Transport of nanoscale zero-valent iron in the presence of rhamnolipid

Nanoscale zero-valent iron (nZVI) particles have gained widespread use for in-situ treatment of various chlorinated hydrocarbons. Their non-toxic nature, affordability, and minimal maintenance requirements have made them a favored material for nanoremediation. The treatment typically involves the injection of nZVI particles into contaminated sites using direct-push well injection systems. However, their small size leads to high surface energy, causing aggregation that alters their physiochemical properties, reactivity, and transport behavior. To counteract aggregation, nZVI suspension can be stabilized with different surfactants, reducing the surface energy during subsurface soil transport. This study investigates the impact of rhamnolipid, a biosurfactant produced by Pseudomonas aeruginosa during the late growth phase, on the aggregation and mobility of nZVI particles. The retardation factor of nZVI in the model media of zeolite, ZK406H, decreased from 1.66 in the absence of rhamnolipid to 1.03, 0.98, 0.93, and 0.87, corresponding to the presence of rhamnolipid at concentrations of 20, 50, 80, and 100 mg/L. The deposition coefficient also decreased from 2.39 in the absence of rhamnolipid to 0.459, 0.279, 0.217, and 0.0966, corresponding to the presence of rhamnolipid at concentrations of 20, 50, 80, and 100 mg/L. The transport parameters of nZVI in ZK406H were linked to the interactions of nZVI particles with ZK406H by the DLVO theory.

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1β-Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2-Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS-Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS-Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1β-Inulin achieved a single conformation with the lowest energy, while COX2-Inulin and iNOS-Inulin exhibited two lowest-energy conformations each. IL-1β-Inulin and COX2-Inulin displayed total binding free energies of - 27.76 kcal/mol and - 37.78 kcal/mol, respectively, while iNOS-Inulin demonstrated the best binding free energy results at - 45.89 kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

Theoretical investigation on the solid-liquid phase transition of gallium through free energy analysis

CONTEXT

Gallium, renowned for its notably low melting point and unique property of becoming liquid at room temperature, is a valuable constituent in phase change materials. In this study, we investigate the solid-liquid phase transition of gallium using the modified embedded atom method (MEAM) potential. It addresses the technique to compute the free energy difference between the solid and liquid without using a reference state. We examine various thermodynamic and dynamic properties, including density, specific heat capacity, diffusivity, and radial distribution functions. We compute the coexistence temperature of the solid-liquid phase transitions of gallium from free energy analysis. This information is crucial for understanding the behavior of the material under different pressure conditions and can be valuable for various applications, such as materials processing and high-pressure studies. The analysis, findings, and insights of the present work will be of great significance to the broad scientific and engineering communities in the field of phase transformation of materials.

METHODS

A series of molecular dynamics(MD) simulations were conducted using the LAMMPS software packages. The gallium atoms are modeled using the modified embedded atom method (MEAM) potential. To accurately predict the solid-liquid phase transitions of gallium, we calculated free energy by employing the "constrained λ integration" method, coupled with multiple histogram reweighting (MHR). The solid-liquid coexistence line is determined through the Gibbs-Duhem integration technique.

Towards understanding the crystallization of photosystem II: influence of poly(ethylene glycol) of various molecular sizes on the micelle formation of alkyl maltosides

The influence of poly(ethylene glycol) (PEG) polymers H-(O-CH2-CH2)p-OH with different average molecular sizes p on the micelle formation of n-alkyl-β-D-maltoside detergents with the number of carbon atoms in the alkyl chain ranging from 10 to 12 is investigated with the aim to learn more about the detergent behavior under conditions suitable for the crystallization of the photosynthetic pigment-protein complex photosystem II. PEG is shown to increase the critical micelle concentration (CMC) of all three detergents in the crystallization buffer in a way that the free energy of micelle formation increases linearly with the concentration of oxyethylene units (O-CH2-CH2) irrespective of the actual molecular weight of the polymer. The CMC shift is modeled by assuming for simplicity that it is dominated by the interaction between PEG and detergent monomers and is interpreted in terms of an increase of the transfer free energy of a methylene group of the alkyl chain by 0.2 kJ mol-1 per 1 mol L-1 increase of the concentration of oxyethylene units at 298 K. Implications of this effect for the solubilization and crystallization of protein-detergent complexes as well as detergent extraction from crystals are discussed.

(De)carboxylation mechanisms of heteroaromatic substrates catalyzed by prenylated FMN-dependent UbiD decarboxylases: An in-silico study

The UbiD enzymes are proposed to catalyze reversible (de)carboxylation reaction of unsaturated carboxylic acids using prenylated flavin mononucleotide (prFMN) as a cofactor. This positions UbiD enzymes as promising candidates for converting CO2 into valuable chemicals. However, their industrial-scale biotransformation is currently constrained by low conversion rates attributed to thermodynamic limitations. To enhance the carboxylation activity of UbiD enzymes, a molecular-level understanding of the (de)carboxylation mechanisms is necessary. In this study, we investigated the reaction mechanisms of heteroaromatic substrates catalyzed by PtHmfF, PaHudA, and AnlnD enzymes using molecular dynamics (MD) simulations and free energy calculations. Our extensive mechanistic study elucidates the mechanisms involved in the formation of the initial prFMN-substrate intermediate. Specifically, we observed nucleophilic attack during decarboxylation, while carboxylation reactions involving furoic acid, pyrrole, and indole tend to favor a 1,3-dipolar cycloaddition mechanism. Furthermore, we identified proton transfer as the rate-limiting step in the carboxylation reaction. In addition, we considered the perspectives of reaction energies and electron transfer to understand the distinct mechanisms underlying decarboxylation and carboxylation. Our calculated free energies are consistent with available experimental kinetics data. Finally, we explored how different rotamers of catalytic residues influence the efficiency of the initial intermediate formation.

