Glycyrrhizin as a promising kryptonite against SARS-CoV-2: Clinical, experimental, and theoretical evidences.

COVID-19 is the most devastating disease in recent times affecting most people globally. The higher rate of transmissibility and mutations of SARS-CoV-2 along with the lack of potential therapeutics has made it a global crisis. Potential molecules from natural sources could be a fruitful remedy to combat COVID-19. This systematic review highlights the detailed therapeutic implication of naturally occurring glycyrrhizin and its related derivatives against COVID-19. Glycyrrhizin has already been established for blocking different biomolecular targets related to the SARS-CoV-2 replication cycle. In this article, several experimental and theoretical evidences of glycyrrhizin and related derivatives have been discussed in detail to evaluate their potential as a promising therapeutic strategy against COVID-19. Moreover, the implication of glycyrrhizin in traditional Chinese medicines for alleviating the symptoms of COVID-19 has been reviewed. The potential role of glycyrrhizin and related compounds in affecting various stages of the SARS-CoV-2 life cycle has also been discussed in detail. Derivatization of glycyrrhizin for designing potential lead compounds along with combination therapy with other anti-SARS-CoV-2 agents followed by extensive evaluation may assist in the formulation of novel anti-coronaviral therapy for better treatment to combat COVID-19.

Multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis of the synergistic effects between natural compounds baicalein and cubebin for the inhibition of the main protease of SARS-CoV-2.

Combination drugs have been used for several diseases for many years since they produce better therapeutic effects. However, it is still a challenge to discover candidates to form a combination drug. This study aimed to investigate whether using a comprehensive in silico approach to identify novel combination drugs from a Chinese herbal formula is an appropriate and creative strategy. We, therefore, used Toujie Quwen Granules for the main protease (Mpro) of SARS-CoV-2 as an example. We first used molecular docking to identify molecular components of the formula which may inhibit Mpro. Baicalein (HQA004) is the most favorable inhibitory ligand. We also identified a ligand from the other component, cubebin (CHA008), which may act to support the proposed HQA004 inhibitor. Molecular dynamics simulations were then performed to further elucidate the possible mechanism of inhibition by HQA004 and synergistic bioactivity conferred by CHA008. HQA004 bound strongly at the active site and that CHA008 enhanced the contacts between HQA004 and Mpro. However, CHA008 also dynamically interacted at multiple sites, and continued to enhance the stability of HQA004 despite diffusion to a distant site. We proposed that HQA004 acted as a possible inhibitor, and CHA008 served to enhance its effects via allosteric effects at two sites. Additionally, our novel wavelet analysis showed that as a result of CHA008 binding, the dynamics and structure of Mpro were observed to have more subtle changes, demonstrating that the inter-residue contacts within Mpro were disrupted by the synergistic ligand. This work highlighted the molecular mechanism of synergistic effects between different herbs as a result of allosteric crosstalk between two ligands at a protein target, as well as revealed that using the multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis to discover novel combination drugs from a Chinese herbal remedy is an innovative pathway.

Localized Phonon Densities of States at Grain Boundaries in Silicon.

Since it is now possible to record vibrational spectra at nanometer scales in the electron microscope, it is of interest to explore whether extended defects in crystals such as dislocations or grain boundaries will result in measurable changes of the phonon densities of states (dos) that are reflected in the spectra. Phonon densities of states were calculated for a set of high angle grain boundaries in silicon. The boundaries are modeled by supercells with up to 160 atoms, and the vibrational densities of states were calculated by taking the Fourier transform of the velocity­velocity autocorrelation function from molecular dynamics simulations with larger supercells doubled in all three directions. In selected cases, the results were checked on the original supercells by comparison with the densities of states obtained by diagonalizing the dynamical matrix calculated using density functional theory. Near the core of the grain boundary, the height of the optic phonon peak in the dos at 60 meV was suppressed relative to features due to acoustic phonons that are largely unchanged relative to their bulk values. This can be attributed to the variation in the strength of bonds in grain boundary core regions where there is a range of bond lengths.

