The mechanism of epigallocatechin-3-gallate inhibiting the antigenicity of ß-lactoglobulin under pH 6.2, 7.4 and 8.2: Multi-spectroscopy and molecular simulation methods.

The antigenicity of ß-lactoglobulin (ß-LG) can be influenced by pH values and reduced by epigallocatechin-3-gallate (EGCG). However, a detailed mechanism concerning EGCG decreasing the antigenicity of ß-LG at different pH levels lacks clarity. Here, we explore the inhibition mechanism of EGCG on the antigenicity of ß-LG at pH 6.2, 7.4 and 8.2 using enzyme-linked immunosorbent assay, multi-spectroscopy, mass spectrometry and molecular simulations. The results of Fourier transform infrared spectroscopy (FTIR) and circular dichroism (CD) elucidate that the noncovalent binding of EGCG with ß-LG induces variations in the secondary structure and conformations of ß-LG. Moreover, EGCG inhibits the antigenicity of ß-LG the most at pH 7.4 (98.30 %), followed by pH 6.2 (73.18 %) and pH 8.2 (36.24 %). The inhibitory difference is attributed to the disparity in the number of epitopes involved in the interacting regions of EGCG and ß-LG. Our findings suggest that manipulating pH conditions may enhance the effectiveness of antigenic inhibitors, with the potential for further application in the food industry.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations.

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Insight into the Interaction Mechanism of Vitamin D against Metabolic Syndrome: A Meta-Analysis and In Silico Study.

As a dietary supplement or functional food additive, vitamin D (VD) deficiency may impact extra-skeletal functions associated with metabolic syndrome (MetS) risk factors. However, the precise effects and mechanisms of VD supplementation on dyslipidemia and insulin resistance in MetS subjects remain controversial. Here, we investigate potential therapeutic targets, pathways and mechanisms of VD against MetS through a comprehensive strategy including meta-analysis, network pharmacology analysis, molecular docking, dynamics simulations, and quantum chemical calculations. Our results reveal that VD supplementation significantly reduces triglyceride levels, fasting glucose, and insulin concentrations in subjects, thereby improving insulin homeostasis to some extent. We theoretically identify 14 core MetS-associated targets. Notably, VD exhibits substantial interactions with three targets (PPARγ, FABP4, and HMGCR) in the PPAR signaling pathway, indicating that VD can modulate this pathway. Van der Waals forces predominantly stabilize the complexes formed between VD and the three targets. Nonetheless, to provide valuable insights for personalized MetS management, further research is necessary to confirm our findings, emphasizing the importance of exploring genetic variability in VD response. In conclusion, our study contributes insights into the mechanisms of VD in preventing and treating MetS through dietary supplementation, promoting the development of VD-based functional foods or nutritious diets.

Insight into covalent conjugates of ß-lactoglobulin with rutin: Characterizing allergenicity, digestibility, and antioxidant properties in vitro.

ß-lactoglobulin (ß-LG) is an essential nutrient in milk, but it is the primary allergen causing dairy allergy in humans. Currently, researchers are focusing on using flavonoids to covalently modify ß-LG for improving its functionality. However, the impact and underlying mechanisms of rutin covalent modification on the functional properties and allergenicity of ß-LG remain unclear. Here, we aim to investigate the changes in allergenicity, digestive characteristics, and antioxidant properties of ß-LG after covalent modification using a combination of spectroscopy, enzyme-linked immunosorbent assay (ELISA), simulated digestion, and antioxidant assays. The results indicate that rutin forms covalent bonds with the free amino group, sulfhydryl group, and tryptophan of ß-LG, leading to alterations in the secondary structure of ß-LG. Furthermore, the modified ß-LG exhibits improved antioxidant capacity and decreased allergenicity, along with reduced resistance to pancreatin digestion in vitro. This study provides novel insights and strategies to expand the functional application of ß-LG.

Reducing Antigenicity and Improving Antioxidant Capacity of ß-Lactoglobulin through Covalent Interaction with Six Flavonoids.