Graphene Exfoliation in Binary NMP/Water Mixtures by Molecular Dynamics Simulations

We investigate the molecular mechanism underlying the liquid-phase exfoliation of graphene in aqueous/N-methyl-2-pyrrolidone (NMP) solvent mixtures and calculate the associated free energies, considering different NMP concentrations and exfoliation temperatures. We employ steered molecular dynamics to establish a path for the exfoliation of a graphene sheet from graphite within each solvent environment. Then, we conduct umbrella sampling simulations throughout the created paths to compute the potential of mean force (PMF) of the graphene sheet. As the exfoliated nanosheet disperses into the liquid, it becomes fully covered by an adsorbed solvent monolayer. We analyze the composition of the monolayer by measuring the direct contacts of either NMP or water molecules with the carbon surface. The carbon surface exhibits a preference for adsorbing NMP over water. The NMP molecules form a hydrophobic compact monolayer structure, effectively protecting the carbon interface from unfavorable interactions with water. The creation of the hydrophobic monolayer is a key factor in the exfoliation process, as it effectively inhibits the restacking of exfoliated nanosheets. An adequate level of graphene solubility is achieved through the addition of 20 % to 30 % water by weight to the NMP solvent. This finding holds significant importance for improving production efficiency and reducing dependence on organic solvents in the industrial manufacturing of graphene.

To be or not to be: The active inference of suicide

Suicide presents an apparent paradox as a behavior whose motivation is not obvious since its outcome is non-existence and cannot be experienced. To address this paradox, we propose to frame suicide in the integrated theory of stress and active inference. We present an active inference-based cognitive model of suicide as a type of stress response hanging in cognitive balance between predicting self-preservation and self-destruction. In it, self-efficacy emerges as a meta-cognitive regulator that can bias the model toward either survival or suicide. The model suggests conditions under which cognitive homeostasis can override physiological homeostasis in motivating self-destruction. We also present a model proto-suicidal behavior, programmed cell death (apoptosis), in active inference terms to illustrate how an active inference model of self-destruction can be embodied in molecular mechanisms and to offer a hypothesis on another puzzle of suicide: why only humans among brain-endowed animals are known to practice it.

Effect of temperature on the degradation of glyphosate by Mn-oxide: Products and pathways of degradation

Glyphosate is the most commonly used herbicide in the United States. In the environment, glyphosate residues can either degrade into more toxic and persistent byproducts such as aminomethylphosphonic acid (AMPA) or environmentally benign species such as sarcosine or glycine. In this research, the birnessite-catalyzed degradation of glyphosate was studied under environmentally relevant temperatures (10-40 °C) using high-performance liquid chromatography, inductively coupled plasma mass spectrometry, nuclear magnetic resonance, and theoretical calculations. Our results show a temperature-dependent degradation pathway preference for AMPA and glycine production. The AMPA and glycine pathways are competitive at short reaction times, but the glycine pathway became increasingly preferred as reaction time and temperature increased. The measured free energy barriers are comparable for both the glycine and AMPA pathways (93.5 kJ mol-1 for glycine and 97.1 kJ mol-1 for AMPA); however, the entropic energy penalty for the AMPA pathway is significantly greater than the glycine pathway (-TΔS‡ = 26.2 and 42.8 kJ mol-1 for glycine and AMPA, respectively). These findings provide possible routes for biasing glyphosate degradation towards safer products, thus to decrease the overall environmental toxicity.

Molecular simulations of understanding the Na+ ion structure, dynamic and thermodynamic behavior in ionic liquids: Butyl ammonium hydrogen bisulfate and tri-butyl ammonium hydrogen bisulfate

This manuscript presents the all-atom molecular dynamics simulations to investigate intermolecular structure and solvation thermodynamics of Na+ ion in two different ammonium-based protic ionic liquids (1) Butyl Ammonium hydrogen bisulfate [BA+][HSO4-], (2) Tri-butyl ammonium hydrogen bisulfate [TBA+][HSO4-]. The ionic liquid [BA+][HSO4-] show a more coordinated behavior when compared to [TBA+][HSO4-], which is observed over the temperature range from 278 K to 348 K. Hydrogens of the cations show a hydrogen bonding interaction with oxygens of anions. The cationic [TBA+] molecules show more solvation behavior with anions when compared to the [BA+]. The Na+ ion show a strong coordination structure with [HSO4-] in [TBA+][HSO4-] when compared to the [BA+][HSO4-]. We further calculate the detailed solvation free energy (ΔG) calculations using thermodynamic integration. We found that the ΔG of Na+ is more favorable in [TBA+][HSO4-] when compared to [BA+][HSO4-] in the temperature range varying from 278 K to 348 K. With the temperature rise, we observe the more favorable solvation of Na+ in both ionic liquids. On the other hand, the solvation of Cl- becomes less favorable. Overall, this manuscript provides detailed molecular level structural and thermodynamic origins of Na+ in protic ionic liquids useful for designing and developing sustainable electrolytes for Na+ battery applications.

Computation of topographic and three-dimensional atomic force microscopy images of biopolymers by calculating forces

Atomic force microscopy (AFM) is widely utilized to visualize the molecular motions of biomolecules. Comparison of experimentally measured AFM images with simulated AFM images based on known structures of biomolecules is often necessary to elucidate what is actually resolved in the images. Experimental AFM images are generated by force measurements; however, conventional AFM simulation has been based on geometrical considerations rather than calculating forces using molecular dynamics simulations due to limited computation time. This letter summarizes recently developed methods to simulate topographic and three-dimensional AFM (3D-AFM) images of biopolymers such as chromosomes and cytoskeleton fibers. Scanning such biomolecules in AFM measurements usually results in nonequilibrium-type work being performed. As such, the Jarzynski equality was employed to relate the nonequilibrium work to the free energy profiles, and the forces were calculated by differentiating the free energy profiles. The biomolecules and probes were approximated using a supra-coarse-grained model, allowing the simulation of force-distance curves in feasible time. It was found that there is an optimum scanning velocity and that some of polymer structures are resolved in the simulated 3D-AFM images. The theoretical background adopted to rationalize the use of small probe radius in the conventional AFM simulation of biomolecules is clarified.

