Accurate prediction for adsorption rate of peptides with high ACE-inhibitory activity from sericin hydrolysate on thiophene hypercross-linked polymer using CoMSIA in 3D-QSAR model

Antioxidant potential of peptides from poultry hemoglobin via probiotic-assisted hydrolysis: Deciphering mechanisms at the cellular level and through molecular dynamics simulations

Achieving the therapeutic goal of treating diseases by effectively controlling the excessive accumulation of intracellular free radicals is still very challenging, which motivates researchers to develop efficient novel antioxidant peptides from sustainable resources continuously. This study first pioneered a probiotic-assisted enzymatic hydrolysis of hemoglobin, which obtained 149 peptides. Two antioxidant peptides were rapidly screened using advanced molecular dynamics simulation techniques, revealing their molecular interaction mechanisms with Keap1. It was found that GLWGKV occupied six binding sites for Keap1 to form hydrogen bonds with Nrf2, whereas LIVYPW occupied two binding sites, and the binding free energy of GLWGKV to Keap1 was lower binding more stable. Cellular experiments confirmed that GLWGKV up-regulated the expression of related proteins and increased antioxidant enzyme activities, thereby attenuating H2O2-induced oxidative damage in Caco-2 cells. This research increases the economic added value of animal blood and demonstrates its great potential for development in functional foods.

Novel anti-oxidative peptides from equine hemoplasma protein hydrolysates: Purification, identification and protective effects on Caco-2 cells

In this study, we purified and identified antioxidant peptides from equine plasma protein hydrolysates and assessed their protective effects against H2O2-induced oxidative stress in Caco-2 cells. Four antioxidant peptides were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in equine plasma protein hydrolysate, namely: GTMVGC (567.69 Da), FGMTST (662.88 Da), VGYHSHF (847.01 Da) and ALSPFFKE (939.18 Da). Among them, ALSPFFKE showed the strongest antidigestive properties after modelled digestion studies. Moreover, ALSPFFKE enhanced intracellular superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) activities while significantly reducing reactive oxygen species accumulation and malondialdehyde formation in Caco-2 cells. The molecular docking analysis suggested that ALSPFFKE achieves regulation of the Keap1-Nrf2 pathway mainly by forming multiple hydrogen bonds and hydrophobic interactions with key amino acids (Arg380, Ser555, Gln530, Tyr334) in Keap1. These findings suggested that equine plasma peptides hold significant promise for the development of novel, potent, and stable antioxidant functional foods.

Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE)

Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to identify novel angiotensin I-converting enzyme (ACE) inhibitors from bioactive peptides derived from food sources, particularly highland barley proteins, addressing the gap in effective natural ACE inhibitors. This research employs a machine learning-based pipeline combined with peptidomics to screen for ACE-inhibitory peptides, Gradient Boosted Decision Trees (GBDT) with the best performance among four tested models was used to predict the ACE-inhibitory capacity of peptides derived from papain-hydrolyzed highland barley protein. The selected peptides were validated through computer simulations and in vitro experiments, with FPRPFL identified as the most potent ACE-inhibitor (IC50 = 1.18 μM). Enzyme inhibition kinetics and digestion stability simulations were used to investigate its inhibition mode and stability. The binding mode and mechanism of action of FPRPFL with ACE were further analyzed using circular dichroism, molecular docking and molecular dynamics simulations. Network pharmacology revealed its multi-target and multi-pathway antihypertensive properties. The integration of machine learning and in vitro experiments enables accurate bioactive peptides identification and comprehensive their functionality analysis, establishing a valuable pipeline for elucidating peptide mechanisms and laying a solid foundation for industrial-scale production of natural ACE-inhibitors.

Sericin coats of silk fibres, a degumming waste or future material?

Silk is a fibrous biopolymer with a recorded history in the textile industries for centuries. This fibre is constituted of two different proteins: fibroin and sericin, of which the latter accounting for approximately 20-30 % of the silk mass. Silk sericin (SSER) was previously considered as a waste by-product in silk fibroin extraction. SSER has recently garnered significant scientific interest due to its extensive biological and pharmacological properties. These include antioxidant effects, biocompatibility, low immunogenicity, controlled biodegradability, and the ability to induce cell proliferation. This review covers studies about various aspects of this emerging material, namely, its general morphology, specific structure, molecular weight, features of different layers, and gene sequences. The impact of different extraction methods and the application of extracted SSER based on molecular weight are discussed. Additionally, the characteristic functional groups in the amino acids of sericin facilitate its applications in regenerative medicine, wound healing, drug delivery, textile, environment, and energy, in various forms like hydrogels, films, scaffolds, and conduits. SSER-based materials offer great potentials for multi-functional applications in the upcoming decades, showcasing adaptability for various functional uses and promising future technological advancements.

