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.

Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy.

PURPOSE: Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. MATERIALS AND METHODS: A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. RESULTS: The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort. CONCLUSION: We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

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.

Design of Vif-Derived Peptide Inhibitors with Anti-HIV-1 Activity by Interrupting Vif-CBFß Interaction.

One of the major functions of the accessory protein Vif of human immunodeficiency virus type 1 (HIV-1) is to induce the degradation of APOBEC3 (A3) family proteins by recruiting a Cullin5-ElonginB/C-CBFß E3 ubiquitin ligase complex to facilitate viral replication. Therefore, the interactions between Vif and the E3 complex proteins are promising targets for the development of novel anti-HIV-1 drugs. Here, peptides are designed for the Vif-CBFß interaction based on the sequences of Vif mutants with higher affinity for CBFß screened by a yeast surface display platform. We identified two peptides, VMP-63 and VMP-108, that could reduce the infectivity of HIV-1 produced from A3G-positive cells with IC50 values of 49.4 µM and 55.1 µM, respectively. They protected intracellular A3G from Vif-mediated degradation in HEK293T cells, consequently increasing A3G encapsulation into the progeny virions. The peptides could rapidly enter cells after addition to HEK293T cells and competitively inhibit the binding of Vif to CBFß. Homology modeling analysis demonstrated the binding advantages of VMP-63 and VMP-108 with CBFß over their corresponding wild-type peptides. However, only VMP-108 effectively restricted long-term HIV-1 replication and protected A3 functions in non-permissive T lymphocytes. Our findings suggest that competitive Vif-derived peptides targeting the Vif-CBFß interaction are promising for the development of novel therapeutic strategies for acquired immune deficiency syndrome.

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.

Unveiling Anti-Diabetic Potential of Baicalin and Baicalein from Baikal Skullcap: LC-MS, In Silico, and In Vitro Studies.

Type 2 diabetes mellitus (T2DM) is marked by persistent hyperglycemia, insulin resistance, and pancreatic ß-cell dysfunction, imposing substantial health burdens and elevating the risk of systemic complications and cardiovascular diseases. While the pathogenesis of diabetes remains elusive, a cyclical relationship between insulin resistance and inflammation is acknowledged, wherein inflammation exacerbates insulin resistance, perpetuating a deleterious cycle. Consequently, anti-inflammatory interventions offer a therapeutic avenue for T2DM management. In this study, a herb called Baikal skullcap, renowned for its repertoire of bioactive compounds with anti-inflammatory potential, is posited as a promising source for novel T2DM therapeutic strategies. Our study probed the anti-diabetic properties of compounds from Baikal skullcap via network pharmacology, molecular docking, and cellular assays, concentrating on their dual modulatory effects on diabetes through Protein Tyrosine Phosphatase 1B (PTP1B) enzyme inhibition and anti-inflammatory actions. We identified the major compounds in Baikal skullcap using liquid chromatography-mass spectrometry (LC-MS), highlighting six flavonoids, including the well-studied baicalein, as potent inhibitors of PTP1B. Furthermore, cellular experiments revealed that baicalin and baicalein exhibited enhanced anti-inflammatory responses compared to the active constituents of licorice, a known anti-inflammatory agent in TCM. Our findings confirmed that baicalin and baicalein mitigate diabetes via two distinct pathways: PTP1B inhibition and anti-inflammatory effects. Additionally, we have identified six flavonoid molecules with substantial potential for drug development, thereby augmenting the T2DM pharmacotherapeutic arsenal and promoting the integration of herb-derived treatments into modern pharmacology.

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.

Molecular dynamics simulations to explore the binding mode between the amyloid-ß protein precursor (APP) and adaptor protein Mint2.

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative disease management, with limited therapeutic options available for its prevention and treatment. At the heart of AD pathogenesis is the amyloid-ß (Aß) protein precursor (APP), with the interaction between APP and the adaptor protein Mint2 being crucial. Despite previous explorations into the APP-Mint2 interaction, the dynamic regulatory mechanisms by which Mint2 modulates APP binding remain poorly understood. This study undertakes molecular dynamics simulations across four distinct systems-free Mint2, Mint2 bound to APP, a mutant form of Mint2, and the mutant form bound to APP-over an extensive 400 ns timeframe. Our findings reveal that the mutant Mint2 experiences significant secondary structural transformations, notably the formation of an α-helix in residues S55-K65 upon APP binding, within the 400 ns simulation period. Additionally, we observed a reduction in the active pocket size of the mutant Mint2 compared to its wild-type counterpart, enhancing its APP binding affinity. These insights hold promise for guiding the development of novel inhibitors targeting the Mints family, potentially paving the way for new therapeutic strategies in AD prevention and treatment.