Interaction of sulfasalazine with outer surface of boron-nitride nanotube as a drug carrier in aqueous solution: insights from quantum mechanics and Monte Carlo simulation

The improvement of the solubility of sulfasalazine in physiological media was the major aim of this study. Accordingly, BNNT inspected as a notable candidate for the carriage of this drug in aqueous media. For this purpose, four possible interactions of two tautomer of sulfasalazine with (9,0) boron-nitride nanotube were considered in aqueous media. The compounds were optimized in gas phase using density functional calculations. Solvation free energies and association free energies of the optimized structures were then studied by Monte Carlo simulation and perturbation method in water environment. Outcomes of quantum mechanical calculations presented that interaction of keto form of sulfasalazine produce the most stable complexes with boron-nitride nanotube in gas phase. Simulation results revealed that electrostatic interactions play a vital role in the intermolecular interaction energies after binding of drug and nanotube in aqueous solution. Results of association free energy calculations indicated that complexes of both two sulfasalazine tautomers (keto and enol) and nanotube were stable in solution. Computed solvation free energies in water showed that the interaction with boron-nitride nanotube significantly improved the solubility of sulfasalazine, which could improve its in vivo bioavailability.

Pharmacotherapeutic Potential of Natural Products to Target the SARS-CoV-2 PLpro Using Molecular Screening and Simulation Approaches

Because of the essential role of PLpro in the regulation of replication and dysregulation of the host immune sensing, it is considered a therapeutic target for novel drug development. To reduce the risk of immune evasion and vaccine effectiveness, small molecular therapeutics are the best complementary approach. Hence, we used a structure-based drug-designing approach to identify potential small molecular inhibitors for PLpro of SARS-CoV-2. Initial scoring and re-scoring of the best hits revealed that three compounds NPC320891 (2,2-Dihydroxyindene-1,3-Dione), NPC474594 (Isonarciclasine), and NPC474595 (7-Deoxyisonarciclasine) exhibit higher docking scores than the control GRL0617. Investigation of the binding modes revealed that alongside the essential contacts, i.e., Asp164, Glu167, Tyr264, and Gln269, these molecules also target Lys157 and Tyr268 residues in the active site. Moreover, molecular simulation demonstrated that the reported top hits also possess stable dynamics and structural packing. Furthermore, the residues' flexibility revealed that all the complexes demonstrated higher flexibility in the regions 120-140, 160-180, and 205-215. The 120-140 and 160-180 lie in the finger region of PLpro, which may open/close during the simulation to cover the active site and push the ligand inside. In addition, the total binding free energy was reported to be - 32.65 ± 0.17 kcal/mol for the GRL0617-PLpro, for the NPC320891-PLpro complex, the TBE was - 35.58 ± 0.14 kcal/mol, for the NPC474594-PLpro, the TBE was - 43.72 ± 0.22 kcal/mol, while for NPC474595-PLpro complex, the TBE was calculated to be - 41.61 ± 0.20 kcal/mol, respectively. Clustering of the protein's motion and FEL further revealed that in NPC474594 and NPC474595 complexes, the drug was seen to have moved inside the binding cavity along with the loop in the palm region harboring the catalytic triad, thus justifying the higher binding of these two molecules particularly. In conclusion, the overall results reflect favorable binding of the identified hits strongly than the control drug, thus demanding in vitro and in vivo validation for clinical purposes.

TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

Exploring the medicinal potential of Dark Chemical Matters (DCM) to design promising inhibitors for PLpro of SARS-CoV-2 using molecular screening and simulation approaches

The growing concerns and cases of COVID-19 with the appearance of novel variants i.e., BA.2.75. BA.5 and XBB have prompted demand for more effective treatment options that could overcome the risk of immune evasion. For this purpose, discovering novel small molecules to inhibit druggable proteins such as PLpro required for viral pathogenesis, replication, survival, and spread is the best choice. Compounds from the Dark chemical matter (DCM) database is consistently active in various screening tests and offer intriguing possibilities for finding drugs that are extremely selective or active against uncommon targets. Considering the essential role of PLpro, the current study uses DCMdatabase for the identification of potential hits using in silico virtual molecular screening and simulation approaches to inhibit the current and emerging variants of SARS-CoV-2. Our results revealed the 10 best compounds with docking scores between -7.99 to -7.03 kcal/mol better than the control drug (GRL0617) among which DC 5977-0726, DC 6623-2024, DC C879-0379 and DC D135-0154 were observed as the best hits. Structural-dynamics properties such as dynamic stability, protein packing, and residue flexibility demonstrated the pharmacologically favorable properties of these top hits in contrast to GRL0617. The hydrogen bonding half-life revealed that Asp164, Arg166, Tyr264, and Tyr268 have major contributions to the hydrogen bonding during the simulation. However, some of the important hydrogen bonds were missing in the control drug (GRL0617). Finally, the total binding free energy was reported to be -34.41 kcal/mol for GRL0617 (control), -41.03 kcal/mol for the DC5977-0726-PLpro, for the DC6623-2024-Plpro complex the TBE was -48.87 kcal/mol, for the for DCC879-0379-Plpro complex the TBE was -45.66 kcal/mol while for the DCD135-0154-PLpro complex the TBE was calculated to be -40.09 kcal/mol respectively, which shows the stronger potency of these compounds against PLpro and further in in vivo and in vitro test are required for the possible usage as potential drug against SARS-CoV-2.

Theoretical investigation of hydroxylated analogues of valinomycin as potassium transporter

Valinomycin is a potent ionophore known for its ability to transport potassium ions across biological membranes. The study focuses on the hydroxylated analogues of valinomycin (HyVLMs) and compares their energy profiles and capabilities for transporting potassium ions across phospholipid membranes. Using metadynamics, we investigated the energy profiles of wildtype valinomycin (VLM_1) and its three hydroxylated analogues (VLM_2, VLM_3, and VLM_4). We observed that all analogues exhibited energy maxima in the centre of the membrane and preferred positions below the phospholipid heads. Furthermore, the entry barriers for membrane penetration were similar among the analogues, suggesting that the hydroxyl group did not significantly affect their passage through the membrane. Transition state calculations provided insights into the ability of valinomycin analogues to capture potassium ions, with VLM_4 showing the lowest activation energy and VLM_2 displaying the highest. Our findings contribute to understanding the mechanisms of potassium transport by valinomycin analogues and highlight their potential as ionophores. The presence of the hydroxyl group is of particular importance because it paves the way for subsequent chemical modifications and the synthesis of new antiviral agents with reduced intrinsic toxicity.