ß-lactoglobulin (ß-LG) is a pivotal nutritional and functional protein. However, its application is limited by its antigenicity and susceptibility to oxidation. Here, we explore the impact of covalent modification by six natural compounds on the antigenicity and antioxidant characteristics of ß-LG to explore the underlying interaction mechanism. Our findings reveal that the covalent interaction of ß-LG and flavonoids reduces the antigenicity of ß-LG, with the following inhibition rates: epigallocatechin-3-gallate (EGCG) (57.0%), kaempferol (42.4%), myricetin (33.7%), phloretin (28.6%), naringenin (26.7%), and quercetin (24.3%). Additionally, the ß-LG-flavonoid conjugates exhibited superior antioxidant capacity compared to natural ß-LG. Our results demonstrate that the significant structural modifications from α-helix to ß-sheet induced by flavonoid conjugation elicited distinct variations in the antigenicity and antioxidant activity of ß-LG. Therefore, the conjugation of ß-LG with flavonoids presents a prospective method to reduce the antigenicity and enhance the antioxidant capacity of ß-LG.

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges.

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.

Nanomechanical Profiling of Aß42 Oligomer-Induced Biological Changes in Single Hippocampus Neurons.

Understanding how Aß42 oligomers induce changes in neurons from a mechanobiological perspective has important implications in neuronal dysfunction relevant to neurodegenerative diseases. However, it remains challenging to profile the mechanical responses of neurons and correlate the mechanical signatures to the biological properties of neurons given the structural complexity of cells. Here, we quantitatively investigate the nanomechanical properties of primary hippocampus neurons upon exposure to Aß42 oligomers at the single neuron level by using atomic force microscopy (AFM). We develop a method termed heterogeneity-load-unload nanomechanics (HLUN), which exploits the AFM force spectra in the whole loading-unloading cycle, allowing comprehensive profiling of the mechanical properties of living neurons. We extract four key nanomechanical parameters, including the apparent Young's modulus, cell spring constant, normalized hysteresis, and adhesion work, that serve as the nanomechanical signatures of neurons treated with Aß42 oligomers. These parameters are well-correlated with neuronal height increase, cortical actin filament strengthening, and calcium concentration elevation. Thus, we establish an HLUN method-based AFM nanomechanical analysis tool for single neuron study and build an effective correlation between the nanomechanical profile of the single neurons and the biological effects triggered by Aß42 oligomers. Our finding provides useful information on the dysfunction of neurons from the mechanobiological perspective.

An investigation on pickering nano-emulsions stabilized by dihydromyricetin/high-amylose corn starch composite particles: Preparation conditions and carrier properties.

With dihydromyricetin (DMY)/high-amylose corn starch (HCS) composite particles as the emulsifier, Pickering nano-emulsions were fabricated by combining high-speed shearing and high-pressure homogenization. The effect of particle properties and processing conditions on the formation and physicochemical properties of the Pickering nano-emulsions was then investigated systematically. The results showed that the DMY content of the composite particles, the oil phase volume fraction of the emulsion, and the homogenization conditions had obvious effects on the droplet size of the emulsion, where appropriate DMY content in the composite particles (5-20%) contributed to the formation of stable Pickering nano-emulsions. The oil phase of the obtained emulsions exhibited good stability during high-temperature storage, and their ß-carotene protecting performance against UV irradiation was superior to the emulsion stabilized by Tween 20. The in vitro simulated digestion analysis indicated that the nano-emulsions developed by the composite particles could enhance the bioaccessibility of ß-carotene and inhibit starch hydrolysis.

Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors.

Antioxidant peptides can protect against free radical-mediated diseases, especially food-derived antioxidant peptides are considered as potential competitors among synthetic antioxidants due to their safety, high activity and abundant sources. However, wet experimental methods can not meet the need for effectively screening and clearly elucidating the structure-activity relationship of antioxidant peptides. Therefore, it is particularly important to build a reliable prediction platform for antioxidant peptides. In this work, we developed a platform, AnOxPP, for prediction of antioxidant peptides using the bidirectional long short-term memory (BiLSTM) neural network. The sequence characteristics of peptides were converted into feature codes based on amino acid descriptors (AADs). Our results showed that the feature conversion ability of the combined-AADs optimized by the forward feature selection method was more accurate than that of the single-AADs. Especially, the model trained by the optimal descriptor SDPZ27 significantly outperformed the existing predictor on two independent test sets (Accuracy = 0.967 and 0.819, respectively). The SDPZ27-based AnOxPP learned four key structure-activity features of antioxidant peptides, with the following importance as steric properties > hydrophobic properties > electronic properties > hydrogen bond contributions. AnOxPP is a valuable tool for screening and design of peptide drugs, and the web-server is accessible at http://www.cqudfbp.net/AnOxPP/index.jsp.

