Prediction of the Health Effects of Food Peptides and Elucidation of the Mode‐of‐action Using Multi‐task Graph Convolutional Neural Network

Fukunaga, Itsuki; Sawada, Ryusuke; Shibata, Tomokazu; Kaitoh, Kazuma; Sakai, Yukie; Yamanishi, Yoshihiro

doi:10.1002/minf.201900134

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…170 For example, Fukunaga et al reported the antihypertensive and hypoglycemic effects of food peptides and the anticancer effects of several food peptides. 171 Peptidespecific Maillard reaction products (MRPs) have a unique flavor in food processing and storage due to their unique molecular structure and formation mechanism. In addition, these MRPs also have antioxidant, antibacterial, antihypertensive, anti-inflammatory, and other biological activities.…”

Section: Other Functional Peptidesmentioning

confidence: 99%

Recent research progress of biologically active peptides

et al. 2022

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With the rapid development of molecular biology and biochemical technology, great progress has been made in the study of peptides. Peptides are easy to digest and absorb, with lowering of blood pressure and cholesterol, improving immunity, regulating hormones, antibacterial, and antiviral effects. Peptides also have physiological regulation and biological metabolism functions with applications in the fields of feed production and biomedical research. In the future, the research focus of bioactive peptides will focus on their efficient preparation and application. This article introduces a comprehensive review of the types, synthesis, functionalization, and bio‐related applications of bioactive peptides. For this aim, we introduced in detail various biopeptides and then presented the production methods of bioactive peptides, such as enzymatic synthesis, microbial fermentation, chemical synthesis, and others. The applications of bioactive peptides for anticancers, immune therapy, antibacterial, and other applications have been introduced and discussed. And discussed the development prospects of biologically active peptides.

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Section: Other Functional Peptidesmentioning

confidence: 99%

Recent research progress of biologically active peptides

et al. 2022

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“…Deep learning has also been used in peptide drug research, such as for predicting health effects using peptide chemical structure and antimicrobial activity using amino acid sequences [21,22]. Automatic peptide-generation models using recurrent neural network, VAE, and GAN have been developed to generate peptides with antimicrobial activity [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization

Fukunaga,

Matsukiyo,

Kaitoh

et al. 2024

Molecular Informatics

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Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process‐based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.

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“…However, for data with irregular graph structures, the surrounding structure of each data node may be unique, which makes the CNN and RNN fail instantaneously, so Bruna et al [14] proposed the GCN. GCN can effectively process irregular graph data, fully learn node features and edge information representing node association in the graph, extract hidden and weak features, and express the index information in the graph [15,16]. It should be noted that the calculation of the next-layer feature of a node is only related to itself and its neighbor nodes in GCN.…”

Section: Introductionmentioning

confidence: 99%

Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Fan,

Gao,

Zuo

et al. 2023

Land

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Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

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Prediction of the Health Effects of Food Peptides and Elucidation of the Mode‐of‐action Using Multi‐task Graph Convolutional Neural Network

Cited by 9 publications

References 47 publications

Recent research progress of biologically active peptides

Recent research progress of biologically active peptides

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization

Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

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