Changes in the structure and enzyme binding of starches during in vitro enzymatic hydrolysis using mammalian mucosal enzyme mixtures.

Starches are hydrolyzed into monosaccharides by mucosal α-glucosidases in the human small intestine. However, there are few studies assessing the direct digestion of starch by these enzymes. The objective of this study was to investigate the changes in the structure and enzyme binding of starches during in vitro hydrolysis by mammalian mucosal enzymes. Waxy maize (WMS), normal maize (NMS), high-amylose maize (HAMS), waxy potato (WPS), and normal potato (NPS) starches were examined. The order of the digestion rate was different compared with other studies using a mixture of pancreatic α-amylase and amyloglucosidase. NPS was digested more than other starches. WPS was more digestible than WMS. Hydrolyzed starch from NPS, NMS, WPS, WMS, and HAMS after 24 h was 66.4, 64.2, 61.7, 58.7, and 46.2 %, respectively. Notably, a significant change in the morphology, reduced crystallinity, and a decrease in the melting enthalpy of the three starches (NPS, NMS, and WPS) after 24 h of hydrolysis were confirmed by microscopy, X-ray diffraction, and differential scanning calorimetry, respectively. The bound enzyme fraction of NPS, NMS, and WPS increased as hydrolysis progressed. In contrast, HAMS was most resistant to hydrolysis by mucosal α-glucosidases in terms of digestibility, changes in morphology, crystallinity, and thermal properties.

pLM4Alg: Protein Language Model-Based Predictors for Allergenic Proteins and Peptides.

The rising prevalence of allergy demands efficient and accurate bioinformatic tools to expedite allergen identification and risk assessment while also reducing wet experiment expenses and time. Recently, pretrained protein language models (pLMs) have successfully predicted protein structure and function. However, to our best knowledge, they have not been used for predicting allergenic proteins/peptides. Therefore, this study aims to develop robust models for allergenic protein/peptide prediction using five pLMs of varying sizes and systematically assess their performance through fine-tuning with a convolutional neural network. The developed pLM4Alg models have achieved state-of-the-art performance with accuracy, Matthews correlation coefficient, and area under the curve scoring 93.4-95.1%, 0.869-0.902, and 0.981-0.990, respectively. Moreover, pLM4Alg is the first model capable of handling prediction tasks involving residue-missed sequences and sequences containing nonstandard amino acid residues. To facilitate easy access, a user-friendly web server (https://f6wxpfd3sh.us-east-1.awsapprunner.com) has been established. pLM4Alg is expected to become the leading machine learning-based prediction model for allergenic peptides and proteins. Its collaboration with other predictors holds great promise for accelerating allergy research.

UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selection, model selection and hyperparameter tuning. Recently, advanced pretrained deep learning-based language models (LMs) have been released for protein sequence embedding and applied to structure and function prediction. Based on these developments, we have developed UniDL4BioPep, a universal deep-learning model architecture for transfer learning in bioactive peptide binary classification modeling. It can directly assist users in training a high-performance deep-learning model with a fixed architecture and achieve cutting-edge performance to meet the demands in efficiently novel bioactive peptide discovery. To the best of our best knowledge, this is the first time that a pretrained biological language model is utilized for peptide embeddings and successfully predicts peptide bioactivities through large-scale evaluations of those peptide embeddings. The model was also validated through uniform manifold approximation and projection analysis. By combining the LM with a convolutional neural network, UniDL4BioPep achieved greater performances than the respective state-of-the-art models for 15 out of 20 different bioactivity dataset prediction tasks. The accuracy, Mathews correlation coefficient and area under the curve were 0.7-7, 1.23-26.7 and 0.3-25.6% higher, respectively. A user-friendly web server of UniDL4BioPep for the tested bioactivities is established and freely accessible at https://nepc2pvmzy.us-east-1.awsapprunner.com. The source codes, datasets and templates of UniDL4BioPep for other bioactivity fitting and prediction tasks are available at https://github.com/dzjxzyd/UniDL4BioPep.

Encapsulation of tannins and tannin-rich plant extracts by complex coacervation to improve their physicochemical properties and biological activities: A review.

As a major class of dietary polyphenols, tannins are demonstrated to have various health-promoting properties. Although tannins have been widely utilized in food, pharmaceutical and many other industries, the applications of tannins are quite limited due to their poor stability, sensory attributes and bioavailability. Encapsulation helps improve all of these properties. Complex coacervation, one of the most effective encapsulation techniques, is known for its simplicity, low cost, scalability and reproducibility in encapsulation of functional components. In recent years, complex coacervation has been successfully used for encapsulation of tannins and tannin-rich plant extracts. In this article, the research progress in encapsulating tannins and tannin-rich plant extracts by complex coacervation to improve their physicochemical properties and biological activities is critically reviewed for the first time. Encapsulation of tannins and tannin-rich plant extracts can effectively improve their sensory characteristics, stabilities, bioavailability, anti-hypercholesterolemia, anti-diabetic, antioxidant, anticancer and antimicrobial activities. In particular, the enhancement of biological activities of tannins and tannin-rich plant extracts is usually correlated to their improved physicochemical properties imparted by the encapsulation technique. Moreover, we introduce the issues that need to be further resolved in future studies on encapsulation of tannins and tannin-rich plant extracts by complex coacervation.

Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides.

Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides based on wet-chemistry methods are time-consuming and highly rely on many advanced instruments and trained personnel. Quantitative structure-activity relationship (QSAR) analysis as an in silico method can be more efficient and cost-effective. However, model performance of QSAR studies on antioxidant peptides was still poor due to limited attempts in model development approaches. The objective of this study was to compare popular machine learning methods for antioxidant activity modeling and screening of tripeptides and identify the critical amino acid features that determine the antioxidant activity. 533 numerical indices of amino acids were adopted to characterize 130 tripeptides with known antioxidant activity from the published literature, and then 7 feature selection strategies plus pairwise correlation were used to screen the most important indices for antioxidant activity and model building. 14 machine learning methods were used to build models based on the feature selection strategies, respectively. Among the 98 models, non-linear regression methods tended to perform better, and the best model with an R 2 Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining random forest for feature selection and tree-based extreme gradient boost regression for model development. Based on the predicted antioxidant values of 7870 unknown tripeptides, potentially high antioxidant activity tripeptides all have a tyrosine, tryptophan, or cysteine residue at the C-terminal position. Furthermore, the predicted antioxidant activity of six synthesized tripeptides was confirmed through experimental determination, and for the first time, the cysteine or tyrosine residue at the C-terminal was found to be critical to the antioxidant activity based on both QSAR models and experimental observations.

Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review.

Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.

Metal-organic framework for the extraction and detection of pesticides from food commodities.

Pesticide residues in food matrices, threatening the survival and development of humanity, is one of the critical challenges worldwide. Metal-organic frameworks (MOFs) possess excellent properties, which include excellent adsorption capacity, tailorable shape and size, hierarchical structure, numerous surface-active sites, high specific surface areas, high chemical stabilities, and ease of modification and functionalization. These promising properties render MOFs as advantageous porous materials for the extraction and detection of pesticides in food samples. This review is based on a brief introduction of MOFs and highlights recent advances in pesticide extraction and detection through MOFs. Furthermore, the challenges and prospects in this field are also described.

Preparation, characterization and in vitro hypoglycemic activity of banana condensed tannin-inulin conjugate.

To enhance the hypoglycemic effects of inulin, banana condensed tannins (BCT) were grafted onto inulin via a free radical method to synthesize the novel BCT grafted inulin (BCT-g-inulin) complex. Spectroscopic methods, XRD, TGA, 1H NMR, GPC and morphology analyses were utilized to characterize the structural properties of the BCT-g-inulin complex, and our results confirmed the conjugation of BCT and inulin. The conjugation possibly occurred between the hydroxyl group attached at the C6 position of inulin and the C6/C8 position of flavon-3-ol units of BCT. The grafting ratio and grafting efficiency of the BCT-g-inulin complex were 357.54 ± 2.98 g kg-1 complex and 74.57 ± 1.44%, respectively. The data of the antioxidant assays indicated that the BCT-g-inulin complex showed a significantly higher antioxidant activity than native inulin. Also, the grafting reaction remarkably improved the in vitro anti-diabetic activity of inulin. The glucose adsorption capacity and glucose dialysis retardation index of the BCT-g-inulin complex were remarkably higher than those of inulin, while the BCT-g-inulin complex showed much stronger inhibitory effects against α-amylase and α-glucosidase compared with inulin. Notably, the inhibition of both α-amylase and α-glucosidase by the BCT-g-inulin complex occurred through mixed-competitive mode. On the basis of fluorescence spectroscopy, the fluorescence of α-amylase and α-glucosidase could be quenched by the BCT-g-inulin complex through a static quenching mechanism. Hence, the BCT-g-inulin complex might have the potential to be developed as an effective anti-diabetic agent.

Characterization of the direct interaction between apple condensed tannins and cholesterol in vitro.

To provide the scientific evidences for a possible new hypocholesterolemic mechanism of apple condensed tannins (ACT), the direct interaction of ACT with cholesterol (CH) was investigated in the present study. Our results suggested that the quenching of ACT fluorescence by CH was carried out according to a static mechanism, while the interaction between ACT and CH in vitro was a spontaneous process. ACT were capable of binding with CH directly, and the CH-binding capacity (35.9-43.9%) of ACT remarkably enhanced with the increase of ACT concentration (0.5-2.0 mg proanthocyanidin B2 equivalent/mL). Besides, spectroscopic methods and morphological analysis were used to characterize the ACT-CH coprecipitates, the findings indicated that ACT were able to precipitate CH via ionic interactions, hydrophobic interactions and intermolecular hydrogen bonds rather than covalent bonds. In conclusion, the direct interaction of ACT with CH might play a role in their CH-lowering effects in humans and animals.

Characterization of the interactions between banana condensed tannins and biologically important metal ions (Cu2+, Zn2+ and Fe2+).

To characterize the interactions of banana condensed tannins (BCT) with copper (Cu2+), zinc (Zn2+) and iron ions (Fe2+), various methods and techniques were utilized in the current work. Our findings indicated that BCT isolated from green bananas (cv. Brazil) were a mixture of flavan-3-ol oligomers with degree of polymerization in the range of 3-5 and with the trimers as the main component. The (-)-epigallocatechin and (-)-epicatechin were the dominant units of oligomer structures. BCT could interact with divalent transition metal ions mainly via catechol group-ion exchange and pyrogallol group-ion exchange, while static quenching mechanism was the main action mode of BCT fluorescence quenching by metal ions. The effectiveness of precipitation of metal ions by BCT decreased with the order of Fe2+ > Cu2+ > Zn2+. Alternatively, the characteristics of BCT-metal ion coprecipitates confirmed the interactions between BCT and divalent metal ions. Therefore, characterization of the interaction mechanisms between BCT and biologically important metal ions should help in the design of strategies to prevent the metal-induced toxicity in individuals with a high exposure of transition metal ions.