The value of audiovisual sexual stimulation with virtual reality in diagnosing erectile dysfunction.

Background: The traditional audiovisual sexual stimulation (AVSS) test may experience limitations including low erectile response rate and lack of unified diagnostic criteria. Aim: We aimed to explore the clinical value of AVSS with virtual reality (VR-AVSS) test in assessing erectile function and diagnosing erectile dysfunction (ED). Methods: Participants 18 to 60 years of age were screened for analysis in 3 clinical centers from June 2020 to March 2022. Demographic data, 5-item International Index of Erectile Function (IIEF-5), erectile hardness score (EHS), and self-reported symptom questions were collected. The ED patients and control patients were confirmed according to the IIEF-5 and EHS. All subjects watched a 60-minute erotic video by VR device during RigiScan recording. The parameters including tip average rigidity, tip effective erectile duration (duration of rigidity ≥60%, tip effective erectile duration), base average rigidity, and base effective erectile duration were evaluated. Outcomes: The main outcome of interest was the application of VR immersion technology to improve the traditional AVSS test. Results: A total of 301 ED cases and 100 eligible control patients were included for final analysis. Compared with control patients, ED cases had significantly lower IIEF-5 scores, EHS, positive response rate, and erectile rigidity and duration. The positive response rate of ED and control patients were 75.5% and 90.9%, respectively. The cutoff points of tip average rigidity, tip effective erectile duration, base average rigidity, and base effective erectile duration were 40.5% (sensitivity: 77.6%, specificity: 70.2%; P < .001), 4.75 minutes (sensitivity: 75.9%, specificity: 75.4%; P < .001), 48.5% (sensitivity: 77.6%, specificity: 75.1%; P < .001), and 7.75 minutes (sensitivity: 79.3%, specificity: 75.7%; P < .001). Clinical Implications: The technological superiority of VR will enable the VR-AVSS immersion test to be a more accurate detection than traditional AVSS modes. Strengths and Limitations: Our study applied VR immersion technology to establish the standard operation procedure for the AVSS test, which could effectively reduce the interference of adverse factors and minimize the detecting errors. However, the test data only included positive response subjects, so the true erectile status of men with a negative response to the AVSS test cannot be obtained. Conclusions: The VR-AVSS test can effectively improve the diagnostic accuracy of ED. The average rigidity and effective erectile duration were the optimal diagnostic parameters for excluding ED.

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.

A review of the clinical efficacy of FDA-approved antibody‒drug conjugates in human cancers.

While strategies such as chemotherapy and immunotherapy have become the first-line standard therapies for patients with advanced or metastatic cancer, acquired resistance is still inevitable in most cases. The introduction of antibody‒drug conjugates (ADCs) provides a novel alternative. ADCs are a new class of anticancer drugs comprising the coupling of antitumor mAbs with cytotoxic drugs. Compared with chemotherapeutic drugs, ADCs have the advantages of good tolerance, accurate target recognition, and small effects on noncancerous cells. ADCs occupy an increasingly important position in the therapeutic field. Currently, there are 13 Food and Drug Administration (FDA)‒approved ADCs and more than 100 ADC drugs at different stages of clinical trials. This review briefly describes the efficacy and safety of FDA-approved ADCs, and discusses the related problems and challenges to provide a reference for clinical work.

Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase.

Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems. The results reveal a stronger binding affinity of the LWM peptide to XO, potentially due to increased hydrogen bond formation. Notable changes were observed in the XO tunnel upon inhibitor binding, particularly with LWM, which showed a thinner, longer, and more twisted configuration compared to ALPM. The study highlights the importance of residue F914 in the allosteric pathway. Methodologically, we utilized the perturbed response scan (PRS) based on Python, enhancing tools for MD analysis. These findings deepen our understanding of food-derived anti-XO inhibitors and could inform the development of food-based therapeutics for reducing uric acid levels with minimal side effects.

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.

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.

