Molecular Dynamics Simulation Combined with Neural Relationship Inference and Markov Model to Reveal the Relationship between Conformational Regulation and Bioluminescence Properties of Gaussia Luciferase.

Gaussia luciferase (Gluc) is currently known as the smallest naturally secreted luciferase. Due to its small molecular size, high sensitivity, short half-life, and high secretion efficiency, it has become an ideal reporter gene and is widely used in monitoring promoter activity, studying protein-protein interactions, protein localization, high-throughput drug screening, and real-time monitoring of tumor occurrence and development. Although studies have shown that different Gluc mutations exhibit different bioluminescent properties, their mechanisms have not been further investigated. The purpose of this study is to reveal the relationship between the conformational changes of Gluc mutants and their bioluminescent properties through molecular dynamics simulation combined with neural relationship inference (NRI) and Markov models. Our results indicate that, after binding to the luciferin coelenterazine (CTZ), the α-helices of the 109-119 residues of the Gluc Mutant2 (GlucM2, the flash-type mutant) are partially unraveled, while the α-helices of the same part of the Gluc Mutant1 (GlucM1, the glow-type mutant) are clearly formed. The results of Markov flux analysis indicate that the conformational differences between glow-type and flash-type mutants when combined with luciferin substrate CTZ mainly involve the helicity change of α7. The most representative conformation and active pocket distance analysis indicate that compared to the flash-type mutant GlucM2, the glow-type mutant GlucM1 has a higher degree of active site closure and tighter binding. In summary, we provide a theoretical basis for exploring the relationship between the conformational changes of Gluc mutants and their bioluminescent properties, which can serve as a reference for the modification and evolution of luciferases.

Specific Substrate Activity of Lotus Root Polyphenol Oxidase: Insights from Gaussian-Accelerated Molecular Dynamics and Markov State Models.

Polyphenol oxidase (PPO) plays a key role in the enzymatic browning process, and this study employed Gaussian-accelerated molecular dynamics (GaMD) simulations to investigate the catalytic efficiency mechanisms of lotus root PPO with different substrates, including catechin, epicatechin, and chlorogenic acid, as well as the inhibitor oxalic acid. Key findings reveal significant conformational changes in PPO that correlate with its enzymatic activity. Upon substrate binding, the alpha-helix in the Q53-D63 region near the copper ion extends, likely stabilizing the active site and enhancing catalysis. In contrast, this helix is disrupted in the presence of the inhibitor, resulting in a decrease in enzymatic efficiency. Additionally, the F350-V378 region, which covers the substrate-binding site, forms an alpha-helix upon substrate binding, further stabilizing the substrate and promoting catalytic function. However, this alpha-helix does not form when the inhibitor is bound, destabilizing the binding site and contributing to inhibition. These findings offer new insights into the substrate-specific and inhibitor-induced structural dynamics of lotus root PPO, providing valuable information for enhancing food processing and preservation techniques.

Switching engineered Vitreoscilla hemoglobin into carbene transferase for enantioselective SH insertion.

An attractive strategy for efficiently forming CS bonds is through the use of diazo compounds SH insertion. However, achieving good enantioselective control in this reaction within a biocatalytic system has proven to be challenging. This study aimed to enhance the activity and enantioselectivity of to enable asymmetric SH insertion. The researchers conducted site-saturation mutagenesis (SSM) on 5 amino acid residues located around the iron carbenoid intermediate within a distance of 5 Å, followed by iterative saturation mutagenesis (ISM) of beneficial mutants. Through this process, the beneficial variant VHbSH(P54R/V98W) was identified through screening with 4-(methylmercapto) phenol as the substrate. This variant exhibited up to 4-fold higher catalytic efficiency and 6-fold higher enantioselectivity compared to the wild-type VHb. Computational studies were also conducted to elucidate the detailed mechanism of this asymmetric SH insertion, explaining how active-site residues accelerate this transformation and provide stereocontrol.

Regulating Strain and Electronic Structure of Indium Tin Oxide Supported IrOx Electrocatalysts for Highly Efficient Oxygen Evolution Reaction in Acid.

The development of proton exchange membrane water electrolysis is a promising technology for hydrogen production, which has always been restricted by the slow kinetics of the oxygen evolution reaction (OER). Although IrOx is one of the benchmark acidic OER electrocatalysts, there are still challenges in designing highly active and stable Ir-based electrocatalysts for commercial application. Herein, a Ru-doped IrOx electrocatalyst with abundant twin boundaries (TB-Ru0.3Ir0.7Ox@ITO) is reported, employing indium tin oxide with high conductivity as the support material. Combing the TB-Ru0.3Ir0.7Ox nanoparticles with ITO support could expose more active sites and accelerate the electron transfer. The TB-Ru0.3Ir0.7Ox@ITO exhibits a low overpotential of 203 mV to achieve 10 mA cm-2 and a high mass activity of 854.45 A g-1noble metal at 1.53 V vs RHE toward acidic OER, which exceeds most reported Ir-based OER catalysts. Moreover, improved long-term stability could be obtained, maintaining the reaction for over 110 h at 10 mA cm-2 with negligible deactivation. DFT calculations further reveal the activity enhancement mechanism, demonstrating the synergistic effects of Ru doping and strains on the optimization of the d-band center (εd) position and the adsorption free energy of oxygen intermediates. This work provides ideas to realize the trade-off between high catalytic activity and good stability for acidic OER electrocatalysts.