Computational studies indicated the effectiveness of human metabolites against SARS-Cov-2 main protease

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CLpro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

Electric field as a crystallization switch of heterogeneous ice formation

Using molecular dynamics simulations, we investigated the effect of external electric field on ice formation with the present of a substrate surface. It turns out that the electric field can affect the ice formation on substrate surface by altering the dipole orientation of interfacial water molecules: a crossover from inhibiting to promoting ice formation with the increase of electric field strength. According to the influence of the electric field on ice formation, the electric field strength range of 0.0 V/nm - 7.0 V/nm can be divided into three regions. In the region I and region III, there are both ice formation on the substrate surface. While, the behavior of interfacial water molecules in the region I and region III are distinguished, including the arrangements of oxygen atoms and the dipole orientation distribution. In region II, ice formation does not occur in the system within 5 × 200 ns simulations. The interfacial water molecules show a disorder structure, preventing the ice formation process on substrate. The interfacial water molecular orientation distribution and 2-dimentional free energy landscape reveals that the electric field can alter the dipole orientation of the interfacial water and lead a free energy barrier, making the ice formation process harder. Our result demonstrates the external electric field can regulate the behavior of interfacial water molecules, and further affect the ice formation process. The external electric field act as a crystallization switch of ice formation on substrate, shedding light into the studies on the control of ice crystallization.

Multi-scale modeling of natural organic matter-heavy metal cations interactions: Aggregation and stabilization mechanisms

Interaction between natural organic matters (NOM) and heavy metal cations in aqueous environment are of great significance for maintaining stability of organic carbon and restraining transport of heavy metal contaminants in (bio)geochemical processes. We systematically explore the aggregation process and complexation between NOM and heavy metal cations (Ag⁺, Cd²⁺, Pb²⁺, Zn²⁺, Eu³⁺) under different pH condition by molecular dynamics (MD) simulations, umbrella sampling method, and quantum chemistry calculations. The character of molecular structures NOM-heavy metal complexes and association are quantified. In acidic pH condition, aggregation proceeds via H-bonding and π-π interactions between NOM fragments. In neutral condition, Ag⁺, Cd²⁺, Pb²⁺, and Eu³⁺ can form inner-sphere complexes with the surface carboxylic groups and therefore reduce intermolecular charge repulsion, eventually leading to NOM aggregation, and it shows that even without direct binding, the outer-sphere adsorbed Zn²⁺ can also result in the formation of NOM assemble through H-bonding. Consequently, these heavy metals are capable of promoting NOM aggregation regardless of the complexing ways. Complexing free energy calculations characterized the dynamic processes of cations binding to the carboxylic groups of NOM fragment and the related energy landscape. This study provides quantitative insights for understanding the environmental processes of heavy metals and cycle of C in aquatic ecosystem, and contributes to developing environment-friendly strategies for controlling heavy metal contaminants.

Insights into the binding mechanism of ascorbic acid and violaxanthin with violaxanthin de-epoxidase (VDE) and chlorophycean violaxanthin de-epoxidase (CVDE) enzymes

Photosynthetic organisms have evolved to work under low and high lights in photoprotection, acting as a scavenger of reactive oxygen species. The light-dependent xanthophyll cycle involved in this process is performed by a key enzyme (present in the thylakoid lumen), Violaxanthin De-Epoxidase (VDE), in the presence of violaxanthin (Vio) and ascorbic acid substrates. Phylogenetically, VDE is found to be connected with an ancestral enzyme Chlorophycean Violaxanthin De-Epoxidase (CVDE), present in the green algae on the stromal side of the thylakoid membrane. However, the structure and functions of CVDE were not known. In search of functional similarities involving this cycle, the structure, binding conformation, stability, and interaction mechanism of CVDE are explored with the two substrates compared to VDE. The structure of CVDE was determined by homology modeling and validated. In silico docking (of first-principles optimized substrates) revealed it has a larger catalytic domain than VDE. A thorough analysis of the binding affinity and stability of four enzyme-substrate complexes is performed by computing free energies and their decomposition, the root-mean-square deviation (RMSD) and fluctuation (RMSF), the radius of gyration, salt bridge, and hydrogen bonding interactions in molecular dynamics. Based on these, violaxanthin interacts with CVDE to a similar extent as that of VDE. Hence, its role is expected to be the same for both enzymes. On the contrary, ascorbic acid has a weaker interaction with CVDE than VDE. Given these interactions drive epoxidation or de-epoxidation in the xanthophyll cycle, it immediately discerns that either ascorbic acid does not participate in de-epoxidation or a different cofactor is necessary as CVDE has a weaker interaction with ascorbic acid than VDE.

Towards a free energy-based elastic network model and its application to the SARS-COV2 binding to ACE2

Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of NMA exist that perform normal mode analysis directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. sbNMA is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model for normal mode analysis. The vast majority of elastic network models are entropy models. One significance of having a free energy-based model for normal mode analysis is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.

Picloram binds to the h1 and h4 helices of HSA domain IIIA at drug binding site 2

Picloram (PC) is a systemic herbicide that controls herbaceous weeds and woody plants. HSA, the most abundant protein in human physiology, binds to all exogenic and endogenic ligands. PC is a stable molecule (t_1/2~157-513 days) and a potential threat to human health via the food chain. HSA and PC binding study has been done to decipher the location and thermodynamics of binding. It has been studied with prediction tools like autodocking and MD simulation and then confirmed with fluorescence spectroscopy. HSA fluorescence was quenched by PC at pH 7.4 (N state), pH 3.5 (F state), and pH 7.4 with 4.5 M urea (I state) at temperatures 283 K, 297 K, and 303 K. The location of binding was found to be interdomain between II and III which overlaps with drug binding site 2. The binding was spontaneous, and entropy-driven that show a noticeable increase in binding with the increase in temperature. No secondary structure change at the native state has been observed due to binding. The binding results are important to understand the physiological assimilation of PC. In silico predictions and the results of spectroscopic studies unambiguously indicate the locus and nature of the binding.

