Gut colonization by Bacteroides requires translation by an EF-G paralog lacking GTPase activity.

Protein synthesis is crucial for cell growth and survival yet one of the most energy-consuming cellular processes. How, then, do cells sustain protein synthesis under starvation conditions when energy is limited? To accelerate the translocation of mRNA-tRNAs through the ribosome, bacterial elongation factor G (EF-G) hydrolyzes energy-rich guanosine triphosphate (GTP) for every amino acid incorporated into a protein. Here, we identify an EF-G paralog-EF-G2-that supports translocation without hydrolyzing GTP in the gut commensal bacterium Bacteroides thetaiotaomicron. EF-G2's singular ability to sustain protein synthesis, albeit at slow rates, is crucial for bacterial gut colonization. EF-G2 is ~10-fold more abundant than canonical EF-G1 in bacteria harvested from murine ceca and, unlike EF-G1, specifically accumulates during carbon starvation. Moreover, we uncover a 26-residue region unique to EF-G2 that is essential for protein synthesis, EF-G2 dissociation from the ribosome, and responsible for the absence of GTPase activity. Our findings reveal how cells curb energy consumption while maintaining protein synthesis to advance fitness in nutrient-fluctuating environments.

Dual-polarization RF channelizer based on microcombs.

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. Two high-Q micro-ring resonators (MRRs) with slightly different free spectral ranges (FSRs) are used: one MRR is pumped to yield soliton crystal microcombs ("active"), and the other MRR is used as a "passive" periodic optical filter supporting dual-polarization operation to slice the RF spectrum. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital-compatible bandwidths via the Vernier effect. Due to the use of dual-polarization states, the number of channelized spectral segments, and thus the RF instantaneous bandwidth (with a certain spectral resolution), can be doubled. In our experiments, we used 20 microcomb lines with ∼ 49 GHz FSR to achieve 20 channels for each polarization, with high RF spectra slicing resolutions at 144 MHz (TE) and 163 MHz (TM), respectively; achieving an instantaneous RF operation bandwidth of 3.1 GHz (TE) and 2.2 GHz (TM). Our approach paves the path towards monolithically integrated photonic RF receivers (the key components - active and passive MRRs are all fabricated on the same platform) with reduced complexity, size, and unprecedented performance, which is important for wide RF applications with digital-compatible signal detection.

Fluorescence color change of supramolecular polymer networks controlled by crown ether-cation recognition.

We report a fluorescent supramolecular polymer networks (SPNs) system based on crown ether-cation recognition. The polymer side chains bear ammonium cations, which can be recognized by host molecules with a B15C5 unit and a quinoline group at each end. The quinoline group makes the host molecule exhibit blue fluorescence. After the formation of SPNs, the recognition of the crown ether-cation transforms the blue fluorescence into yellow fluorescence. The accompanying fluorescence color change during the formation of SPNs makes it with potential applications in the fields of display, printing, information storage, and bioimaging.

NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation.

Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.

Building a Kokumi Database and Machine Learning-Based Prediction: A Systematic Computational Study on Kokumi Analysis.

Kokumi is a subtle sensation characterized by a sense of fullness, continuity, and thickness. Traditional methods of taste discovery and analysis, including those of kokumi, have been labor-intensive and costly, thus necessitating the emergence of computational methods as critical strategies in molecular taste analysis and prediction. In this study, we undertook a comprehensive analysis, prediction, and screening of the kokumi compounds. We categorized 285 kokumi compounds from a previously unreleased kokumi database into five groups based on their molecular characteristics. Moreover, we predicted kokumi/non-kokumi and multi-flavor compositions using six structure-taste relationship models: MLP-E3FP, MLP-PLIF, MLP-RDKFP, SVM-RDKFP, RF-RDKFP, and WeaveGNN feature of Atoms and Bonds. These six predictors exhibited diverse performance levels across two different models. For kokumi/non-kokumi prediction, the WeaveGNN model showed an exceptional predictive AUC value (0.94), outperforming the other models (0.87, 0.90, 0.89, 0.92, and 0.78). For multi-flavor prediction, the MLP-E3FP model demonstrated a higher predictive AUC and MCC value (0.94 and 0.74) than the others (0.73 and 0.33; 0.92 and 0.70; 0.95 and 0.73; 0.94 and 0.64; and 0.88 and 0.69). This data highlights the model's proficiency in accurately predicting kokumi molecules. As a result, we sourced kokumi active compounds through a high-throughput screening of over 100 million molecules, further refined by toxicity and similarity screening. Lastly, we launched a web platform, KokumiPD (https://www.kokumipd.com/), offering a comprehensive kokumi database and online prediction services for users.