Application of Molecular Simulation Methods in Food Science: Status and Prospects.

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Interaction mechanism of flavonoids and Tartary buckwheat bran protein: A fluorescence spectroscopic and 3D-QSAR study.

Tartary buckwheat bran protein (TBBP) is a valuable by-product of Tartary buckwheat processing. Current studies have indicated that TBBP is a desirable carrier and protector of flavonoids. However, the quantitative structure-affinity relationship of flavonoids with TBBP still remains to be elucidated. Here, by using the fluorescence spectroscopy method to measure the binding constants of 16 flavonoids with TBBP, we employ a combination of the atom-based three dimensional-quantitative structure-affinity relationships (3D-QSAR) and quantum-chemical calculation to explore the underlying binding mechanisms. We show that flavonoids can cause the intrinsic fluorescence quenching and form non-covalent complexes with TBBP driven by hydrogen bonding and van der Waal forces. The atom-based 3D-QSAR model reveals that the rutoside group at the C-7 position of flavones is favorable to enhance the binding constants of flavonoids with TBBP. Quantum-chemical calculations consistently disclose that the rutoside group can intensify interaction forces of flavonoids, thereby strengthening the binding. This work provides theoretical evidence that TBBP has the potential application to be raw materials for develop functional food, and be the delivery carriers of flavonoids.

Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning.

Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure-odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

Experimental and computational studies on the mechanism of the ß-lactoglobulin-derived peptide inhibiting the antigenicity of ß-lactoglobulin.

In this study, through a combined simulated enzymolysis-molecular docking-molecular simulation-activity determination-action mechanism strategy, we screened a ß-LG-derived peptide (VAGTWYSL) to inhibit the antigenicity of ß-LG and explored its mechanism of action. Our results indicate that the inhibitory effect of the peptide on the antigenicity of ß-LG is affected by different experimental conditions, including pH, reaction time and concentration. Three factors may contribute to the reduced allergenicity of ß-LG. First, there must be sufficient forces between the peptide and ß-LG, as a result, hydrophobic forces and hydrogen bonds are the main forces to maintain the structural stability of the complex. Second, the binding of the peptide changes the secondary structure of ß-LG, especially with an increase in α-helices and a decrease in ß-turns. Third, the peptide binds to the hydrophobic region of ß-LG, involving the antigenic epitope region Val41-Lys60, which may reduce the antigenicity.

DFBP: a comprehensive database of food-derived bioactive peptides for peptidomics research.

MOTIVATION: Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence-structure-activity relationship of FBPs in a large dataset. RESULTS: Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence-structure-activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. AVAILABILITY AND IMPLEMENTATION: DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation.

Atopic dermatitis (AD) is a common inflammatory skin disease involving multiple signaling pathways. One of the effective treatment strategies of AD is to develop a new drug capable of regulating the key therapeutic targets. Here we report the combination use of network analysis, deep learning, and molecular simulation for the identification of key therapeutic targets for AD and screening of potential multi-target drugs. From the TCM@Taiwan database, we identify a small molecule, namely caffeoyl malic acid (CMA), to inhibit the key therapeutic targets (TNFα and IL-4) for AD. CMA is further identified as a TNFα inhibitor by a deep learning model based on convolutional neural network. Molecular simulations demonstrate that CMA can stably bind to TNFα and IL-4, thereby producing diverse effects on the structural fluctuation, structural flexibility, looseness, and motion strength of each protein. Furthermore, conformation alignments reveal that CMA makes the distance between chain A and C of TNFα become wider and the slit between the two α helices of IL-4 get narrow obviously. CMA leads to the change of protein conformation, which hinders the formation of the protein-receptor complex. Collectively, our findings suggest that CMA is a potential dual TNFα/IL-4 inhibitor for the treatment of AD.