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/.

Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds.

[The effect of narrow band imaging in CO2 laser therapy in early-stage glottic cancer].

Objective:To explore efficacy of narrow band imaging（NBI） technique in CO2laser therapy in Early-Stage Glottic cancer. Methods:The clinical data of patients with Early-Stage Glottic cancer who underwent CO2laser vocal cord resection from June 2011 to August 2022 were retrospectively analyzed. Among these, 27 patients who underwent surgery assisted by NBI were assigned to the observation group, while 25 patients who underwent conventional CO2 laser microsurgery with a suspension laryngoscope were assigned to the control group. The differences between the two groups were analyzed in terms of intraoperative frozen pathology results, postoperative recurrence rates, 5-year cumulative disease-free survival rates, complications, and voice recovery. Results:All 52 patients were operated successfully. Temporary tracheostomy and serious complications did not occur during the operation. The postoperative patient's pronunciation was satisfactory. One patient experienced vocal cord adhesion, but there were no severe complications such as breathing difficulties or bleeding, with an overall complication rate of 1.92%. Postoperative follow-up was 1-5 years. The 5 years recurrence free survival in the general group was 77.90％, and the 5 years recurrence free survival in the NBI group was 100％, the difference was statistically significant（P<0.05）. NBI endoscopy is safer and more accurate than the general group in determining the safe margin of tumor mucosal resection（P<0.05）. Among the patients who accepted the voice analysis, the difference was no statistically significant（P>0.05）. Conclusion:Compared with conventional CO2laser surgery under microscope, NBI guided laser resection of Early-Stage Glottic cancer is more accurate. NBI guided laser resection could improve 5 years recurrence free survival rate. In a word, narrow-band imaging endoscopy can has very high value in clinical application.

Molecular insights into chronic atrophic gastritis treatment: Coptis chinensis Franch studied via network pharmacology, molecular dynamics simulation and experimental analysis.

Chronic atrophic gastritis (CAG), characterized by inflammation and erosion of the gastric lining, is a prevalent digestive disorder and considered a precursor to gastric cancer (GC). Coptis chinensis France (CCF) is renowned for its potent heat-clearing, detoxification, and anti-inflammatory properties. Zuojin Pill (ZJP), a classic Chinese medicine primarily composed of CCF, has demonstrated effectiveness in CAG treatment. This study aims to elucidate the potential mechanism of CCF treatment for CAG through a multifaceted approach encompassing network pharmacology, molecular docking, molecular dynamics simulation and experimental verification. The study identified three major active compounds of CCF and elucidated key pathways, such as TNF signaling, PI3K-Akt signaling and p53 signaling. Molecular docking revealed interactions between these active compounds and pivotal targets like PTGS2, TNF, MTOR, and TP53. Additionally, molecular dynamics simulation validated berberine as the primary active compound of CCF, which was further confirmed through experimental verification. This study not only identified berberine as the primary active compound of CCF but also provided valuable insights into the molecular mechanisms underlying CCF's efficacy in treating CAG. Furthermore, it offers a reference for refining therapeutic strategies for CAG management.

PredCoffee: A binary classification approach specifically for coffee odor.

Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.

VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach.

Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.

Directed evolution of Escherichia coli surface-displayed Vitreoscilla hemoglobin as an artificial metalloenzyme for the synthesis of 5-imino-1,2,4-thiadiazoles.

Artificial metalloenzymes (ArMs) are constructed by anchoring organometallic catalysts to an evolvable protein scaffold. They present the advantages of both components and exhibit considerable potential for the in vivo catalysis of new-to-nature reactions. Herein, Escherichia coli surface-displayed Vitreoscilla hemoglobin (VHbSD-Co) that anchored the cobalt porphyrin cofactor instead of the original heme cofactor was used as an artificial thiourea oxidase (ATOase) to synthesize 5-imino-1,2,4-thiadiazoles. After two rounds of directed evolution using combinatorial active-site saturation test/iterative saturation mutagenesis (CAST/ISM) strategy, the evolved six-site mutation VHbSD-Co (6SM-VHbSD-Co) exhibited significant improvement in catalytic activity, with a broad substrate scope (31 examples) and high yields with whole cells. This study shows the potential of using VHb ArMs in new-to-nature reactions and demonstrates the applicability of E. coli surface-displayed methods to enhance catalytic properties through the substitution of porphyrin cofactors in hemoproteins in vivo.

Elucidating the structural basis of vitamin B12 derivatives as novel potent inhibitors of PTP1B: Insights from inhibitory mechanisms using Gaussian accelerated molecular dynamics (GaMD) and in vitro study.

Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 µM and 8.998 ± 0.587 µM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 µM and 4.04 µM respectively. To elucidate the inhibition mechanism, we conducted a 500 ns Gaussian accelerated molecular dynamics (GaMD) simulation. Utilizing PCA and tICA, we constructed Markov state models (MSM) to examine secondary structure changes during motion. Our findings revealed that the α-helix at residues 37-42 remained the most stable in the PTP1B-OHCbl Acetate system. Furthermore, upon binding of OHCbl Acetate or MeCbl, the WPD loop of PTP1B moved inward to the active pocket, forming a closed conformation and potentially obstructs substrate entry. Protein-ligand interaction analysis and MM-PBSA showed that OHCbl Acetate exhibited lower binding free energy and engaged in more residue interactions with PTP1B. In summary, our study confirmed the substantial inhibitory activity of OHCbl Acetate against PTP1B, with its inhibitory potency notably surpassing that of MeCbl. We demonstrated potential molecular mechanisms of OHCbl Acetate inhibiting PTP1B.

Using Gaussian accelerated molecular dynamics combined with Markov state models to explore the mechanism of action of new oral inhibitors on Complex I.

In this study, our focus was on investigating H-1,2,3-triazole derivative HP661 as a novel and highly efficient oral OXPHOS inhibitor, with its molecular-level inhibitory mechanism not yet fully understood. We selected the ND1, NDUFS2, and NDUFS7 subunits of Mitochondrial Complex I as the receptor proteins and established three systems for comparative analysis: protein-IACS-010759, protein-lead compound 10, and protein-HP661. Through extensive analysis involving 500 ns Gaussian molecular dynamics simulations, we gained insights into these systems. Additionally, we constructed a Markov State Models to examine changes in secondary structures during the motion processes. The research findings suggest that the inhibitor HP661 enhances the extensibility and hydrophilicity of the receptor protein. Furthermore, HP661 induces the unwinding of the α-helical structure in the region of residues 726-730. Notably, key roles were identified for Met37, Phe53, and Pro212 in the binding of various inhibitors. In conclusion, we delved into the potential molecular mechanisms of triazole derivative HP661 in inhibiting Complex I. These research outcomes provide crucial information for a deeper understanding of the mechanisms underlying OXPHOS inhibition, offering valuable theoretical support for drug development and disease treatment design.

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.

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.

Synthesis of Indolyl Phenyl Diketones through Visible-Light-Promoted Ni-Catalyzed Intramolecular Cyclization/Oxidation Sequence of Ynones.

The combination of visible light catalysis and Ni catalysis has enabled the synthesis of indolyl phenyl diketones through the cyclization/oxidation process of ynones. This reaction proceeded under mild and base-free conditions and showed a broad scope and feasibility for gram-scale synthesis. Several natural products and biologically interesting molecules could be readily postfunctionalized by this method.

Prediction of bitterness based on modular designed graph neural network.

Motivation: Bitterness plays a pivotal role in our ability to identify and evade harmful substances in food. As one of the five tastes, it constitutes a critical component of our sensory experiences. However, the reliance on human tasting for discerning flavors presents cost challenges, rendering in silico prediction of bitterness a more practical alternative. Results: In this study, we introduce the use of Graph Neural Networks (GNNs) in bitterness prediction, superseding traditional machine learning techniques. We developed an advanced model, a Hybrid Graph Neural Network (HGNN), surpassing conventional GNNs according to tests on public datasets. Using HGNN and three other GNNs, we designed BitterGNNs, a bitterness predictor that achieved an AUC value of 0.87 in both external bitter/non-bitter and bitter/sweet evaluations, outperforming the acclaimed RDKFP-MLP predictor with AUC values of 0.86 and 0.85. We further created a bitterness prediction website and database, TastePD (https://www.tastepd.com/). The BitterGNNs predictor, built on GNNs, offers accurate bitterness predictions, enhancing the efficacy of bitterness prediction, aiding advanced food testing methodology development, and deepening our understanding of bitterness origins. Availability and implementation: TastePD can be available at https://www.tastepd.com, all codes are at https://github.com/heyigacu/BitterGNN.

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.

Exploring the anti-gout potential of sunflower receptacles alkaloids: A computational and pharmacological analysis.

Gout, a painful condition marked by elevated uric acid levels often linked to the diet's high purine and alcohol content, finds a potential treatment target in xanthine oxidase (XO), a crucial enzyme for uric acid production. This study explores the therapeutic properties of alkaloids extracted from sunflower (Helianthus annuus L.) receptacles against gout. By leveraging computational chemistry and introducing a novel R-based clustering algorithm, "TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)," we assessed 231 alkaloid molecules from sunflower receptacles. Our clustering analysis pinpointed six alkaloids with significant gout-targeting potential, particularly emphasizing the fifth cluster's XO inhibition capabilities. Through molecular docking and the BatchDTA prediction model, we identified three top compounds-2-naphthylalanine, medroxalol, and fenspiride-with the highest XO affinity. Further molecular dynamics simulations assessed their enzyme active site interactions and binding free energies, employing MM-PBSA calculations. This investigation not only highlights the discovery of promising compounds within sunflower receptacle alkaloids via LC-MS but also introduces medroxalol as a novel gout treatment candidate, showcasing the synergy of computational techniques and LC-MS in drug discovery.