Dynamic Cross-correlation Research Articles

The relationship between inter-diffusion and self-diffusion of different liquid metals studied by molecular dynamics simulations

Long-capillary simulation method was used to calculate the inter-diffusion coefficient using nonequilibrium molecular dynamics simulations. The inter-diffusion coefficient of the binary Al80Ni20, Al80Cu20, La80Ni20 and La80Al20 melts were calculated over a wide temperature range. We found that the relationship between inter- and self-diffusion coefficients of Al80Cu20 melts can be well expressed by Darken equation, which agreed with the previous results. For Al80Ni20, La80Ni20 and La80Al20 melts, dynamic cross correlation factor S was needed to correlate the Darken equation. S of Al80Ni20 and La80Ni20 ranged from 0.68 to 0.92, while that of La80Al20 was larger than 1. The effect of pressure on S was further investigated. We found that S of Al80Ni20 under 30 GPa was about 0.46 at 2000K and it was much lower than that of zero pressure, which means that the pressure had a great influence on S. Our investigations provided an improved understanding of cross correlation factor S.

Investigation of the structural dynamics of a knotted protein and its unknotted analog using molecular dynamics.

The role of knots in proteins remains elusive. Some studies suggest an impact on stability; the difficulty in comparing systems to assess this effect, however, has been a significant challenge. In this study, we produced and analyzed molecular dynamic trajectories considering three different temperatures of two variants of ornithine transcarbamylase (OTC), only one of which has a 31 knot, in order to evaluate the relative stability of the two molecules. RMSD showed equilibrated structures for the produced trajectories, and RMSF showed subtle differences in flexibility. In the knot moiety, the knotted protein did not show a great deal of fluctuation at any temperature. For the unknotted protein, the residue GLY243 showed a high fluctuation in the corresponding moiety. The fraction of native contacts (Q) showed a similar profile at all temperatures, with the greatest decrease by 436K. The investigation of conformational behavior with principal component analysis (PCA) and dynamic cross-correlation map (DCCM) showed that knotted protein is less likely to undergo changes in its conformation under the conditions employed compared to unknotted. PCA data showed that the unknotted protein had greater dispersion in its conformations, which suggests that it has a greater capacity for conformation transitions in response to thermal changes. DCCM graphs comparing the 310K and 436K temperatures showed that the knotted protein had less change in its correlation and anti-correlation movements, indicating stability compared to the unknotted.

Exploring the Resistance Mechanisms of Distal D835V Mutation in FLT3 to Inhibitors.

Objective FMS-like tyrosine kinase 3 (FLT3) is an attractive therapeutic target in acute myeloid leukemia. Unfortunately, secondary FLT3 mutations that developed resistance to inhibitors have become a severe problem. Specifically, ASP-835 (D835F/H/V/Y) mutant within the activation loop of FLT3 is the most commonly encountered drug-resistant and observed secondary FLT3 mutations. In this study, we carried out a set of computational approaches to explore how this mutation influenced the conformation and dynamics of DFG motif in a manner altered inhibitors' susceptibility. Methods Molecular dynamics (MD) simulation, dynamic cross-correlation (DCC) analysis, surface area (SASA), binding free energy (MM-GBSA), and structural analysis were used to compare the severe and minor D835V mutation-induced impact to sorafenib and crenolanib, respectively. Results The A-loop of the FLT3 protein may experience conformational change in the presence of the resistant mutation, which were mainly positioned at PHE-830. The protein-inhibitor interactions displayed that the motions of PHE-830 influenced that of sorafenib, but not to crenolanib. Conclusions These findings indicated that the structural impact brought by D835V mutation should be considered in designing novel drugs to overcome resistance to FLT3-D835V.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics.

