5,102 research outputs found

    Characterization and insight mechanism of an acid-adapted β-Glucosidase from Lactobacillus paracasei and its application in bioconversion of glycosides

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    Introduction: β-glucosidase is one class of pivotal glycosylhydrolase enzyme that can cleavage glucosidic bonds and transfer glycosyl group between the oxygen nucleophiles. Lactobacillus is the most abundant bacteria in the human gut. Identification and characterization of new β-glucosidases from Lactobacillus are meaningful for food or drug industry.Method: Herein, an acid-adapted β-glucosidase (LpBgla) was cloned and characterized from Lactobacillus paracasei. And the insight acid-adapted mechanism of LpBgla was investigated using molecular dynamics simulations.Results and Discussion: The recombinant LpBgla exhibited maximal activity at temperature of 30°C and pH 5.5, and the enzymatic activity was inhibited by Cu2+, Mn2+, Zn2+, Fe2+, Fe3+ and EDTA. The LpBgla showed a more stable structure, wider substrate-binding pocket and channel aisle, more hydrogen bonds and stronger molecular interaction with the substrate at pH 5.5 than pH 7.5. Five residues including Asp45, Leu60, Arg120, Lys153 and Arg164 might play a critical role in the acid-adapted mechanism of LpBgla. Moreover, LpBgla showed a broad substrate specificity and potential application in the bioconversion of glycosides, especially towards the arbutin. Our study greatly benefits for the development novel β-glucosidases from Lactobacillus, and for the biosynthesis of aglycones

    Fluctuation relations to calculate protein redox potentials from molecular dynamics simulations

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    This is the final version. Available on open access from the American Chemical Society via the DOI in this recordThe tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here, we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are postprocessed into estimates for the redox potentials in an efficient manner. This new method, denoted MD + CB, is tested on a de novo four-helix bundle heme protein (the m4D2 “maquette”) and five designed mutants, including some mutants characterized experimentally in this work. The MD + CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD + CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD + CB approach is straightforwardly transferable to standard equilibrium MD simulations and holds promise for redox protein engineering and design applications.European Union Horizon 2020European Research Council (ERC)Engineering and Physical Sciences Research Council (EPSRC)Biotechnology and Biological Sciences Research Council (BBSRC)BrisSynBioUKRIOracleRoyal SocietySurrey Future Fellowship Programm

    Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus

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    Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of −9.576 kcal/mol, −8.1 kcal/mol, −7.744 kcal/mol, −7.647 kcal/mol, and − 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD

    A clinical decision support system for detecting and mitigating potentially inappropriate medications

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    Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice

    Effective player guidance in logic puzzles

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    Pen & paper puzzle games are an extremely popular pastime, often enjoyed by demographics normally not considered to be ‘gamers’. They are increasingly used as ‘serious games’ and there has been extensive research into computationally generating and efficiently solving them. However, there have been few academic studies that have focused on the players themselves. Presenting an appropriate level of challenge to a player is essential for both player enjoyment and engagement. Providing appropriate assistance is an essential mechanic for making a game accessible to a variety of players. In this thesis, we investigate how players solve Progressive Pen & Paper Puzzle Games (PPPPs) and how to provide meaningful assistance that allows players to recover from being stuck, while not reducing the challenge to trivial levels. This thesis begins with a qualitative in-person study of Sudoku solving. This study demonstrates that, in contrast to all existing assumptions used to model players, players were unsystematic, idiosyncratic and error-prone. We then designed an entirely new approach to providing assistance in PPPPs, which guides players towards easier deductions rather than, as current systems do, completing the next cell for them. We implemented a novel hint system using our design, with the assessment of the challenge being done using Minimal Unsatisfiable Sets (MUSs). We conducted four studies, using two different PPPPs, that evaluated the efficacy of the novel hint system compared to the current hint approach. The studies demonstrated that our novel hint system was as helpful as the existing system while also improving the player experience and feeling less like cheating. Players also chose to use our novel hint system significantly more often. We have provided a new approach to providing assistance to PPPP players and demonstrated that players prefer it over existing approaches

    MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN

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    Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment. To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution

    Modellierung der lichtabhängigen Photosynthesekinetik und Akklimatisation von Mikroalgen – Von der molekularen Struktur zur prozessnahen Formulierung