Experimental determination of surface energy for high-energy surface: A review

The experimental determination of surface free energy is of great importance for engineering applications. However, there is no universal, reliable, and convenient measurement means to achieve experimental determination of solid surface energy for high-energy surface. In this work, the existing techniques for experimental determination of surface energy of solids, including indirect (4 kinds) and direct methods (3 kinds), were critically reviewed. In the indirect methods: the explicit interfacial bonding characteristics are required for the multiphase equilibrium technique and for the determination method from crystal equilibrium shape; the critical surface energy technique does not satisfy Zisman's hypothesis that the solid-liquid interface tension is zero (or close to zero), and the parameters fitted by empirical equations cannot have definite physical meaning; the derivation based on the surface tension in the liquid state can only obtain the surface energy of the solid phase near the melting point, and is limited to the prediction of the surface energy of elemental metal. Among the direct determination methods, except for the zero-creep method, are based on generalized determination technologies. All determined results are strongly influenced by the accuracy of the particle (grain) size, scale effect, the atmosphere, etc. This leads to the fact that the errors are still huge regardless of the determination method used, and it is difficult to achieve uniformity of the data. All methods rely to some extent on specific assumptions or theoretical models.

Ligand binding free energy evaluation by Monte Carlo Recursion

The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).

Molecular dynamics and absolute binding free energy studies of piperine derivatives as potential inhibitors of SARS-CoV-2 main protease

The work presents a library of piperine derivatives as potential inhibitors of the main protease protein (Mpro) functionality using Docking Studies, Molecular Dynamics (MD) Simulations and Absolute Binding Free-Energy calculations. 342 ligands were selected for this work and docked with Mpro protein. Among all the ligands studied, PIPC270, PIPC299, PIPC252, PIPC63, PIPC311 were the top five docked conformations having significant hydrogen bonding and hydrophobic interactions inside the active pocket of Mpro. These top five ligands were subjected to MD simulations for 100 ns using GROMACS. Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA) and hydrogen bond analysis revealed that the ligands bounded to protein remain stable without significant deviations during the course of MD simulations. Absolute binding free energy (ΔGb) was calculated for theses complexes and found that the ligand PIPC299 shows the prevalent binding affinity with binding free-energy of about -113.05 Kcal/mol. Thus, these molecules can be further tested by in vitro and in vivo studies on Mpro. This study lays a path to explore the new functionality of piperine derivatives as novel drug like molecules.Communicated by Ramaswamy H. Sarma.

Free energy landscape of wrapping of lipid nanocluster by polysaccharides

Wrapping of a 20-mer cholesterol nano-cluster (CHL-nanoC) by two widely different types of β-glucan polysaccharides (23-25 mers) having significantly varying glycosidic linkage patterns and side chains is studied by Well-Tempered MetaDynamics (WT-MetaD) simulations. The problem has its relevance in the faecal sterol and bile acid excretion in humans and the role of dietary fibres in aiding the process and combating dyslipidemia. Additionally, the distinctive collective variables studied here can be extended for modeling of polymer wrapped soft clusters/nano-particles in general. The wrapping ability is observed to be significantly correlated to the bending of the polysaccharide chain, an attribute of the glycosidic linkage type. By biasing two unique collective variables, the radius of gyration of the polysaccharide (R_{g, poly}) and the second order Legendre polynomial of the segment orientation parameter, θ, we could successfully observe the wrapping process. This work compares in detail the physical properties of the polysaccharide encapsulated CHL-nanoC by probing the radius of curvature (R_{curv, poly}) of the polysaccharides, their coordination number with respect to the CHL-nanoC (CN), fractional CHL-nanoC surface coverage and the electrostatic surface potentials of the complex assembly. Results indicate that the β-glucan having 1-4 glycosidic linked monomers with intermittent 1-3 linkage is able to wrap the CHL-nanoC more effectively. The 1-3 glycosidic linked β-glucan with 1-6 glycosidic bonds in side chains is significantly curled up and appears to be less efficient in wrapping the nanoC. This work provides a comparative molecular level picture of mutual interaction between two major dietary polysaccharide variants and lipid globules as indicated by numerous clinical level studies involving mice and human models.

Molecular surveillance of Kelch-13 gene in Plasmodium falciparum field isolates from Mayurbhanj District, Odisha, India, and in silico artemisinin-Kelch-13 protein interaction study

The global malaria control and elimination program faces major threats due to the emergence and transmission of the anti-malarial drug-resistant strain of Plasmodium falciparum. Monitoring of artemisinin (ART) resistance marker Kelch-13 in the malaria-endemic region is essential in mitigating the disease's morbidity and mortality. The current study aimed to generate baseline information for further surveillance in the future. The current research was designed and conducted from July 2019 to June 2021 to monitor Pfkelch13 mutation at the molecular level in the eastern region of India. We also conducted an in silico study to understand the drug-protein interactions between ART and the protein crystal of PfKelch13 (KELCH) with PDB id:4ZGC. The kelch-13 gene was amplified by nested polymerase chain reaction (PCR) and sequenced through the Sanger sequencing method. Reference 3D7 clone (PF3D7_1343700) was used to align and probe all the sequences. The sequence analysis showed the absence of validated or associated mutation in the Kelch-13 propeller domain. The absence of natural selection in drug resistance was confirmed by the Tajima test. Further, in silico interaction studies between the drug ART and the Kelch propeller domain of P. falciparum were evaluated by structure predictions, molecular docking, molecular dynamics (MD) simulations, and estimations of binding free energies for the KELCH-ART complex. The results were compared with the apoprotein (KELCH-APO). The study confirmed the favorable binding of ART with the Kelch-13 propeller domain.

The molecular memory code and synaptic plasticity: A synthesis

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.

Molecular Dynamics Simulation for Investigating Antigen-Antibody Interaction

Molecular dynamics (MD) simulation is a computational method which elucidates the protein dynamics. Following analyses characterize the dynamics and structural change as well as interaction energy. To characterize the protein structure effectively, the internal angular coordinates are often useful. Directional analysis provides the averages and variances of those coordinates in a mathematically rigorous way. Here, we describe not only a standard MD simulation procedure for the antigen-antibody system but also an umbrella sampling method following a multistep targeted MD simulation (US/mTMD), which is useful for evaluating the free energy profile along the antigen-antibody dissociation coordinate.