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MD methods are unsuitable for sampling structures that depart significantly from the initial structure without any biased force. In this study, we developed a new adaptive CG elastic network model (ENM), in which the dynamic cross-correlation coefficient based on short-time AA-MD of at most ns order is considered. By applying Bayesian optimization to search for a suitable parameter among the vast parameter space of adaptive CG-ENM, we succeeded in reducing the searching cost to approximately 10% of those for random sampling and exhaustive sampling. To evaluate the performance of adaptive CG-ENM, we applied the new methodology to adenylate kinase (ADK) and glutamine binding protein (GBP) in the apo state. The results showed that the structural ensembles explored by adaptive CG-ENM could be considerably more diverse than those by conventional ENMs with enhanced sampling such as temperature replica exchange MD and long-time AA-MD of 1 μs. In particular, some of the structures sampled by adaptive ENM are relatively close to the holo-type structures of ADK and GBP. Furthermore, as a challenging task, to demonstrate the advantages of the CG model with lower calculation cost, we applied our new methodology to a larger biomolecule, integrin (αV) in the inactive state. Then, we sampled various structural ensembles, including extended structures that are apparently different from inactive ones.

An extensive computational study to identify potential inhibitors of Acyl-homoserine-lactone synthase from Acinetobacter baumannii (strain AYE)

A member of the ESKAPE family of pathogens, A. baumannii, is an opportunistic gram-negative multidrug-resistant bacterium. A. baumannii is a ubiquitous coccobacillus involved in various hospital-related infections such as wound infections, pneumonia, urinary tract infections, septicaemia, endocarditis and ventilator assisted pneumonia and accounts for approximately 1–2% of all nosocomial bloodstream infections; hence it becomes imperative to identify potential therapeutic agents against the dreadful pathogen. The quorum-sensing pathway becomes an attractive drug target due to its role in biofilm regulation and formation, which provides the bacteria insulation from the harsh environment. A crucial protein in biofilm formation and regulation is Acyl-homoserine-lactone synthase (AHLS), responsible for producing signal molecules that trigger the signalling pathway for biofilm formation and regulation. The current study modeled the three-dimensional structure of AHLS in A. baumannii (strain AYE) followed by high-throughput virtual screening of the enamine-AC small-molecule database to identify lead molecules against its acylated-ACP (Acyl Carrier Protein) substrate-binding site. Based on the estimated binding affinity, estimated inhibition constant, ADME analysis and interaction patterns of the screened molecules, three lead candidates (Z815888654, Z2416029019, Z3766992625) were identified along with a control molecule (J8-C8). These molecules were then subjected to molecular dynamics simulations where the physiological effect of ligand binding on the protein was virtually predicted and analysed. The MM/PBSA based binding free energy calculations showed favourable results for Z815888654 (−22.77 ± 2.94 kcal/mol), Z2416029019 (−33.68 ± 2.63 kcal/mol), Z3766992625 (−21.44 ± 3.40 kcal/mol). The study employed global and essential dynamics analyses, MM/PBSA based binding free energy, free energy landscape and dynamic cross-correlation matrix to suggest Z815888654, Z2416029019 and Z3766992625 as potential inhibitors against the acylated-ACP substrate-binding site in AHLS from A. baumannii.

Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier.

SummaryA new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/).Supplementary information Supplementary data are available at Bioinformatics online.

Site-selective dynamics of ligand-free and ligand-bound azidolysozyme.

Azido-modified alanine residues (AlaN3) are environment-sensitive, minimally invasive infrared probes for the site-specific investigation of protein structure and dynamics. Here, the capability of the label is investigated to query whether or not a ligand is bound to the active site of lysozyme and how the spectroscopy and dynamics change upon ligand binding. The results demonstrate specific differences for center frequencies of the asymmetric azide stretch vibration, the longtime decay, and the static offset of the frequency fluctuation correlation function (FFCF)-all of which are experimental observables-between the ligand-free and the ligand-bound N3-labeled protein. The center-frequency shifts range from 1 to 8 cm-1, which is detectable from state-of-the art experiments. Similarly, the nonvanishing static component Δ0 of the FFCF between ligand-free and ligand-bound protein can differ by up to a factor of 2.5. This makes the azide label a versatile and structurally sensitive probe to report on the dynamics of proteins in a variety of environments and for a range of different applications. Ligand-induced differences in the dynamics are also mapped onto changes in the local and through-space coupling between residues by virtue of dynamical cross correlation maps. This demonstrates that the position where the label is placed also influences the local and global protein motions.