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    Im Rahmen der vorliegenden Arbeit wurde das lichtabhängige Akklimatisations- und Wachstumsverhalten der Grünalge Chlorella vulgaris, die hierbei als Modellorganismus fungierte, auf makroskopischer Ebene experimentell untersucht. Die aus diesen Experimenten gewonnenen Erkenntnisse und Daten wurden anschließend, zusammen mit den stationären Photosynthesekinetiken, als experimentelle Grundlage für die Modellierung der lichtabhängigen Photosynthesekinetik genutzt. Als Modellierungsgrundlage diente hierbei das Konzept der photosynthetic unit (PSU), das im bioverfahrenstechnischen Kontext häufig zur Modellierung der Photosynthesekinetik eingesetzt wird. Im Gegensatz zu der typischen Annahme eines absolut limitierenden Reaktionsschrittes innerhalb der PSU, der die gesamte Photosynthesekinetik definiert, wurde angenommen, dass unterschiedliche limitierende Reaktionen die jeweiligen Bereiche der lichtabhängigen Photosynthesekinetik definieren. Im Rahmen des hier entwickelten Modells wurde festgelegt, dass der lichtlimitierte Bereich der Photosynthesekinetik hauptsächlich durch die Lichtabsorption und die Prozessierung der Lichtenergie am Photosystem II determiniert wird, während ein zweiter limitierender Reaktionsschritt den lichtgesättigten Bereich der Photosynthesekinetik determiniert. Weiterhin wurde angenommen, dass die beiden limitierenden Reaktionsschritte über den Plastochinon-Pool miteinander gekoppelt sind. Unter den im Rahmen der vorliegenden Arbeit untersuchten Prozessbedingungen wurde die Kinetik von Ribulose-1,5-bisphosphat-carboxylase/-oxygenase als die zweite limitierende Reaktion definiert. Durch die Einführung dieser Erweiterungen des gängigen PSU-Konzeptes konnte ein Modell entwickelt werden, dass dem häufig genutzten PSU-Modell nach Han, das hierbei als Referenz diente, deutlich überlegen ist. Weiterhin konnte das in dieser Arbeit entwickelte Modell die operativen Photosyntheseraten im photoakklimatisierten Zustand, die nicht Teil des zur Parameterschätzung genutzten Datensatzes waren, mit einer zufriedenstellenden Genauigkeit vorhersagen (R²: 0,9688, nRMSE: 11,74 % und nMBE: -9,76 %)

    Understanding Gas and Energy Storage in Geological Formations with Molecular Simulations

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    Methane (CH4), the cleanest burning fossil fuel, has the potential to solve the energy crisis owing to the growing population and geopolitical tensions. Whilst highly calorific, realising its potential requires efficient storage solutions, which are safe and less energy-intensive during production and transportation. On the other hand, carbon dioxide (CO2), the by-product of human activities, exacerbates global heating driving climate change. CH4 is abundant in natural systems, in the form of gas hydrate and trapped gas within geological formations. The primary aim of this project was to learn how Nature could store such a large quantity of CH4 and how we can potentially extract and replace the in-place CH4 with atmospheric CO2, thereby reducing greenhouse gas emissions. We studied this question by applying molecular dynamics (MD) and Monte Carlo (MC) simulation techniques. Such techniques allow us to understand the behaviour of confined fluids, i.e., within the micropores of silica and kerogen matrices. Our simulations showed that CH4 hydrate in confinement could form under milder conditions than required, deviating from the typical methane-water phase diagram, complementing experimental observations. This research can contribute to artificial gas hydrate production via porous materials for gas storage. Besides that, the creation of 3D kerogen models via simulated annealing has enabled us to understand how maturity level affects the structural heterogeneity of the matrices and, ultimately CH4 diffusion. Immature and overmature kerogen types were identified to having fast CH4 diffusion. Subsequently, our proof-of-concept study demonstrated the feasibility of recovering CH4 via supercritical CO2 injection into kerogens. Insights from our study also explained why full recovery of CH4 is impossible. A pseudo-second-order rate law can predict the kinetics of such a process and the replacement quantity. A higher CO2 input required than the CH4 recovered highlights the possibility of achieving a net-zero future via geological CO2 sequestration
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