Exploring Bitter and Sweet: The Application of Large Language Models in Molecular Taste Prediction.

The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

Mlp4green: A Binary Classification Approach Specifically for Green Odor.

Fresh green leaves give off a smell known as "green odor." It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.

Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins.

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays.

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

Network Pharmacology Combined with Machine Learning to Reveal the Action Mechanism of Licochalcone Intervention in Liver Cancer.

There are reports indicating that licochalcones can inhibit the proliferation, migration, and invasion of cancer cells by promoting the expression of autophagy-related proteins, inhibiting the expression of cell cycle proteins and angiogenic factors, and regulating autophagy and apoptosis. This study aims to reveal the potential mechanisms of licochalcone A (LCA), licochalcone B (LCB), licochalcone C (LCC), licochalcone D (LCD), licochalcone E (LCE), licochalcone F (LCF), and licochalcone G (LCG) inhibition in liver cancer through computer-aided screening strategies. By using machine learning clustering analysis to search for other structurally similar components in licorice, quantitative calculations were conducted to collect the structural commonalities of these components related to liver cancer and to identify key residues involved in the interactions between small molecules and key target proteins. Our research results show that the seven licochalcones molecules interfere with the cancer signaling pathway via the NF-κB signaling pathway, PDL1 expression and PD1 checkpoint pathway in cancer, and others. Glypallichalcone, Echinatin, and 3,4,3',4'-Tetrahydroxy-2-methoxychalcone in licorice also have similar structures to the seven licochalcones, which may indicate their similar effects. We also identified the key residues (including ASN364, GLY365, TRP366, and TYR485) involved in the interactions between ten flavonoids and the key target protein (nitric oxide synthase 2). In summary, we provide valuable insights into the molecular mechanisms of the anticancer effects of licorice flavonoids, providing new ideas for the design of small molecules for liver cancer drugs.

Functionalized Fullerene Potentially Inhibits SARS-CoV-2 Infection by Modulating Spike Protein Conformational Changes.

The disease of SARS-CoV-2 has caused considerable morbidity and mortality globally. Spike proteins on the surface of SARS-CoV-2 allow it to bind with human cells, leading to infection. Fullerenes and their derivatives are promising SARS-CoV-2 inhibitors and drug-delivery vehicles. In this study, Gaussian accelerated molecular dynamics simulations and the Markov state model were employed to delve into the inhibitory mechanism of Fullerene-linear-polyglycerol-b-amine sulfate (F-LGPS) on spike proteins. During the study, it was discovered that fullerene derivatives can operate at the interface of the receptor-binding domain (RBD) and the N-terminal domain (NTD), keeping structural domains in a downward conformation. It was also observed that F-LGPS demonstrated superior inhibitory effects on the XBB variant in comparison to the wild-type variant. This study yielded invaluable insights for the potential development of efficient therapeutics targeting the spike protein of SARS-CoV-2.

Probing the Mechanisms of Inhibitors Binding to Presenilin Homologue Using Molecular Dynamics Simulations.

γ-secretase is an intramembrane proteolytic enzyme that is mainly involved in the cleavage and hydrolysis of the amyloid precursor (APP). The catalytic subunit presenilin 1 (PS1) is the catalytic subunit of γ-secretase. Since it was found that PS1 is responsible for Aß-producing proteolytic activity, which is involved in Alzheimer's disease, it is believed that reducing the activity of PS1 and preventing or delaying the production of Aß could help treat Alzheimer's disease. Consequently, in recent years, researchers have begun investigating the potential clinical efficacy of PS1 inhibitors. Currently, most PS1 inhibitors are only used as a tool to study the structure and function of PS1, and a few inhibitors with a high selectivity have been tested in clinics. Less-selective PS1 inhibitors were found to not only inhibit Aß production but also inhibit Notch cleavage, which led to serious adverse events. The archaeal presenilin homologue (PSH) is a surrogate protease of presenilin that is useful for agent screening. In this study, we performed 200 ns molecular dynamics simulations (MD) of four systems to explore the conformational changes of different ligands binding to PSH. Our results indicated that the PSH-L679 system formed 3-10 helices in TM4, loosening up TM4 and allowing substrates to enter the catalytic pocket, thereby making it less inhibitory. Additionally, we found that III-31-C can bring TM4 and TM6 closer, resulting in the contraction of the PSH active pocket. Altogether, these results provide the basis for the potential design of newer PS1 inhibitors.

Investigating the Vital Role of the Identified Abietic Acid from Helianthus annuus L. Calathide Extract against Hyperuricemia via Human Embryonic Kidney 293T Cell Model.