Testing Financial Contagion in Emerging Countries: Evidence From BRICS Countries

This study uses a Multivariate Generalised Autoregressive Conditional Heteroskedasticity (MGARCH) model to examine the pure form of financial contagion in BRICS countries (Brazil, Russia, India, China, and South Africa) in the wake of two major international financial crises namely the U.S. sub-prime and Eurozone sovereign debt crises (EZDC). The pure form of contagion refers to the spread of shocks that are unrelated to macroeconomic fundamentals and are simply the product of irrational phenomena like panics, herd behaviour, loss of confidence, and risk aversion. To investigate contagion the present study analyses the pairwise dynamic cross-correlation between the US and Eurozone equity markets as ‘source’ (ground zero) markets and individual BRICS stock markets as ‘target’ markets.For the each of the two crises that are examined the sets of data used, were divided into two sub-periods (1) the crisis period and (2) the stable period. For the Sub-prime crisis, the findings of the present study indicate the presence of cross-conditional volatility between the US and BRICS stock markets. The results also showed that the cross-conditional volatility coefficient is high in magnitude during periods of financial upheaval compared to a tranquil period, hence the conclusion that there was financial contagion during in BRICs stock markets (except in Chinese market) following the U.S. sub-prime crisis. As for the EZDC, equity markets in Brazil, India and China seemed to react equally (in both the ‘crisis’ and ‘post-crisis’ periods) from shocks emanating from European equity market. Hence the conclusion that there was no contagion in Brazil, India and China following the Eurozone sovereign debt crisis.

The molecular docking and molecular dynamics study of flavonol synthase and flavonoid 3’-monooxygenase enzymes involved for the enrichment of kaempferol

Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, antidiabetic activities etc. It has been reported in many vegetables, fruits, herbs and medicinal plants. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols. Whereas flavonoid 3’-monooxygenase (F3’H, EC 1.14.14.82) catalyses the hydroxylation of dihydroflavonol, and flavonol. FLS is involved in the synthesis of the kaempferol whereas F3’H causes degradation of kaempferol. The present study aimed to analyse the binding affinity, stability and activating activity of enzyme FLS as well as inhibitory activity of enzyme F3’H involved in the enrichment of the kaempferol using the in-silico approaches. Computational study for physico-chemical properties, conserved domain identification, 3-D structure prediction and its validation, conservation analysis, molecular docking followed by molecular dynamics analysis of FLS and F3’H, protein-activator (FLS-LIG Complex) and protein-inhibitor (F3’H-LIG Complex) complexes have been performed. Other structural analyses like root mean square fluctuation (RMSF), root mean square deviation (RMSD), surface area solvent accessibility (SASA), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA), Poisson-Boltzmann analysis (MM_PBSA) and the dynamic cross correlation map (DCCM) analysis to explore the structural, functional and thermodynamic stability of the proteins and the complexes were also studied. The molecular docking result showed that FLS binds strongly with the activator ascorbate (CID _54670067) while F3’H binds with the inhibitor ketoconazole (CID_456201). The most powerful inhibitor (ketoconazole for F3’H) and activator (ascorbate for FLS) is determined by computing the thermodynamic binding free energy through MM_PBSA analysis. The current work provides wide-ranging structural and functional information about FLS and F3’H enzymes showing detailed molecular mechanism of kaempferol biosynthesis and its degradation and hence kaempferol enrichment. Finding of the present work opens up new possibilities for future research towards enrichment of kaempferol by using activator (ascorbate) for FLS and inhibitor (ketoconazole) for F3’H as well as for its large-scale production using in vitro approaches. Communicated by Ramaswamy H. Sarma

Deciphering the binding mechanism of inhibitors of the SARS-CoV-2 main protease through multiple replica accelerated molecular dynamics simulations and free energy landscapes.