Ca2+ and Mg2+ Influence the Thermodynamics of Peptide-Membrane Interactions

Accurate quantitative estimates of protein-membrane interactions are critical to studies of membrane proteins. Here, we demonstrate that thermodynamic analyses based on current hydropathy scales do not account for the significant and experimentally determined effects that Ca2+ or Mg2+ have on protein-membrane interactions. We examined distinct modes of interaction (interfacial partitioning and folding and transmembrane insertion) by studying three highly divergent peptides: Bid-BH3 (derived from apoptotic regulator Bid), peripherin-2-derived prph2-CTER, and the cancer-targeting pH-Low-Insertion-Peptide (pHLIP). Fluorescence experiments demonstrate that adding 1-2 mM of divalent cations led to a substantially more favorable bilayer partitioning and insertion, with free energy differences of 5-15 kcal/mol.

Effect of the R126C mutation on the structure and function of the glucose transporter GLUT1: A molecular dynamics simulation study

Glucose transporter 1 (GLUT1) is responsible for basal glucose uptake and is expressed in most tissues under normal conditions. GLUT1 mutations can cause early-onset absence epilepsy and myoclonus dystonia syndrome (MDS), with MDS potentially lethal. In this study, the effect of the R126C mutation, which is associated with MDS, on structural stability and substrate transport of GLUT1 was investigated. Various bioinformatics tools were used to predict the stability of GLUT1, revealing that the R126C mutation reduces the structural stability of GLUT1. Molecular dynamics (MD) simulations were used to further characterize the effect of the R126C mutation on GLUT1 structural stability. Based on the MD simulations, specific conformational changes and dominant motions of the GLUT1 mutant were characterized by Principal component analysis (PCA). The mutation disrupts hydrogen bonds between substrate-binding residues and glucose, thus likely reducing substrate affinity. The R126C mutation reduces the conformational stability of the protein, and fewer intramolecular hydrogen bonds were present in the mutated GLUT1 when compared with that of wild-type GLUT1. The mutation increased the free energy of glucose transport through GLUT1 significantly, especially at the mutation site, indicating that passage of glucose through the channel is hindered, and this mutant may even release cytoplasmic glucose. This study provides a detailed atomic-level explanation for the reduced structural stability and substrate transport capacity of a GLUT1 mutant. The results aid our understanding of the structure of GLUT1 and provide a framework for developing drugs to treat GLUT1-related diseases, such as MDS.

An overview of the SAMPL8 host-guest binding challenge

The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.

Quantitative characterization of the path of glucose diffusion facilitated by human glucose transporter 1

Glucose transporter GLUT1 is ubiquitously expressed in the human body from the red cells to the blood-brain barrier to the skeletal muscles. It is physiologically relevant to understand how GLUT1 facilitates diffusion of glucose across the cell membrane. It is also pathologically relevant because GLUT1 deficiency causes neurological disorders and anemia and because GLUT1 overexpression fuels the abnormal growth of cancer cells. This article presents a quantitative investigation of GLUT1 based on all-atom molecular-dynamics (MD) simulations of the transporter embedded in lipid bilayers of asymmetric inner-and-outer-leaflet lipid compositions, subject to asymmetric intra-and-extra-cellular environments. This is in contrast with the current literature of MD studies that have not considered both of the aforementioned asymmetries of the cell membrane. The equilibrium (unbiased) dynamics of GLUT1 shows that it can facilitate glucose diffusion across the cell membrane without undergoing large-scale conformational motions. The Gibbs free-energy profile, which is still lacking in the current literature of GLUT1, quantitatively characterizes the diffusion path of glucose from the periplasm, through an extracellular gate of GLUT1, on to the binding site, and off to the cytoplasm. This transport mechanism is validated by the experimental data that GLUT1 has low water-permeability, uptake-efflux symmetry, and 10 kcal/mol Arrhenius activation barrier around 37 °C.

Left and right temporal-parietal junctions (TPJs) as "match/mismatch" hedonic machines: A unifying account of TPJ function

Experimental and theoretical studies have tried to gain insights into the involvement of the Temporal Parietal Junction (TPJ) in a broad range of cognitive functions like memory, attention, language, self-agency and theory of mind. Recent investigations have demonstrated the partition of the TPJ in discrete subsectors. Nonetheless, whether these subsectors play different roles or implement an overarching function remains debated. Here, based on a review of available evidence, we propose that the left TPJ codes both matches and mismatches between expected and actual sensory, motor, or cognitive events while the right TPJ codes mismatches. These operations help keeping track of statistical contingencies in personal, environmental, and conceptual space. We show that this hypothesis can account for the participation of the TPJ in disparate cognitive functions, including "humour", and explain: a) the higher incidence of spatial neglect in right brain damage; b) the different emotional reactions that follow left and right brain damage; c) the hemispheric lateralisation of optimistic bias mechanisms; d) the lateralisation of mechanisms that regulate routine and novelty behaviours. We propose that match and mismatch operations are aimed at approximating "free energy", in terms of the free energy principle of decision-making. By approximating "free energy", the match/mismatch TPJ system supports both information seeking to update one's own beliefs and the pleasure of being right in one's own' current choices. This renewed view of the TPJ has relevant clinical implications because the misfunctioning of TPJ-related "match" and "mismatch" circuits in unilateral brain damage can produce low-dimensional deficits of active-inference and predictive coding that can be associated with different neuropsychological disorders.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.

Free Energy Landscape of H2A-H2B Displacement From Nucleosome

Nucleosome reconstitution plays an important role in many cellular functions. As an initial step, H2A-H2B dimer displacement, which is accompanied by disruption of many of the interactions within the nucleosome, should occur. To understand how H2A-H2B dimer displacement occurs, an adaptively biased molecular dynamics (ABMD) simulation was carried out to generate a variety of displacements of the H2A-H2B dimer from the fully wrapped to partially unwrapped nucleosome structures. With regards to these structures, the free energy landscape of the dimer displacement was investigated using umbrella sampling simulations. We found that the main contributors to the free energy were the docking domain of H2A and the C-terminal of H4. There were various paths for the dimer displacement which were dependent on the extent of nucleosomal DNA wrapping, suggesting that modulation of the intra-nucleosomal interaction by external factors such as histone chaperons could control the path for the H2A-H2B dimer displacement. Key residues which contributed to the free energy have also been reported to be involved in the mutations and posttranslational modifications (PTMs) which are important for assembling and/or reassembling the nucleosome at the molecular level and are found in cancer cells at the phenotypic level. Our results give insight into how the H2A-H2B dimer displacement proceeds along various paths according to different interactions within the nucleosome.