To explore the anti-hyperuricemia components in sunflower (Helianthus annuus L.) calathide extract (SCE), we identified abietic acid (AA) via liquid chromatography-mass spectrometry and found an excellent inhibitor of xanthine oxidase (IC50 = 10.60 µM, Ki = 193.65 nM) without cytotoxicity. Based on the transcriptomics analysis of the human embryonic kidney 293T cell model established using 1 mM uric acid, we evaluated that AA showed opposite modulation of purine metabolism to the UA group and markedly suppressed the intensity of purine nucleoside phosphorylase, ribose phosphate pyrophosphokinase 2, and ribose 5-phosphate isomerase A. Molecular docking also reveals the inhibition of purine nucleoside phosphorylase and ribose phosphate pyrophosphokinase 1. The SCE exhibits similar regulation of these genes, so we conclude that AA was a promising component in SCE against hyperuricemia. This present study provided a novel cell model for screening anti-hyperuricemia natural drugs in vitro and illustrated that AA, a natural diterpenoid, is a potential inhibitor of purine biosynthesis or metabolism.

ISLR affects colon cancer progression by regulating the epithelial-mesenchymal transition signaling pathway.

This study aims to determine the mechanism of ISLR on the progression of colon cancer. TCGA database was used to analyze ISLR expression in colon cancer tumor tissues. QRT-PCR and western blotting were used to detect ISLR expression in colon cancer cells. CCK-8, colony formation, EDU, wound healing and transwell assays were used to measure cell viability, proliferation, migration and invasion of colon cancer cells, respectively. The signaling pathway enrichment analysis of ISLR was analyzed on the basis of the KEGG database. The protein expression of genes related to signaling pathway was measured by western blotting. Results of TCGA analysis, qRT-PC and western blotting showed that ISLR was upregulated in colon cancer tumor tissues and cells. High level of ISLR was related to low overall survival of patients with colon cancer. ISLR silence significantly inhibited cell viability, proliferation, migration and invasion of colon cancer cells. ISLR overexpression markedly enhanced the cell viability, proliferation, migration and invasion of colon cancer cells. KEGG database analyzed showed that ISLR can activate the EMT signaling pathway. Inhibition of the EMT signaling pathway can suppress the growth, migration, and invasion of colon cancer cells and eliminate the promoted effect of ISLR overexpression on colon cancer progression. ISLR promotes the progression of colon cancer by activating the EMT signaling pathway.

Dietary sugar silences a colonization factor in a mammalian gut symbiont.

The composition of the gut microbiota is largely determined by environmental factors including the host diet. Dietary components are believed to influence the composition of the gut microbiota by serving as nutrients to a subset of microbes, thereby favoring their expansion. However, we now report that dietary fructose and glucose, which are prevalent in the Western diet, specifically silence a protein that is necessary for gut colonization, but not for utilization of these sugars, by the human gut commensal Bacteroides thetaiotaomicron Silencing by fructose and glucose requires the 5' leader region of the mRNA specifying the protein, designated Roc for regulator of colonization. Incorporation of the roc leader mRNA in front of a heterologous gene was sufficient for fructose and glucose to turn off expression of the corresponding protein. An engineered strain refractory to Roc silencing by these sugars outcompeted wild-type B. thetaiotaomicron in mice fed a diet rich in glucose and sucrose (a disaccharide composed of glucose and fructose), but not in mice fed a complex polysaccharide-rich diet. Our findings underscore a role for dietary sugars that escape absorption by the host intestine and reach the microbiota: regulation of gut colonization by beneficial microbes independently of supplying nutrients to the microbiota.

Water compatible supramolecular polymers: recent progress.

Water compatible supramolecular polymers (WCSPs) combine aqueous compatibility with the reversibility and environmental responsiveness of supramolecular polymers. WCSPs have seen application across a number of fields, including stimuli-responsive materials, healable materials, and drug delivery, and are attracting increasing attention from the design, synthesis, and materials perspectives. In this review, we summarize the chemistry of WCSPs from 2016 to mid-2021. For the sake of discussion, we divide WCSPs into five categories based on the core supramolecular approaches at play, namely hydrogen-bonding arrays, electrostatic interactions, large π-conjugated subunits, host-guest interactions, and peptide-based systems, respectively. We discuss both synthesis and polymer structure, as well as the underlying design expectations. The goal of this overview is to deepen our understanding of the strategies that have been exploited to prepare WCSPs, as well as their properties and uses. Thus, a section devoted to potential applications is included in this review.

Clustering Analysis, Structure Fingerprint Analysis, and Quantum Chemical Calculations of Compounds from Essential Oils of Sunflower (Helianthus annuus L.) Receptacles.