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. The development of safe and highly effective antiviral drugs is of great significance for the treatment of COVID-19. The main protease (Mpro) of SARS-CoV-2 is a key enzyme for viral replication and transcription and has no homolog in humans. Therefore, the Mpro is an ideal target for the design of drugs against COVID-19. Insights into the inhibitor-Mpro binding mechanism and conformational changes of the Mpro are essential for the design of potent drugs that target the Mpro. In this study, we analyzed the conformational changes of the Mpro that are induced by the binding of three inhibitors, YTV, YSP and YU4, using multiple replica accelerated molecular dynamics (MR-aMD) simulations, dynamic cross-correlation map (DCCM) calculations, principal component analysis (PCA), and free energy landscape (FEL) analysis. The results from DCCM calculations and PCA show that the binding of inhibitors significantly affects the kinetic behavior of the Mpro and induces a conformational rearrangement of the Mpro. The binding ability and binding mechanism of YTV, YSP and YU4 to the Mpro were investigated using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The results indicate that substitution of the tert-butanol group by methylbenzene and trifluoromethyl groups enhances the binding ability of YSP and YU4 to the Mpro compared with YTV; moreover, massive hydrophobic interactions are detected between the inhibitors and the Mpro. Meanwhile, T25, L27, H41, M49, N142, G143, C145, M165, E166 and Q189 are identified as the key residues for inhibitor-Mpro interactions using residue-based free energy decomposition calculations, which can be employed as efficient targets in the design of drugs that inhibit the activity of the Mpro.

Ligand-Induced Conformational and Dynamical Changes in a GT-B Glycosyltransferase: Molecular Dynamics Simulations of Heptosyltransferase I Complexes.

Understanding the dynamical motions and ligand recognition motifs of heptosyltransferase I (HepI) can be critical to discerning the behavior of other glycosyltransferase (GT) enzymes. Prior studies in our lab have demonstrated that GTs in the GT-B structural class, which are characterized by their connection of two Rossman-like domains by a linker region, have conserved structural fold and dynamical motions, despite low sequence homology, therefore making discoveries found in HepI transferable to other GT-B enzymes. Through molecular dynamics simulations and ligand binding free energy analysis of HepI in the apo and bound complexes (for all kinetically relevant combinations of the native substrates/products), we have determined the energetically favored enzymatic pathway for ligand binding and release. Our principal component, dynamic cross correlation, and network analyses of the simulations have revealed correlated motions involving residues within the N-terminal domain communicating with C-terminal domain residues via both proximal amino acid residues and also functional groups of the bound substrates. Analyses of the structural changes, energetics of substrate/product binding, and changes in pKa have elucidated a variety of inter and intradomain interactions that are critical for enzyme catalysis. These data corroborate our experimental observations of protein conformational changes observed in both presteady state kinetic and circular dichroism analyses of HepI. These simulations provided invaluable structural insights into the regions involved in HepI conformational rearrangement upon ligand binding. Understanding the specific interactions governing conformational changes is likely to enhance our efforts to develop novel dynamics disrupting inhibitors against GT-B structural enzymes in the future.

Identification of Phytoconstituents as Potent Inhibitors of Casein Kinase-1 Alpha Using Virtual Screening and Molecular Dynamics Simulations.

Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell cycle progression, apoptosis, autophagy, etc. It has been known to involve in the progression of many diseases, including cancer, neurodegeneration, obesity, and behavioral disorders. The elevated expression of CK1α in diseased conditions facilitates its selective targeting for therapeutic management. Here, we have performed virtual screening of phytoconstituents from the IMPPAT database seeking potential inhibitors of CK1α. First, a cluster of compounds was retrieved based on physicochemical parameters following Lipinski’s rules and PAINS filter. Further, high-affinity hits against CK1α were obtained based on their binding affinity score. Furthermore, the ADMET, PAINS, and PASS evaluation was carried out to select more potent hits. Finally, following the interaction analysis, we elucidated three phytoconstituents, Semiglabrinol, Curcusone_A, and Liriodenine, posturing considerable affinity and specificity towards the CK1α binding pocket. The result was further evaluated by molecular dynamics (MD) simulations, dynamical cross-correlation matrix (DCCM), and principal components analysis (PCA), which revealed that binding of the selected compounds, especially Semiglabrinol, stabilizes CK1α and leads to fewer conformational fluctuations. The MM-PBSA analysis suggested an appreciable binding affinity of all three compounds toward CK1α.

Predicting New Anti-Norovirus Inhibitor With the Help of Machine Learning Algorithms and Molecular Dynamics Simulation-Based Model.

Hepatitis C virus (HCV) inhibitors are essential in the treatment of human norovirus (HuNoV). This study aimed to map out HCV NS5B RNA-dependent RNA polymerase inhibitors that could potentially be responsible for the inhibitory activity of HuNoV RdRp. It is necessary to develop robust machine learning and in silico methods to predict HuNoV RdRp compounds. In this study, Naïve Bayesian and random forest models were built to categorize norovirus RdRp inhibitors from the non-inhibitors using their molecular descriptors and PubChem fingerprints. The best model observed had accuracy, specificity, and sensitivity values of 98.40%, 97.62%, and 97.62%, respectively. Meanwhile, an external test set was used to validate model performance before applicability to the screened HCV compounds database. As a result, 775 compounds were predicted as NoV RdRp inhibitors. The pharmacokinetics calculations were used to filter out the inhibitors that lack drug-likeness properties. Molecular docking and molecular dynamics simulation investigated the inhibitors’ binding modes and residues critical for the HuNoV RdRp receptor. The most active compound, CHEMBL167790, closely binds to the binding pocket of the RdRp enzyme and depicted stable binding with RMSD 0.8–3.2 Å, and the RMSF profile peak was between 1.0–4.0 Å, and the conformational fluctuations were at 450–460 residues. Moreover, the dynamic residue cross-correlation plot also showed the pairwise correlation between the binding residues 300–510 of the HuNoV RdRp receptor and CHEMBL167790. The principal component analysis depicted the enhanced movement of protein atoms. Moreover, additional residues such as Glu510 and Asn505 interacted with CHEMBL167790 via water bridge and established H-bond interactions after the simulation. http://zinc15.docking.org/substances/ZINC000013589565.

Hydration modulates oxygen channel residues for oxygenation of cysteine dioxygenase: Perspectives from molecular dynamics simulations

Cysteine dioxygenase (CDO) regulates the concentration of l-cysteine substrate by its oxidation in the body to prevent different diseases, including neurodegenerative and autoimmune diseases. CDO catalyzes the oxidation of thiol group of l-cysteine to l-cysteine sulfinic acid using molecular oxygen. In this study, molecular dynamics simulations were applied to ligand-free CDO, cysteine-bound CDO, and oxygen-bound CDO-cysteine complex which were primarily subjected to the evaluation of their structural and dynamical properties. The simulation data provided significant information not only on the conformational changes of the enzyme after its ligation but also on the co-ligation by sequential binding of l-cysteine and molecular oxygen. It was found that the ligation and co-ligation perturbed the active site region as well as the overall protein dynamics which were analyzed in terms of root mean square deviation, root mean square fluctuation and dynamic cross correlation matrices as well as principal component analysis. Furthermore, oxygen transport pathways were successfully explored by taking various tunnel clusters into account and one of those clusters was given preference based on the throughput value. The bottleneck formed by different amino acid residues was examined to figure out their role in the oxygenation process of the enzyme. The residues forming the tunnel's bottleneck and their dynamics mediated by water molecules were further investigated using radial distribution functions which gave insights into the hydration behavior of these residues. The findings based on the hydration behavior in turn served to explore the water-mediated dynamics of these residues in the modulation of the pathway, including tunnel gating for the oxygen entry and diffusion to the active site, which is essential for the CDO's catalytic function.