Friston's free energy principle: new life for psychoanalysis?

The free energy principle (FEP) is a new paradigm that has gain widespread interest in the neuroscience community. Although its principal architect, Karl Friston, is a psychiatrist, it has thus far had little impact within psychiatry. This article introduces readers to the FEP, points out its consilience with Freud's neuroscientific ideas and with psychodynamic practice, and suggests ways in which the FEP can help explain the mechanisms of action of the psychotherapies.

An informal reconstruction of the free-energy framework, examining the conceptual problems that arise

Recent decades have seen increasing attention given to the free-energy framework. This proposes that many of the phenomena we associate with life, intelligence and adaptation can be explained in terms of a single, mathematical precept: the free-energy principle. This is claimed to apply to the adaptive behavior of primitive organisms as much as it does to the physiological structures of human brains. The proposal is potentially of interest to theorists from multiple disciplines. But as presentations often make intensive use of mathematical notation, it can be hard to understand, even for experts. The present article presents an informal reconstruction, using schematic illustrations. Mathematical notation is largely avoided, while detail and precision are retained as far as possible. The specifically conceptual problems that come to notice in the reconstruction are highlighted and discussed.

Self-association features of NS1 proteins from different flaviviruses

Flaviviruses comprise a large group of arboviral species that are distributed in several countries of the tropics, neotropics, and some temperate zones. Since they can produce neurological pathologies or vascular damage, there has been intense research seeking better diagnosis and treatments for their infections in the last decades. The flavivirus NS1 protein is a relevant clinical target because it is involved in viral replication, immune evasion, and virulence. Being a key factor in endothelial and tissue-specific modulation, NS1 has been largely studied to understand the molecular mechanisms exploited by the virus to reprogram host cells. A central part of the viral maturation processes is the NS1 oligomerization because many stages rely on these protein-protein assemblies. In the present study, the self-associations of NS1 proteins from Zika, Dengue, and West Nile viruses are examined through constant-pH coarse-grained biophysical simulations. Free energies of interactions were estimated for different oligomeric states and pH conditions. Our results show that these proteins can form both dimers and tetramers under conditions near physiological pH even without the presence of lipids. Moreover, pH plays an important role mainly controlling the regimes where van der Waals interactions govern their association. Finally, despite the similarity at the sequence level, we found that each flavivirus has a well-characteristic protein-protein interaction profile. These specific features can provide new hints for the development of binders both for better diagnostic tools and the formulation of new therapeutic drugs.

Plant-derived active compounds as a potential nucleocapsid protein inhibitor of SARS-CoV-2: an in-silico study

The coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2. This virus has a high mismatch repair proofreading ability due to its unique exonuclease activity, making it knotty to treat. The nucleocapsid protein can serve as a potential antiviral drug target, as this protein is responsible for multiple captious functions during the viral life cycle. Herein, we have investigated the potential to repurpose active antiviral compounds of plant origins for treating the SARS-CoV-2 infection. In the present study, we followed the molecular docking methodology to screen druggable natural plants' active compounds against the nucleocapsid protein of SARS-CoV-2. The virtual screening of all 68 compounds revealed that the top seven active compounds, such as withanolide D, hypericin, silymarin, oxyacanthine, withaferin A, Acetyl aleuritolic acid, and rhein, exhibit good binding affinity with druggable ADME properties, toxicity, and Pass prediction. The stability of the docked complexes was studied by conducting molecular simulations of 100 ns. MM-GBSA calculated the binding free energy uncovered that withanolide D, hypericin, and silymarin result in highly stable binding conformations in three different sites of the nucleocapsid protein. However, further investigation is needed in order to validate the candidacy of these inhibitors for clinical trials. HighlightsNatural plants' active compounds may aid in the inhibition of SARS-CoV-2 replication and COVID-19 therapeutics.Hypericin, silymarin, withanolide D, oxyacanthine, withaferin A, Acetyl aleuritolic acid, and rhein are effective against SARS-CoV-2 N protein.Studied natural plants' active compounds could be useful against COVID-19 and its associated organs comorbidities.ADMET properties of selected compounds favor these compounds as druggable candidates.Communicated by Ramaswamy H. Sarma.

Simulations suggest double sodium binding induces unexpected conformational changes in thrombin

Thrombin is a Na[Formula: see text]-activated serine protease existing in two forms targeted to procoagulant and anticoagulant activities, respectively. There is one Na[Formula: see text]-binding site that has been the focus of the study of the thrombin. However, molecular dynamics (MD) simulations suggest that there might be actually two Na[Formula: see text]-binding sites in thrombin and that Na[Formula: see text] ions can even bind to two sites simultaneously. In this study, we performed 12 independent 2-µs all-atom MD simulations for the wild-type (WT) thrombin and we studied the effects of the different Na[Formula: see text] binding modes on thrombin. From the root-mean-square fluctuations (RMSF) for the [Formula: see text]-carbons, we see that the atomic fluctuations mainly change in the 60s, 170s, and 220s loops, and the connection (residue 167 to 170). The correlation matrices for different binding modes suggest regions that may play an important role in thrombin's allosteric response and provide us a possible allosteric pathway for the sodium binding. Amorim-Hennig (AH) clustering tells us how the structure of the regions of interest changes on sodium binding. Principal component analysis (PCA) shows us how the different regions of thrombin change conformation together with sodium binding. Solvent-accessible surface area (SASA) exposes the conformational change in exosite I and catalytic triad. Finally, we argue that the double binding mode might be an inactive mode and that the kinetic scheme for the Na[Formula: see text] binding to thrombin might be a multiple-step mechanism rather than a 2-step mechanism.

An introduction to thermodynamic integration and application to dynamic causal models

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS.