PSMC4 promotes prostate carcinoma progression by regulating the CBX3-EGFR-PI3K-AKT-mTOR pathway.

Proteasome 26S subunit ATPase 4 (PSMC4) could regulate cancer progression. However, the function of PSMC4 in prostate carcinoma (PCa) progression requires further clarification. In the study, PSMC4 and chromobox 3 (CBX3) levels were verified by TCGA data and tissue microarrays. Cell counting kit-8, cell apoptosis, cell cycle, wound healing, transwell and xenograft tumour model assays were performed to verify biological functions of PSMC4 in PCa. RNA-seq, PCR, western blotting and co-IP assays were performed to verify the mechanism of PSMC4. Results showed that PSMC4 level was significantly increased in PCa tissues, and patients with PCa with a high PSMC4 level exhibited shorter overall survival. PSMC4 knockdown markedly inhibited cell proliferation, cell cycle and migration in vitro and in vivo, and significantly promoted cell apoptosis. Then further study revealed that CBX3 was a downstream target of PSMC4. PSMC4 knockdown markedly reduced CBX3 level, and inhibited PI3K-AKT-mTOR signalling. CBX3 overexpression markedly promoted epidermal growth factor receptor (EGFR) level. Finally, PSMC4 overexpression showed reverse effect in DU145 cells, and the effects of PSMC4 overexpression on cell proliferation, migration and clonal formation were rescued by the CBX3 knockdown, and regulated EGFR-PI3K-AKT-mTOR signalling. In conclusion, PSMC4 could regulate the PCa progression by mediating the CBX3-EGFR-PI3K-AKT-mTOR pathway. These findings provided a new target for PCa treatment.

Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement.

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (ß = 0.25, p < 0.001), behavioral engagement (ß = 0.25, p < 0.001), and emotional engagement (ß = 0.27, p < 0.001). Behavioral engagement was affected by TTF (ß = 0.31, p < 0.001) and ITF (ß = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (ß = 0.49, p < 0.001; ß = 0.19, p < 0.001; ß = 0.12, p = 0.001, respectively) and cognitive engagement (ß = 0.28, p < 0.001; ß = 0.34, p < 0.001; ß = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

Exploring the limits of metasurface polarization multiplexing capability based on deep learning.

Metasurfaces provide a new approach for planar optics and thus have realized multifunctional meta-devices with different multiplexing strategies, among which polarization multiplexing has received much attention due to its convenience. At present, a variety of design methods of polarization multiplexed metasurfaces have been developed based on different meta-atoms. However, as the number of polarization states increases, the response space of meta-atoms becomes more and more complex, and it is difficult for these methods to explore the limit of polarization multiplexing. Deep learning is one of the important routes to solve this problem because it can realize the effective exploration of huge data space. In this work, a design scheme for polarization multiplexed metasurfaces based on deep learning is proposed. The scheme uses a conditional variational autoencoder as an inverse network to generate structural designs and combines a forward network that can predict meta-atoms' responses to improve the accuracy of designs. The cross-shaped structure is used to establish a complicated response space containing different polarization state combinations of incident and outgoing light. The multiplexing effects of the combinations with different numbers of polarization states are tested by utilizing the proposed scheme to design nanoprinting and holographic images. The polarization multiplexing capability limit of four channels (a nanoprinting image and three holographic images) is determined. The proposed scheme lays the foundation for exploring the limits of metasurface polarization multiplexing capability.

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.

Exploration of the Product Specificity of chitosanase CsnMY002 and Mutants Using Molecular Dynamics Simulations.

Chitosanase CsnMY002 is a new type of enzyme isolated from Bacillus subtilis that is used to prepare chitosan oligosaccharide. Although mutants G21R and G21K could increase Chitosan yield and thus increase the commercial value of the final product, the mechanism by which this happens is not known. Herein, we used molecular dynamics simulations to explore the conformational changes in CsnMY002 wild type and mutants when they bind substrates. The binding of substrate changed the conformation of protein, stretching and deforming the active and catalytic region. Additionally, the mutants caused different binding modes and catalysis, resulting in different degrees of polymerization of the final Chitooligosaccharide degradation product. Finally, Arg37, Ile145 ~ Gly148 and Trp204 are important catalytic residues of CsnMY002. Our study provides a basis for the engineering of chitosanases.