Investigating potency of TMC-126 against wild-type and mutant variants of HIV-1 protease: a molecular dynamics and free energy study

ABSTRACT A detailed computational study was performed to investigate the conformational changes of flap region and the mechanism underlying the binding of the inhibitor TMC-126 to HIV-1 protease (PR1) and its mutant variants through molecular dynamics simulations in conjunction with the molecular mechanics Poisson–Boltzmann (MM-PBSA) free energy calculation. Further, we have studied the effectiveness of the inhibitor against HIV-2 protease (PR2). The MM-PBSA calculation suggests that TMC-126 loses its potency against mutant variants and PR2 compared to wild-type PR1 mainly due to the loss in intermolecular electrostatic interactions. The potency of the inhibitor decreases in the order: wild type PR1 > M46L > MDR20 > I50V > PR2 > V32I > A28S. Our study reveals that the flap of PR1 adopts a semi-open conformation due to the mutation I50V or MDR20. The dissimilar nature of the movement of the flap tip of both monomers is evident from the dynamic cross-correlation map. The protein structural network analysis displays that mutation causes structural rearrangements and changes the communication path between residues. Overall, we believe our study may help explore and accelerate the development of novel HIV-1/HIV-2 protease inhibitors with better potency.

Improvement of thermostability and catalytic efficiency of glucoamylase from Talaromyces leycettanus JCM12802 via site-directed mutagenesis to enhance industrial saccharification applications

BackgroundGlucoamylase is an important industrial enzyme in the saccharification of starch into glucose. However, its poor thermostability and low catalytic efficiency limit its industrial saccharification applications. Therefore, improving these properties of glucoamylase is of great significance for saccharification in the starch industry.ResultsIn this study, a novel glucoamylase-encoding gene TlGa15B from the thermophilic fungus Talaromyces leycettanus JCM12802 was cloned and expressed in Pichia pastoris. The optimal temperature and pH of recombinant TlGa15B were 65 ℃ and 4.5, respectively. TlGa15B exhibited excellent thermostability at 60 ℃. To further improve thermostability without losing catalytic efficiency, TlGa15B-GA1 and TlGa15B-GA2 were designed by introducing disulfide bonds and optimizing residual charge–charge interactions in a region distant from the catalytic center. Compared with TlGa15B, mutants showed improved optimal temperature, melting temperature, specific activity, and catalytic efficiency. The mechanism underlying these improvements was elucidated through molecular dynamics simulation and dynamics cross-correlation matrices analysis. Besides, the performance of TlGa15B-GA2 was the same as that of the commercial glucoamylase during saccharification.ConclusionsWe provide an effective strategy to simultaneously improve both thermostability and catalytic efficiency of glucoamylase. The excellent thermostability and high catalytic efficiency of TlGa15B-GA2 make it a good candidate for industrial saccharification applications.

Exploring the interaction mechanism between potential inhibitor and multi-target Mur enzymes of mycobacterium tuberculosis using molecular docking, molecular dynamics simulation, principal component analysis, free energy landscape, dynamic cross-correlation matrices, vector movements, and binding free energy calculation