Sunflower (Helianthus annuus L.) is an appropriate crop for current new patterns of green agriculture, so it is important to change sunflower receptacles from waste to useful resource. However, there is limited knowledge on the functions of compounds from the essential oils of sunflower receptacles. In this study, a new method was created for chemical space network analysis and classification of small samples, and applied to 104 compounds. Here, t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensions were used to reduce coordinates as node locations and edge connections of chemical space networks, respectively, and molecules were grouped according to whether the edges were connected and the proximity of the node coordinates. Through detailed analysis of the structural characteristics and fingerprints of each classified group, our classification method attained good accuracy. Targets were then identified using reverse docking methods, and the active centers of the same types of compounds were determined by quantum chemical calculation. The results indicated that these compounds can be divided into nine groups, according to their mean within-group similarity (MWGS) values. The three families with the most members, i.e., the d-limonene group (18), α-pinene group (10), and γ-maaliene group (nine members) determined the protein targets, using PharmMapper. Structure fingerprint analysis was employed to predict the binding mode of the ligands of four families of the protein targets. Thence, quantum chemical calculations were applied to the active group of the representative compounds of the four families. This study provides further scientific information to support the use of sunflower receptacles.

In Silico Discovery of Anticancer Peptides from Sanghuang.

Anticancer peptide (ACP) is a short peptide with less than 50 amino acids that has been discovered in a variety of foods. It has been demonstrated that traditional Chinese medicine or food can help treat cancer in some cases, which suggests that ACP may be one of the therapeutic ingredients. Studies on the anti-cancer properties of Sanghuangporus sanghuang have concentrated on polysaccharides, flavonoids, triterpenoids, etc. The function of peptides has not received much attention. The purpose of this study is to use computer mining techniques to search for potential anticancer peptides from 62 proteins of Sanghuang. We used mACPpred to perform sequence scans after theoretical trypsin hydrolysis and discovered nine fragments with an anticancer probability of over 0.60. The study used AlphaFold 2 to perform structural modeling of the first three ACPs discovered, which had blast results from the Cancer PPD database. Using reverse docking technology, we found the target proteins and interacting residues of two ACPs with an unknown mechanism. Reverse docking results predicted the binding modes of the ACPs and their target protein. In addition, we determined the active part of ACPs by quantum chemical calculation. Our study provides a framework for the future discovery of functional peptides from foods. The ACPs discovered have the potential to be used as drugs in oncology clinical treatment after further research.

Recent Advances in the Heterogeneous Photocatalytic Hydroxylation of Benzene to Phenol.

Phenol is an important chemical material that is widely used in industry. Currently, phenol is dominantly produced by the well-known three-step cumene process, which suffers from severe drawbacks. Therefore, developing a green, sustainable, and economical strategy for the production of phenol directly from benzene is urgently needed. In recent years, the photocatalytic hydroxylation of benzene to phenol, which is economically feasible and could be performed under mild conditions, has attracted more attention, and development of highly efficient photocatalyst would be a key issue in this field. In this review, we systematically introduce the recent achievements of photocatalytic hydroxylation of benzene to phenol from 2015 to mid-2022, and various heterogeneous photocatalysts are comprehensively reviewed, including semiconductors, polyoxometalates (POMs), graphitic carbon nitride (g-C3N4), metal-organic frameworks (MOFs), carbon materials, and some other types of photocatalysts. Much effort is focused on the physical and chemical approaches for modification of these photocatalysts. The challenges and future promising directions for further enhancing the catalytic performances in photocatalytic hydroxylation of benzene are discussed in the end.

High Levels of LINC01140 Expression Predict a Good Prognosis and Improve Radiotherapy in Sarcoma Patients.

Long intergenic non-protein coding RNA has an important biological role in tumors. But, LINC01140 in sarcoma has not been studied yet. This study investigates the expression and prognosis role of LINC01140 in sarcoma. LINC01140 was lower in metastatic sarcoma, and low LINC01140 expression predicted poor overall survival, disease-free survival, and disease-specific survival in sarcoma. High LINC01140 expression and radiotherapy could promote survival of sarcoma. Gene set enrichment analysis showed LINC01140 was involved in interferon-gamma response, epithelial-mesenchymal transition, the interaction between cytokine receptors, and cholesterol homeostasis. Gene ontology enrichment analysis showed LINC01140 was involved in immunity, fatty acid metabolism, amino acid metabolism, cell division, serine/threonine-protein kinase. LINC01140 expression was negatively correlated with various epithelial-mesenchymal transition factors and positively correlated with the expression of anti-cancer factor hypermethylated-in-cancer 1. These results confirmed that LINC01140 may be a potential novel prognostic molecule in sarcoma.