Mechanic-Driven Biodegradable Polyglycolic Acid/Silk Fibroin Nanofibrous Scaffolds Containing Deferoxamine Accelerate Diabetic Wound Healing.

The extracellular matrix (ECM), comprising of hundreds of proteins, mainly collagen, provides physical, mechanical support for various cells and guides cell behavior as an interactive scaffold. However, deposition of ECM, especially collagen content, is seriously impaired in diabetic wounds, which cause inferior mechanical properties of the wound and further delay chronic wound healing. Thus, it is critical to develop ECM/collagen alternatives to remodel the mechanical properties of diabetic wounds and thus accelerate diabetic wound healing. Here, we firstly prepared mechanic-driven biodegradable PGA/SF nanofibrous scaffolds containing DFO for diabetic wound healing. In our study, the results in vitro showed that the PGA/SF-DFO scaffolds had porous three-dimensional nanofibrous structures, excellent mechanical properties, biodegradability, and biocompatibility, which would provide beneficial microenvironments for cell adhesion, growth, and migration as an ECM/collagen alternative. Furthermore, the data in vivo showed PGA/SF-DFO scaffolds can adhere well to the wound and have excellent biodegradability, which is helpful to avoid secondary damage by omitting the removal process of scaffolds. The finite element analysis results showed that the application of silk fibroin-based scaffolds could significantly reduce the maximum stress around the wound. Besides, PGA/SF-DFO scaffolds induced collagen deposition, re-vascularization, recovered impaired mechanical properties up to about 70%, and ultimately accelerated diabetic wound healing within 14 days. Thus, our work provides a promising therapeutic strategy for clinically chronic wound healing.

Identification and molecular mechanism of a tri-peptide inhibitor targeting iNOS from duck embryo protein hydrolysates by experimental and bioinformatics studies.

Duck embryonic proteins are a promising source of food-derived functional peptides. Using a combination of experiments and bioinformatics approaches, a tri-peptide inhibitor YPW targeting iNOS was identified from duck embryo protein hydrolysates. Our results indicated that YPW could significantly inhibit LPS-induced NO generation in macrophages in a dose-dependent manner. YPW also significantly inhibited the expression of IL-6 and iNOS. Molecular simulations revealed that YPW could interact strongly with (iNOS) with a binding energy of -45.71 ± 17.75 kJ/mol. The stability of YPW-iNOS was maintained by the hydrogen bonds of amino acid residues Ile195, Gly196, Gly365, Glu371, Asn364, and Trp366, and the hydrophobic interactions by Trp188, Phe363, and Val346. In conclusion, our study provides a new idea for broadening the utilization of duck embryo proteins, and a strategy for the discovery of food-derived bioactive peptides.

Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network.

Identifying the taste characteristics of molecules is essential for the expansion of their application in health foods and drugs. It is time-consuming and consumable to identify the taste characteristics of a large number of compounds through experiments. To date, computational methods have become an important technique for identifying molecular taste. In this work, bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener are predicted using three structure-taste relationship models based on the convolutional neural networks (CNN), multi-layer perceptron (MLP)-Descriptor, and MLP-Fingerprint. The results showed that all three models have unique characteristics in the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener. For the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener, the MLP-Fingerprint model exhibited a higher predictive AUC value (0.94, 0.94 and 0.95) than the MLP-Descriptor model (0.94, 0.84 and 0.87) and the CNN model (0.88, 0.90 and 0.91) by external validation, respectively. The MLP-Descriptor model showed a distinct structure-taste relationship of the studied molecules, which helps to understand the key properties associated with bitterants and sweeteners. The CNN model requires only a simple 2D chemical map as input to automate feature extraction for favorable prediction. The obtained models achieved accurate predictions of bitterant/non-bitterant, sweetener/non-sweetener and bitterant and sweetener, providing vital references for the identification of bioactive molecules and toxic substances.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends.

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.