Multi-targeting enzyme approaches are considered to be the most significant in suppressing pathogen growth and disease control for MDR and XDR-resistant Mycobacterium tuberculosis. The multiple Mur enzymes involved in peptidoglycan biosynthesis play a key role in a cell’s growth. Firstly, homology modeling was employed to construct the 3 D structure of the Mur enzymes. The computational approaches, including molecular docking and molecular dynamics simulations and MM-PBSA methods, were performed to explore the detailed interaction mechanism to evaluate the inhibitory activity against targeted proteins. The computational calculations revealed that the best-docked phytochemical compound (gallomyricitrin) inhibits the selected targets: Mur enzymes by forming stable hydrogen bonds. The analysis of RMSD, RMSF, Rg, PCA, DCCM, cross-correlation network, FEL, H-bond, and vector movement reveal that the docked complex of MurA, MurI, MurG, MurC, and MurE is more stable compared to MurB, MurF, MurD, and MurX docked complexes during MD simulations. Moreover, FEL exposed that gallomyricitrin stabilized to the minimum global energy of Mur Enzymes. The PCA, DCCM, and vector movements and binding free energy results provided further evidence for the stability of gallomyricitrin’s interactions inside the binding sites by forming hydrogen bonds. The cross-correlation analysis reveals that Mur enzymes exhibit a positive and negative correlated motion between residues in different protein domains. The computational results contribute in several ways to our understanding of inhibition activity and provide a basic insight into the binding activity of gallomyricitrin as a multi-target drug for tuberculosis. Communicated by Ramaswamy H. Sarma

SHIBOR Fluctuations and Stock Market Liquidity: An MF-DCCA Approach

ABSTRACT This paper examines the nonlinear and dynamic cross-correlations between SHIBOR and Chinese stock market liquidity by employing MF-DCCA method. The cross-correlations display weak persistence and multifractal characteristics, explaining the variations in the relationship between them. The multifractality strength of the cross-correlations decreases after a recent liberalization reform. Moreover, interest rates have a significantly strong influence on stock market liquidity during tight monetary policy and emergencies, indicating the asymmetric and time-varying impact of interest rates on stock market liquidity. In addition, the effectiveness of interest rate transmission decreases in the period of the COVID-19 pandemic.

Comparison of irreversible inhibition targeting HSP72 protein: the resurgence of covalent drug developments

ABSTRACT The covalent inhibition mechanism of action, which overcomes competition with high-affinity, high-abundance substrates of challenging protein targets, can deliver effective chemical probes and drugs. Heat shock protein 72 (HSP72) expressed in cancer cells may be responsible for tumorigenesis and tumour progression by providing resistance to chemotherapy. In this study, we explore the most optimal binding mechanism in the inhibition of the HSP72 nucleotide-binding domain by two different covalent generation, TCI2 and TCI8. However, the structural basis and conformational changes associated with this preferential covalent inhibition to HSP72-TCI2 over HSP72-TCI8 remain unclear. Our results revealed that HSP72-TCI2 covalent complex was more able to stabilise and induce better interaction with high correlated dynamic motion of HSP72-NBD at ATP binding site throughout the simulation in comparison to the HSP72-TCI8 covalent complex. This is supported by the dynamic cross-correlation and principal component analysis that was more dominant in the HSP72-TCI2 inhibited complex. The insights demonstrating the above binding mechanism of HSP72 establish TCI2 covalent inhibition as the preferred method of inhibiting the HSP72 protein. This investigation aids in the understanding of the structural mechanism of HSP72 inhibition and would assist in the design of more potent covalent inhibitors of HSP72.

Economic Policy Uncertainty and Local Carbon Emission Trading: A Multifractal Analysis from US and Guangdong

This paper investigates the long-term dynamic cross-correlation evolution between US economic policy uncertainty index (USEPU) and Guangdong carbon emission trading price (GDCP) from the multifractal detrended cross-correlation analysis (MF-DCCA) perspective. With the calculation of correlation statistics and fluctuation function, the beginning procedures of MF-DCCA, we find that the cross-correlation between USEPU and GDCP is significant and presents power law property. Also, with the Hurst exponent, we find that the long-horizon correlations between series are persistent. Moreover, we perform Rényi exponent and spectrum singularity check. The empirical findings reveal that the all the correlations are of multifractality and the correlation of GDCP holds the highest degree.