPepPred-DLEmb demonstrates superior predictive accuracy (0.909) and sensitivity (0.911) compared to traditional methods. Through UMAP visualization and Kplogo analysis, the model effectively differentiates peptide activity states and identifies key biomarkers. The predicted antimicrobial peptides (Pred-AMPs) exhibit potent efficacy in vitro, achieving low micromolar inhibitory concentrations (2-16 μmol/L) against pathogens such as Escherichia coli and Acinetobacter baumannii. These findings establish a robust foundation for bioactive peptide development, with implications for advancements in precision medicine, personalized therapies, and functional food innovations.

Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes

Diabetes mellitus (DM) presents a critical global health challenge, characterized by persistent hyperglycemia and associated with substantial economic and health-related burdens. This study employs advanced machine-learning techniques to improve the prediction and classification of antidiabetic peptides, with a particular focus on differentiating those effective against T1DM from those targeting T2DM. We integrate feature selection with analysis methods, including logistic regression, support vector machines (SVM), and adaptive boosting (AdaBoost), to classify antidiabetic peptides based on key features. Feature selection through the Lasso-penalized method identifies critical peptide characteristics that significantly influence antidiabetic activity, thereby establishing a robust foundation for future peptide design. A comprehensive evaluation of logistic regression, SVM, and AdaBoost shows that AdaBoost consistently outperforms the other methods, making it the most effective approach for classifying antidiabetic peptides. This research underscores the potential of machine learning in the systematic evaluation of bioactive peptides, contributing to the advancement of peptide-based therapies for diabetes management.

A comparative study on bath and horn ultrasound-assisted modification of bentonite and their effects on the bleaching efficiency of soybean and sunflower oil: Machine learning as a new approach for mathematical modeling

In this study, the effect of high-power bath and horn ultrasound at different powers on specific surface area (S BET), total pore volume (V total), and average pore volume (D ave) of bleaching clay was examined. After subjecting the bleaching clay to ultrasonication treatment, the SBET values demonstrated an escalation from 31.4 ± 2.7 m2 g-1 to 59.8 ± 3.1 m2 g-1 for HU200BC, 143.8 ± 3.9 m2 g-1 for HU400BC, 54.4 ± 3.6 m2 g-1 for BU400BC, and 137.5 ± 2.8 m2 g-1 for BU800BC. The mean pore diameter (D ave) declined from 29.7 ± 0.14 nm in bleaching clay to 11.3 ± 0.13 nm in HU200BC, 8.3 ± 0.12 nm in HU400BC, 16.7 ± 0.14 nm in BU400BC, and 9.6 ± 0.12 nm in BU800BC. Therefore, horn ultrasound-treated bleaching clay significantly increased S BET and V total, indicating improved adsorption capacity. Moreover, to establish the relationship between bleaching parameters, seven multi-output ML regression models of Feedforward Neural Network (FNN), Random Forest (RF), Support Vector Regression (SVR), Multi-Task Lasso, Ridge regression, Extreme Gradient Boosting (XGBoost), and Gradient Boosting are used, and compared with response surface methodology (RSM). ML has revolutionized the understanding of complex relationships between ultrasonic parameters, oil color, and pigment degradation, providing insights into how various factors such as temperature, ultrasonic power, and time can influence the bleaching process, ultimately enhancing the efficiency and precision of the treatment. The XGBoost model showed outstanding performance in predicting the target variables with a high R 2-train up to 1, R 2-test up to .983, and a minimum mean absolute error (MAE) of 0.498. The lower error between the predicted and experimental values implies the superiority of the XGBoost model to predict outcomes rather than RSM. It represents the suitability of bath ultrasound as a mild condition for low-pigmented oil bleaching. Finally, the Bayesian optimization method in conjunction with XGBoost was used to optimize the amount of bleaching clay and energy consumption, and its performance was compared with RSM. It was observed that the consumption of bleaching clay was reduced by approximately 60% for sunflower oil and 30%-35% for soybean oil.

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Dipeptidyl peptidase-IV (DPP-4) enzyme inhibitors are a promising category of diabetes medications. Bioactive peptides, particularly those derived from bovine milk proteins, play crucial roles in inhibiting the DPP-4 enzyme. This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques. Five machine learning models, including GBDT, XGBoost, LightGBM, CatBoost, and RF, were trained. Notably, LightGBM demonstrated superior performance with an AUC value of 0.92 ± 0.01. Subsequently, LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins. Through a series of in silico screening process and in vitro experiments, GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity. Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides. Through retracing the virtual proteolysis steps, it was found that GPVRGPF can be obtained from β-casein through enzymatic hydrolysis by chymotrypsin, while HPHPHL can be obtained from κ-casein through enzymatic hydrolysis by stem bromelain or papain. In summary, the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Impact of Lactiplantibacillus plantarum and casein fortification on angiotensin converting enzyme inhibitory peptides in yogurt: identification and in silico analysis

In this study, the effects of Lactiplantibacillus plantarum M11 (Lb. plantarum M11) in conjunction with sodium caseinate on the characteristics and angiotensin converting enzyme (ACE) inhibitory activity of yogurt were investigated. ACE inhibitory peptides (ACEIPs) in yogurt were identified by nano-LC-MS/MS and potential ACEIPs were predicted by in silico and molecular docking methods. The results showed that the ACE-inhibitory activity of yogurt was significantly enhanced (p < 0.05), while maintaining the quality characteristics of the yogurt. Thirteen ACEIPs in the improved yogurt (883 + M11-CS group) were identified, which were more abundant than the other yogurt groups (control 883 group, 883 + M11 group and 883-CS group). Two novel peptides with potential ACE inhibitory activity, YPFPGPIH and NILRFF, were screened. The two peptides showed PeptideRanker scores above 0.8, small molecular weight and strong hydrophobicity, and were non-toxic after prediction. Molecular docking results showed that binding energies with ACE were -9.4 kcal mol-1 and -10.7 kcal mol-1, respectively, and could bind to the active site of ACE. These results indicated that yogurt with Lb. plantarum M11 and sodium caseinate has the potential to be utilized as a functional food with antihypertensive properties. The combination of ACEIP-producing strains and casein fortification could be an effective method to promote the release of ACEIPs from yogurt.

Deep2Pep: A deep learning method in multi-label classification of bioactive peptide

Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the functional peptides from a large number of peptides with unknown functions. With the development of machine learning and Deep learning, the combination of computational methods and biological information provides an effective method for identifying peptide functions. To explore the value of multi-functional active peptides, a new deep learning method named Deep2Pep (Deep learning to Peptides) was constructed, which was based on sequence encoding, embedding, and language tokenizer. It can achieve predictions of peptides on antimicrobial, antihypertensive, antioxidant and antihyperglycemic by converting sequence information into digital vectors, combined BiLSTM, attention-residual algorithm, and BERT Encoder. The results showed that Deep2Pep had a Hamming Loss of 0.095, subset Accuracy of 0.737, and Macro F1-Score of 0.734. which outperformed other models. BiLSTM played a primary role in Deep2Pep, which BERT encoder was in an auxiliary position. Deep learning algorithms was used in this study to accurately predict the four active functions of peptides, and it was expected to provide effective references for predicting multi-functional peptides.

pLM4ACE: A protein language model based predictor for antihypertensive peptide screening

Angiotensin-I converting enzyme (ACE) regulates the renin-angiotensin system and is a drug target in clinical treatment for hypertension. This study aims to develop a protein language model (pLM) with evolutionary scale modeling (ESM-2) embeddings that is trained on experimental data to screen peptides with strong ACE inhibitory activity. Twelve conventional peptide embedding approaches and five machine learning (ML) modeling methods were also tested for performance comparison. Among the 65 classifiers tested, logistic regression with ESM-2 embeddings showed the best performance, with balanced accuracy (BACC), Matthews correlation coefficient (MCC), and area under the curve of 0.883 ± 0.017, 0.77 ± 0.032, and 0.96 ± 0.009, respectively. Multilayer perceptron and support vector machine also exhibited great compatibility with ESM-2 embeddings. The ESM-2 embeddings showed superior performance in enhancing the prediction model compared to the 12 traditional embedding methods. A user-friendly webserver (https://sqzujiduce.us-east-1.awsapprunner.com) with the top three models is now freely available.

A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides

Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the peptides. Machine-learning-based models have been applied in broad areas, which also indicates their potential to be incorporated into the field of bioactive peptides. In this study, a long short-term memory (LSTM) algorithm-based deep learning model was constructed, which could predict the IC₅₀ value of the peptide in inhibiting ACE activity. In addition to the test dataset, the model was also validated using randomly synthesized peptides. The LSTM-based model constructed in this study provides an efficient and simplified method for screening antihypertensive peptides from food proteins.

Revealing the Sequence Characteristics and Molecular Mechanisms of ACE Inhibitory Peptides by Comprehensive Characterization of 160,000 Tetrapeptides

Chronic diseases, such as hypertension, cause great harm to human health. Conventional drugs have promising therapeutic effects, but also cause significant side effects. Food-sourced angiotensin-converting enzyme (ACE) inhibitory peptides are an excellent therapeutic alternative to pharmaceuticals, as they have fewer side effects. However, there is no systematic and effective screening method for ACE inhibitory peptides, and the lack of understanding of the sequence characteristics and molecular mechanism of these inhibitory peptides poses a major obstacle to the development of ACE inhibitory peptides. Through systematically calculating the binding effects of 160,000 tetrapeptides with ACE by molecular docking, we found that peptides with Tyr, Phe, His, Arg, and especially Trp were the characteristic amino acids of ACE inhibitory peptides. The tetrapeptides of WWNW, WRQF, WFRV, YYWK, WWDW, and WWTY rank in the top 10 peptides exhibiting significantly high ACE inhibiting behaviors, with IC₅₀ values between 19.98 ± 8.19 μM and 36.76 ± 1.32 μM. Salt bridges, π-π stacking, π-cations, and hydrogen bonds contributed to the high binding characteristics of the inhibitors and ACE. Introducing eight Trp into rabbit skeletal muscle protein (no Trp in wide sequence) endowed the protein with a more than 90% ACE inhibition rate, further suggesting that meat with a high content of Trp could have potential utility in hypertension regulation. This study provides a clear direction for the development and screening of ACE inhibitory peptides.

Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections

Background

The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents.

Objectives

This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly.

Methods

A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations.

Results

The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity.

Conclusions

In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.

Food protein-derived bioactive peptides for the management of nutrition related chronic diseases

Dietary intervention via modifications of dietary pattern or supplementations of naturally derived bioactive compounds has been considered as an efficient approach in management of nutrition related chronic diseases. Food protein-derived bioactive peptide is representative of natural compounds which show the potential to prevent or mitigate nutrition related chronic diseases. In the past decades, substantial research has been conducted concentrating on the characterization, bioavailability, and activity assessment of bioactive peptides. Although various activities of bioactive peptides have been reported, the activity testes of most peptides were only conducted in cells and animal models. Some clinical trials of bioactive peptides were also reported but only limited to antihypertensive peptides, antidiabetic peptides and peptides modulating blood lipid profile. Hereby, clinical evidence of bioactive peptides in management of nutrition-related chronic diseases is summarized in this chapter, which aims at providing implications for the clinical studies of bioactive peptides in the future.