Computational modelling of potentially emerging SARS-CoV-2 spike protein RBDs mutations with higher binding affinity towards ACE2: A structural modelling study

The spike protein of SARS-CoV-2 and the host ACE2 receptor plays a vital role in the entry to the cell. Among which the hotspot residue 501 is continuously subjected to positive selection pressure and induces unusual virulence. Keeping in view the importance of the hot spot residue 501, we predicted the potentially emerging structural variants of 501 residue. We analyzed the binding pattern of wild type and mutants (Spike RBD) to the ACE2 receptor by deciphering variations in the amino acids’ interaction networks by graph kernels along with evolutionary, network metrics, and energetic information. Our analysis revealed that N501I, N501T, and N501V increase the binding affinity and alter the intra and inter-residue bonding networks. The N501T has shown strong positive selection and fitness in other animals. Docking results and repeated simulations (three times) confirmed the structural stability and tighter binding of these three variants, correlated with the previous results following the global stability trend. Consequently, we reported three variants N501I, N501T, and N501V could worsen the situation further if they emerged. The relations between the viral fitness and binding affinity is a complicated game thus the emergence of high affinity mutations in the SARS-CoV-2 RBD brings up the question of whether or not positive selection favours these mutations or not?

Characterizing Thermodynamics of Protein-Glycosaminoglycan Interactions Using Isothermal Titration Calorimetry

It has now become increasingly clear that a complete atomic description of how biomacromolecules recognize each other requires knowledge not only of the structures of the complexes but also of how kinetics and thermodynamics drive the binding process. In particular, such knowledge is lacking for protein-glycosaminoglycan (GAG) complexes. Isothermal titration calorimetry (ITC) is the only technique that can provide all of the thermodynamic parameters-enthalpy, entropy, free energy (binding constant), and stoichiometry-from a single experiment. Here we describe different factors that must be taken into consideration in carrying out ITC titrations to obtain meaningful thermodynamic data of protein-GAG interactions.

Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria

This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.

Molecular dynamics simulations reveal the plausible agonism/antagonism mechanism by steroids on androgen receptor mutations

Androgen receptors (AR) are the primary drug target in prostate cancer (PCa). There are several drugs developed against its activity for prostate cancer treatment, but cancer cells revive AR signaling against those drugs by using alternative steroids such as glucocorticoids. In addition, antagonists become agonists due to emergence of mutations in AR gene. The mechanism by which antagonists are converted into agonists and how AR signaling is recovered by other steroids has yet to be fully elucidated. In this study, we interrogated the role of bicalutamide conformation in its antagonist function and how glucocorticoids such as prednisolone and dexamethasone revive AR signaling at the molecular level by means of molecular dynamics. We found that the ''closed'' conformation of bicalutamide is essential for its antagonist function and W741 residue is forcing it into this conformation. Moreover, we show that prednisolone and dexamethasone behave like natural agonist DHT which confirm the experimental results that show their role in the reviving AR signaling in the case of AR^L701H mutation.

Bioinformatics analysis of the differences in the binding profile of the wild-type and mutants of the SARS-CoV-2 spike protein variants with the ACE2 receptor

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Reports of new variants that potentially increase virulence and viral transmission, as well as reduce the efficacy of available vaccines, have recently emerged. In this study, we computationally analyzed the N439K, S477 N, and T478K variants for their ability to bind Angiotensin-converting enzyme 2 (ACE2). We used the protein-protein docking approach to explore whether the three variants displayed a higher binding affinity to the ACE2 receptor than the wild type. We found that these variants alter the hydrogen bonding network and the cluster of interactions. Additional salt bridges, hydrogen bonds, and a high number of non-bonded contacts (i.e., non-bonded interactions between atoms in the same molecule and those in other molecules) were observed only in the mutant complexes, allowing efficient binding to the ACE2 receptor. Furthermore, we used a 2.0-μs all-atoms simulation approach to detect differences in the structural dynamic features of the resulting protein complexes. Our findings revealed that the mutant complexes possessed stable dynamics, consistent with the global trend of mutations yielding variants with improved stability and enhanced affinity. Binding energy calculations based on molecular mechanics/generalized Born surface area (MM/GBSA) further revealed that electrostatic interactions principally increased net binding energies. The stability and binding energies of N439K, S477 N, and T478K variants were enhanced compared to the wild-type-ACE2 complex. The net binding energy of the systems was -31.86 kcal/mol for the wild-type-ACE2 complex, -67.85 kcal/mol for N439K, -69.82 kcal/mol for S477 N, and -69.64 kcal/mol for T478K. The current study provides a basis for exploring the enhanced binding abilities and structural features of SARS-CoV-2 variants to design novel therapeutics against the virus.

Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations

SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, M^pro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of M^pro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using M^pro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with M^pro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for M^pro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.

Molecular dynamics simulations of doxorubicin in sphingomyelin-based lipid membranes

Doxorubicin (DOX) is one of the most efficient antitumor drugs employed in numerous cancer therapies. Its incorporation into lipid-based nanocarriers, such as liposomes, improves the drug targeting into tumor cells and reduces drug side effects. The carriers' lipid composition is expected to affect the interactions of DOX and its partitioning into liposomal membranes. To get a rational insight into this aspect and determine promising lipid compositions, we use numerical simulations, which provide unique information on DOX-membrane interactions at the atomic level of resolution. In particular, we combine classical molecular dynamics simulations and free energy calculations to elucidate the mechanism of penetration of a protonated Doxorubicin molecule (DOX⁺) into potential liposome membranes, here modeled as lipid bilayers based on mixtures of phosphatidylcholine (PC), sphingomyelin (SM) and cholesterol lipid molecules, of different compositions and lipid phases. Moreover, we analyze DOX⁺ partitioning into relevant regions of SM-based lipid bilayer systems using a combination of free energy methods. Our results show that DOX⁺ penetration and partitioning are facilitated into less tightly packed SM-based membranes and are dependent on lipid composition. This work paves the way to further investigations of optimal formulations for lipid-based carriers, such as those associated with pH-responsive membranes.

In Silico Mutagenesis-Based Remodelling of SARS-CoV-1 Peptide (ATLQAIAS) to Inhibit SARS-CoV-2: Structural-Dynamics and Free Energy Calculations

The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. Multi-directional efforts are made to design small molecule inhibitors, and vaccines and many other therapeutic options are practiced, but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedures. Hence, here, we have repurposed a small peptide (ATLQAIAS) from the previous study, which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R, and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residue energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at the residue level. Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARS-CoV-2-borne pneumonia. Our research strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak.