Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Deng, Baichuan; Long, Hongrong; Tang, Tianyue; Ni, Xiaojun; Chen, Jialuo; Yang, Guangming; Zhang, Fan; Cao, Ruihua; Cao, Dong‐Sheng; Zeng, Maomao; Yi, Lunzhao

doi:10.3390/ijms20040995

Cited by 22 publications

(36 citation statements)

References 40 publications

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“…When using bioinformatics to prepare PAs, the update and combination of peptide databases are also important. Deng et al applied model population analysis to establish a QSAR model on two datasets containing antioxidant tripeptides (FTC and FRAP) [188]. This three-dimensional QSAR model is constructed through CoMFA and comparative similarity index analysis, and can be used to guide the combination design and virtual screening of new peptides [189].…”

Section: Pas Identification and Sarmentioning

confidence: 99%

Advances on Food-Derived Peptidic Antioxidants—A Review

et al. 2020

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The oxidation process is considered to be the main reason behind human aging, human degenerative diseases and food quality degradation. Food-derived peptidic antioxidants (PAs) have wide sources and great activity, and have broad application prospects in removing excess reactive oxygen species in the body, anti-aging and preventing and treating diseases related to oxidative stress. On the other hand, PAs are expected to inhibit the lipid peroxidation of foods and increase the stability of the food system in the food industry. However, the production pathways and action mechanism of food-derived PAs are diverse, which makes it is difficult to evaluate the performance of PAs which is why the commercial application of PAs is still in its infancy. This article focuses on reviewing the preparation, purification, and characterization methods of food-derived PAs, and expounds the latest progress in performance evaluation and potential applications, in order to provide an effective reference for subsequent related research of PAs.

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Section: Pas Identification and Sarmentioning

confidence: 99%

Advances on Food-Derived Peptidic Antioxidants—A Review

et al. 2020

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“…The results from the ORAC assay (Figure 2c) imply that the peptides identified in this sample may be playing a significant role in scavenging oxygen radicals in boiled bean hydrolysates. If the N‐terminus of a particular peptide sequence has a low isoelectric point, and a strong hydrophobic moiety with amino acid residues such as Ala, Val, Gly, or Leu, then the peptide is believed to exhibit very strong antioxidant activity (Deng et al., 2019). The specific peptide sequences that were found to be higher in boiled samples contain Ile or Leu residues, that may contribute toward the enhanced antioxidant activity of the boiled hydrolysates.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the effect of high‐pressure processing in contrast to boiling on the antioxidant activity from alcalase hydrolysate of Great Northern Beans ( Phaseolus vulgaris )

et al. 2021

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Food processing can alter protein structure, modulate enzyme accessibility, and therefore the release of bioactive peptides. Thus, processing techniques, boiling, high‐pressure processing (HPP), and a combination of both, were compared for their efficiency to release antioxidant peptides after alcalase hydrolysis of Great Northern Beans (GNBs). The oxygen radical absorbance capacity (ORAC) indicated that boiled hydrolysates had the highest antioxidant activity (370.9 ± 43.8 µmol TEAC/g). Mass spectrometry‐based analysis suggested that di‐ and tri‐peptide expression were significantly altered among the three treatments, and either Ile, Leu, Phe, and Arg containing peptides potentially contributed toward the enhanced antioxidant activity. Furthermore, the total phenolic content of the HPP‐treated hydrolysate was higher than the other two treatments, with ferulic acid being the most prominent phenolic compound present in the bean hydrolysates. This study indicates that thermal processing such as boiling is more effective in modulating the release of antioxidant peptides. Practical applications Common beans (Phaseolus vulgaris), such as Great Northern Beans (GNBs) are one of the major pulse crops in the United States. Storage proteins in beans can release peptides with biological activities after enzymatic hydrolysis. However, processing conditions can modulate the release of peptides. The present study is primarily focused on comparing the two processing methods, boiling and HPP, and their combination for the generation of peptides with potential antioxidant activity in alcalase‐digested GNBs. Data from the study suggest that thermal treatment such as boiling is more effective in modulating the release of peptides from alcalase hydrolysate of GNBs with antioxidant activity. This is particularly important because over different cultures around the world, boiling is the most widely used processing method for the cooking of beans, and hence, these data also ensure that boiling is the most effective method in getting the most beneficial effects from the consumption of beans.

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“…Quantitative structure-activity relationship (QSAR) modelling is a well-established field of research that aims at mapping sequence and structural properties of chemical compounds to their biological activities [25]. QSAR models have been successfully applied to ACE-inhibitory peptides [26][27][28], antimicrobial peptides [29][30][31][32], and antioxidant peptides [33][34][35]. For solubility predictions, DSResSol [36] improved prediction accuracy (ACC) to 79.6% by identifying long-range interaction information between amino acid k-mers with dilated convolutional neural networks and outperformed all existing models such as DeepSol [37], PaRSnIP [38], SoluProt [39] and PROSO II [40].…”

Section: Previous Workmentioning

confidence: 99%

Serverless Prediction of Peptide Properties with Recurrent Neural Networks

Ansari

White

2022

Preprint

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We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the art models. These predictive models share a common architecture of bidirectional recurrent neural networks (LSTM). These models are implemented in JavaScript so that they can be run on a static website without use of a dedicated server. This removes the cost, and long-term management of a server, while still enabling open and free access to the models. This "serverless" prediction model is a demonstration of edge computing bioinformatics and removes the dependence on cloud providers or self-hosting of resource-rich academic institutions. This is feasible because of the continued track of Moore's law and ubiquitous hardware acceleration of deep learning computations on new phones and desktops.

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Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Cited by 22 publications

References 40 publications

Advances on Food-Derived Peptidic Antioxidants—A Review

Advances on Food-Derived Peptidic Antioxidants—A Review

Evaluating the effect of high‐pressure processing in contrast to boiling on the antioxidant activity from alcalase hydrolysate of Great Northern Beans ( Phaseolus vulgaris )

Serverless Prediction of Peptide Properties with Recurrent Neural Networks

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