Identification of alkaloids from Justicia adhatoda as potent SARS CoV-2 main protease inhibitors: An in silico perspective.

The COVID-19 pandemic, caused by SARS CoV-2, is responsible for millions of death worldwide. No approved/proper therapeutics is currently available which can effectively combat this outbreak. Several attempts have been undertaken in the search of effective drugs to control the spread of SARS CoV-2 infection. The main protease (Mpro), key component for the cleavage of the viral polyprotein, is considered to be one of the important drug targets for treating COVID-19. Various phytochemicals, including polyphenols and alkaloids, have been proposed as potent inhibitors of Mpro. The alkaloids from leaf extracts of Justicia adhatoda have also been reported to possess anti-viral activity. But whether these alkaloids exhibit any inhibitory effect on SARS CoV-2 Mpro is far from clear. To explore this in detail, we have adopted computational approaches. Justicia adhatoda alkaloids possessing proper drug-likeness properties and two anti-HIV drugs (lopinavir and darunavir; having binding affinity -7.3 to -7.4 kcal/mol) were docked against SARS CoV-2 Mpro to study their binding properties. Only one alkaloid (anisotine) had interaction with both the catalytic residues (His41 and Cys145) of Mpro and exhibited good binding affinity (-7.9 kcal/mol). Molecular dynamic simulations (100 ns) revealed that Mpro-anisotine complex is more stable, conformationally less fluctuated; slightly less compact and marginally expanded than Mpro-darunavir/lopinavir complex. Even the number of intermolecular H-bonds and MM-GBSA analysis suggested that anisotine is a more potent Mpro inhibitor than the two previously recommended antiviral drugs (lopinavir and darunavir) and may evolve as a promising anti-COVID-19 drug if proven in animal experiments and on patients.

In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis.

Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic in silico virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (2-22) with better binding energy than remdesivir (1), marketed SARS CoV-2 inhibitor. Further, the analysis using rules for drug-likeness and their ADMET profile revealed the candidature of these hits due to superior oral bioavailability and druggability. Further, the MD simulation studies of top two hits (2 and 3) performed using GROMACS 2020.1 for 10 ns revealed their stability into the docked complexes. These results provide an important breakthrough in the design of macrocyclic hits as SARS CoV-2 RNA replicase inhibitor.

Mechanism of the enhancing effect of glycyrrhizin on nifedipine penetration through a lipid membrane.

The saponin glycyrrhizin from liquorice root shows the ability to enhance the therapeutic activity of other drugs when used as a drug delivery system. Due to its amphiphilic properties, glycyrrhizin can form self-associates (dimers, micelles) and supramolecular complexes with a wide range of hydrophobic drugs, which leads to an increase in their solubility, stability and bioavailability. That is why the mechanism of the biological activity of glycyrrhizin is of considerable interest and has been the subject of intensive physical and chemical research in the last decade. Two mechanisms have been proposed to explain the effect of glycyrrhizin on drug bioavailability, namely, the increase in drug solubility in water and enhancement of the membrane permeability. Interest in the membrane-modifying ability of glycyrrhizic acid (GA) is also growing at present due to its recently discovered antiviral activity against SARS-CoV-2 Bailly and Vergoten (2020) [1]. In the present study, the passive permeability of the DOPC lipid membrane for the calcium channel blocker nifedipine was elucidated by parallel artificial membrane permeability assay (PAMPA) and full atomistic molecular dynamics (MD) simulation with free energy calculations. PAMPA experiments show a remarkable increase in the amount of nifedipine (NF) permeated with glycyrrhizin compared to free NF. In previous studies, we have shown using MD techniques that glycyrrhizin molecules can integrate into the lipid bilayer. In this study, MD simulation demonstrates a significant decrease in the energy barrier of NF penetration through the lipid bilayer in the presence of glycyrrhizin both in the pure DOPC membrane and in the membrane with cholesterol. This effect can be explained by the formation of hydrogen bonds between NF and GA in the middle of the bilayer.

Interaction of Nevirapine with the Peptide Binding Groove of HLA-DRB1*01:01 and Its Effect on the Conformation of HLA-Peptide Complex.

Human leukocyte antigen (HLA)-DRB1*01:01 has been shown to be involved in nevirapine-induced hepatic hypersensitivity reactions. In the present study, in silico docking simulations and molecular dynamics simulations were performed to predict the interaction mode of nevirapine with the peptide binding groove of HLA-DRB1*01:01 and its possible effect on the position and orientation of the ligand peptide derived from hemagglutinin (HA). In silico analyses suggested that nevirapine interacts with HLA-DRB1*01:01 around the P4 pocket within the peptide binding groove and the HA peptide stably binds on top of nevirapine at the groove. The analyses also showed that binding of nevirapine at the groove will significantly change the inter-helical distances of the groove. An in vitro competitive assay showed that nevirapine (1000 µM) increases the binding of the HA peptide to HLA-DRB1*01:01 in an allele-specific manner. These results indicate that nevirapine might interact directly with the P4 pocket and modifies its structure, which could change the orientation of loaded peptides and the conformation of HLA-DRB1*01:01; these changes could be distinctively recognized by T-cell receptors. Through this molecular mechanism, nevirapine might stimulate the immune system, resulting in hepatic hypersensitivity reactions.

In Silico and In Vitro Analysis of Interaction between Ximelagatran and Human Leukocyte Antigen (HLA)-DRB1*07:01.

Idiosyncratic ximelagatran-induced hepatotoxicity has been reported to be associated with human leukocyte antigen (HLA)-DRB1*07:01 and ximelagatran has been reported to inhibit the binding of the ligand peptide to HLA-DRB1*07:01 in vitro. In order to predict the possible interaction modes of ximelagatran with HLA-DR molecules, in silico docking simulations were performed. Molecular dynamics (MD) simulations were also performed to predict the effect of ximelagatran on the binding mode of the ligand peptide to HLA-DRB1*07:01. A series of in silico simulations supported the inhibitory effect of ximelagatran on the binding of the ligand peptide to HLA-DRB1*07:01 in vitro. Furthermore, direct interactions of ximelagatran with HLA-DR molecules were evaluated in vitro, which supported the simulated interaction mode of ximelagatran with HLA-DRB1*07:01. These results indicated that ximelagatran directly interacts with the peptide binding groove of HLA-DRB1*07:01 and competes with the ligand peptide for the binding site, which could alter the immune response and lead to the idiosyncratic ximelagatran-induced hepatotoxicity.

The C-terminal RNA binding motif of HuR is a multi-functional domain leading to HuR oligomerization and binding to U-rich RNA targets.

Human antigen R (HuR) is a 32 kDa protein with 3 RNA Recognition Motifs (RRMs), which bind to Adenylate and uridylate Rich Elements (AREs) of mRNAs. Whereas the N-terminal and central domains (RRM1 and RRM2) are essential for AREs recognition, little is known on the C-terminal RRM3 beyond its implication in HuR oligomerization and apoptotic signaling. We have developed a detergent-based strategy to produce soluble RRM3 for structural studies. We have found that it adopts the typical RRM fold, does not interact with the RRM1 and RRM2 modules, and forms dimers in solution. Our NMR measurements, combined with Molecular Dynamics simulations and Analytical Ultracentrifugation experiments, show that the protein dimerizes through a helical region that contains the conserved W261 residue. We found that HuR RRM3 binds to 5'-mer U-rich RNA stretches through the solvent exposed side of its ß-sheet, located opposite to the dimerization site. Upon mimicking phosphorylation by the S318D replacement, RRM3 mutant shows less ability to recognize RNA due to an electrostatic repulsion effect with the phosphate groups. Our study brings new insights of HuR RRM3 as a domain involved in protein oligomerization and RNA interaction, both functions regulated by 2 surfaces on opposite sides of the RRM domain.

Comprehensive Investigation of Natural Ligands as Inhibitors of ß Secretase to Identify Alzheimer's Disease Therapeutics.

INTRODUCTION: Alzheimer's disease (AD) is an alarmingly prevalent worldwide neurological disorder that affects millions of people and has severe effects on cognitive functions. The amyloid hypothesis, which links AD to Aß (amyloid beta) plaque aggregation, is a well-acknowledged theory. The ß-secretase (BACE1) is the main cause of Aß production, which makes it a possible target for therapy. FDA-approved therapies for AD do exist, but none of them explicitly target BACE1, and their effectiveness is constrained and accompanied by adverse effects. MATERIALS AND METHODS: We determined the essential chemical components of medicinal herbs by conducting a thorough literature research for BACE1. Computational methods like molecular docking, ADMET (Absorption, distribution, metabolism, excretion, toxicity) screening, molecular dynamic simulations, and MMPBSA analysis were performed in order to identify the most promising ligands for ß-secretase. RESULTS: The results suggested that withasomniferol, tinosporide, and curcumin had better binding affinity with BACE1, suggesting their potential as therapeutic candidates against Alzheimer's disease. CONCLUSION: Herbal therapeutics have immense applications in the treatment of chronic diseases like Alzheimer's disease, and there is an urgent need to assess their efficacy as therapeutics.

Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations.

Nowadays, molecular dynamics (MD) simulations of proteins with hundreds of thousands of snapshots are commonly produced using modern GPUs. However, due to the abundance of data, analyzing transport tunnels present in the internal voids of these molecules, in all generated snapshots, has become challenging. Here, we propose to combine the usage of CAVER3, the most popular tool for tunnel calculation, and the TransportTools Python3 library into a divide-and-conquer approach to speed up tunnel calculation and reduce the hardware resources required to analyze long MD simulations in detail. By slicing an MD trajectory into smaller pieces and performing a tunnel analysis on these pieces by CAVER3, the runtime and resources are considerably reduced. Next, the TransportTools library merges the smaller pieces and gives an overall view of the tunnel network for the complete trajectory without quality loss.

Binding profile of quinonoid-dihydrobiopterin to quinonoid-dihydropteridine reductase examined by in silico and in vitro analyses.

Quinonoid dihydropteridine reductase (QDPR) catalyses the reduction of quinonoid-form dihydrobiopterin (qBH2) to tetrahydrobiopterin (BH4). BH4 metabolism is a drug target for neglected tropical disorders because trypanosomatid protozoans, including Leishmania and Trypanosoma, require exogenous sources of biopterin for growth. Although QDPR is a key enzyme for maintaining intracellular BH4 levels, the precise catalytic properties and reaction mechanisms of QDPR are poorly understood due to the instability of quinonoid-form substrates. In this study, we analysed the binding profile of qBH2 to human QDPR in combination with in silico and in vitro methods. First, we performed docking simulation of qBH2 to QDPR to obtain possible binding modes of qBH2 at the active site of QDPR. Then, among them, we determined the most plausible binding mode using molecular dynamics simulations revealing its atomic-level interactions and confirmed it with the in vitro assay of mutant enzymes. Moreover, it was found that not only qBH2 but also quinonoid-form dihydrofolate (qDHF) could be potential physiological substrates for QDPR, suggesting that QDPR may be a bifunctional enzyme. These findings in this study provide important insights into biopterin and folate metabolism and would be useful for developing drugs for neglected tropical diseases.

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.

Bioinformatics evaluation of anticancer properties of GP63 protein-derived peptides on MMP2 protein of melanoma cancer.

Background: GP63, also known as Leishmanolysin, is a multifunctional virulence factor abundant on the surface of Leishmania spp. small peptides with anticancer capabilities that are selective and toxic to cancer cells are known as anticancer peptides. We aimed to demonstrate the activity of GP63 and its anticancer properties on melanoma using a range of in silico tools and screening methods to identify predicted and designed anticancer peptides. Methods: Various in silico modeling methodologies are used to establish the three-dimensional (3D) structure of GP63. Refinement and re-evaluation of the modeled structures and the built models' quality evaluated using the different docking used to find the interacting amino acids between MMP2 and GP63 and its anticancer peptides. AntiCP2.0 is used for screening anticancer peptides. 2D interaction plots of protein-ligand complexes evaluated by Protein-Ligand Interaction Profiler server. It is for the first time that used anticancer peptides of GP63 and the predicted and designed peptides. Results: We used 3 peptides of GP63 based on the AntiCP 2.0 server with scores of 0.63, 0.53, and 0.49, and common peptides of GP63/MMP2 (continues peptide: mean the completely selected peptide after docking with non-anticancer effect, predicted with 0.58 score and designed peptides with 0.47 and 0.45 scores by AntiCP 2.0 server). Conclusions: The antileishmanial and anticancer peptide research topics exemplify the multidisciplinary nature of peptide research. The advancement of therapeutics targeting cancer and/or Leishmania requires an interconnected research strategy shown in this work.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

MH2c: Characterization of major histocompatibility α-helices - an information criterion approach.

Major histocompatibility proteins share a common overall structure or peptide binding groove. Two binding groove domains, on the same chain for major histocompatibility class I or on two different chains for major histocompatibility class II, contribute to that structure that consists of two α-helices ("wall") and a sheet of eight anti-parallel beta strands ("floor"). Apart from the peptide presented in the groove, the major histocompatibility α-helices play a central role for the interaction with the T cell receptor. This study presents a generalized mathematical approach for the characterization of these helices. We employed polynomials of degree 1 to 7 and splines with 1 to 2 nodes based on polynomials of degree 1 to 7 on the α-helices projected on their principal components. We evaluated all models with a corrected Akaike Information Criterion to determine which model represents the α-helices in the best way without overfitting the data. This method is applicable for both the stationary and the dynamic characterization of α-helices. By deriving differential geometric parameters from these models one obtains a reliable method to characterize and compare α-helices for a broad range of applications. PROGRAM SUMMARY: Program title: MH2c (MH helix curves) Catalogue identifier: AELX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 327 565 No. of bytes in distributed program, including test data, etc.: 17 433 656 Distribution format: tar.gz Programming language: Matlab Computer: Personal computer architectures Operating system: Windows, Linux, Mac (all systems on which Matlab can be installed) RAM: Depends on the trajectory size, min. 1 GB (Matlab) Classification: 2.1, 4.9, 4.14 External routines: Curve Fitting Toolbox and Statistic Toolbox of Matlab Nature of problem: Major histocompatibility (MH) proteins share a similar overall structure. However, identical MH alleles which present different peptides differ by subtle conformational alterations. One hypothesis is that such conformational differences could be another level of T cell regulation. By this software package we present a reliable and systematic way to compare different MH structures to each other. Solution method: We tested several fitting approaches on all available experimental crystal structures of MH to obtain an overall picture of how to describe MH helices. For this purpose we transformed all complexes into the same space and applied splines and polynomials of several degrees to them. To draw a general conclusion which method fits them best we employed the "corrected Akaike Information Criterion". The software is applicable for all kinds of helices of biomolecules. Running time: Depends on the data, for a single stationary structure the runtime should not exceed a few seconds.

Long-Timescale Simulations Revealed Critical Non-Conserved Residues of Phosphodiesterases Affecting Selectivity of BAY60-7550.

A major obstacle of the selective inhibitor design for specific human phosphodiesterase (PDE) is that highly conserved catalytic pockets are difficult to be distinguished by inhibitor molecules. To overcome this, a feasible path is to understand the molecular determinants underlying the selectivity of current inhibitors. BAY60-7550 (BAY for short; IC50 = 4.7 nM) is a highly selective inhibitor targeting PDE2A which is a dual-specificity PDE and an attractive target for therapeutic intervention of the central nervous system (CNS) disorders. Recent studies suggest that molecular determinants may be in binding processes of BAY. However, a detailed understanding of these processes are still lacking. To explore these processes, High-Throughput Molecular Dynamics (HTMD) simulations were performed to reproduce the spontaneous association of BAY with catalytic pockets of 4 PDE isoforms; Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD) simulations were performed to reproduce the unbinding-rebinding processes of FKG and MC2, two pyrazolopyrimidinone PDE2A selective inhibitors, in the PDE2A system. The produced molecular trajectories were analyzed by the Markov state model (MSM) and the molecular mechanics/generalized Born surface area (MM/GBSA). The results showed that the non-covalent interactions between the non-conserved residues and BAY, especially the hydrogen bonds, determined the unique binding pathways of BAY on the surface of PDE2A. These pathways were different from those of BAY on the surface of the other three PDE isoforms and the binding pathways of the other two PDE2A inhibitors in PDE2A systems. These differences were ultimately reflected in the high selectivity of this inhibitor for PDE2A. As a result, this study demonstrates the critical role of the binding processes in the selectivity of BAY, and also identifies the key non-conserved residues affecting the binding processes of BAY. Thus, this study provides a new perspective and data support for the further development of BAY-derived inhibitors targeting PDE2A.

Bridging the N-terminal and middle domains in FliG of the flagellar rotor.

Flagella are necessary for bacterial movement and contribute to various aspects of virulence. They are complex cylindrical structures built of multiple molecular rings with self-assembly properties. The flagellar rotor is composed of the MS-ring and the C-ring. The FliG protein of the C-ring is central to flagellar assembly and function due to its roles in linking the C-ring with the MS-ring and in torque transmission from stator to rotor. No high-resolution structure of an assembled C-ring has been resolved to date, and the conformation adopted by FliG within the ring is unclear due to variations in available crystallographic data. Here, we use molecular dynamics (MD) simulations to study the conformation and dynamics of FliG in different states of assembly, including both in physiologically relevant and crystallographic lattice environments. We conclude that the linker between the FliG N-terminal and middle domain likely adopts an extended helical conformation in vivo, in contrast with the contracted conformation observed in some previous X-ray studies. We further support our findings with integrative model building of full-length FliG and a FliG ring model that is compatible with cryo-electron tomography (cryo-ET) and electron microscopy (EM) densities of the C-ring. Collectively, our study contributes to a better mechanistic understanding of the flagellar rotor assembly and its function.

Studies into interactions and interfacial characteristics between cellulose nanocrystals and bovine serum albumin.

This study investigates the interactions between cellulose nanocrystals (CNCs) and bovine serum albumin (BSA) under different pH conditions. A multiscale technique was employed to characterize the CNCs and BSA at pH 7 and pH 3. ζ-Potential measurement and UV-vis spectroscopy demonstrated strong interactions between CNCs and BSA at pH 3, whereat they have opposite charges. Interfacial tensiometry showed a deficiency in the surface activity of the CNCs and indicated that BSA dominated the interface behavior in their complex. Quartz crystal microbalance with dissipation revealed that the sequential adsorption of BSA and CNCs produced viscoelastic bilayers at pH 3, and the mass adsorbed was âˆ¼ 28 times that adsorbed at pH 7. Molecular dynamics simulations indicated that the key interactions between the two materials were produced between the hydrophobic CNC surface and the BSA domain IIA region. These results provide interesting insights into the design of complex food emulsions and fluid interfaces.