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Molecular design of environment-friendly amide herbicide substitutes with high efficacy, low phytotoxicity and medication safety

The primary goal of this investigation was to formulate an ecologically sustainable alternative to amide herbicides (AHs) characterized by robust herbicidal effectiveness, minimal corn phytotoxicity, and commendable pharmaceutical safety. We employed comparative molecular similarity index analysis (CoMSIA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, which systematically outlined parameters such as herbicidal effectiveness, corn phytotoxicity, and AHs biodegradability. Subsequently, after thorough evaluation, we carefully selected a group of fourteen stable AH-substitute compounds known for their safety and environmental compatibility, considering aspects like pharmacokinetics, toxicokinetics, functional properties, and environmental friendliness. This resulted in a significant increase in herbicidal effectiveness, ranging from 21.64% to 34.07%, alongside a decrease in corn phytotoxicity within the range of 12.19-20.87%. Furthermore, we achieved an improvement in biodegradability, measured within the spectrum of 4.92-9.40%. Importantly, these changes also correlated with the reduction of hepatotoxicity, mutagenicity, and cutaneous health risks. Finally, we delved into the mechanisms underlying the improved herbicidal effectiveness, reduced corn phytotoxicity, and enhanced biodegradability of AHs substitutes through molecular docking and analysis of amino acid interactions. The investigation concluded that non-covalent forces governing the interaction between AHs substitutes and receptor proteins are crucial in determining herbicidal effectiveness, corn phytotoxicity, and biodegradability. Specifically, Van der Waals and electrostatic forces emerged as key factors governing the binding affinities of AH molecules with receptor proteins, both before and after modification. In summary, this study introduces innovative approaches in the field of agricultural chemical weeding technology and provides a theoretical framework for the environmentally responsible management of AHs herbicides.

Exposure to high-performance benzotriazole ultraviolet stabilizers: Advance in toxicological effects, environmental behaviors and remediation mechanism using in-silica methods

Benzotriazole ultraviolet stabilizers (BUVSs), as light stabilizers, have attracted widespread attention because of their easy migration in the environment and their acute toxicity and biological toxicity effects, such as immunotoxicity and hepatotoxicity. Accordingly, the treatment and remediation mechanisms of high-performance, environmentally friendly, and low human health risk BUVS substitutes were analyzed. Firstly, the weights and the comprehensive effect (CE) values of migration and toxicity of BUVSs were determined by Topsis assisted by the coefficient of variation (CV) method. From this, a three-dimensional quantitative structure activity relationship (3D-QSAR) model based on the CE values of the 13 BUVSs was constructed. Secondly, EPI software was used to predict the functionality and environmental friendliness of BUVS substitutes, and a partial least squares regression machine learning (ML-PLSR) model was used to analyze the mechanism. Then, ADMET (absorption, distribution, metabolism, excretion, toxicity), TOPKAT, and exposure dose models were used to evaluate the ecological and human health risks of BUVSs and their substitutes. Finally, the key charge information affecting the UV-326 substitutes was deduced by time dependent density functional theory (TDDFT). Using UV-326 as an example, 15 UV-326 substitutes with reduced CE values were designed (reductions of 2.61%-23.18%). Compared with ML-PLSR models of acute toxicity, immunotoxicity, and hepatotoxicity, it was found that the decrease of DM and Q_yy values and the increase of Q_zz value could further decrease the toxicity of the UV-326 substitutes. Ecological and human health risk assessment showed that the exposure risks of the six UV-326 substitutes were within acceptable limits. TDDFT showed that the change of electron distribution and electron excitation type were the key factors affecting the performance of the UV-326 substitutes, and a charge transfer excitation type was more conducive to obtaining high-performance, environmentally friendly UV-326 substitutes. This study aims to alleviate the toxic damage to the ecological environment and human health caused by BUVS exposure.