Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus.

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles.

BACKGROUND: Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge. RESULTS: This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects. CONCLUSIONS: The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Allosteric Signal within the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein Mediated by a Class 3 Monoclonal Antibody Revealed through Molecular Dynamics Simulations and Protein Residue Networks.

This study investigated the allosteric action within the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein caused by class 3 monoclonal antibody (mAb) binding. As the emergence of SARS-CoV-2 variants has raised concerns about the effectiveness of treatments by antibodies, targeting the highly conserved class 3 epitopes has become an alternative strategy of antibody design. Simulations of explicitly solvated RBD of the BA.2.75 omicron subvariants were carried out both in the presence and in the absence of bebtelovimab, as a model example of class 3 monoclonal antibodies against the RBD of the SARS-CoV-2 spike protein. The comparative analysis showed that bebtelovimab's binding on two α helices at the epitope region disrupted the nearby interaction network, which triggered a denser interaction network formation on the opposite side of the receptor-binding motif (RBM) region and resulted in a "close" conformation that could prevent the ACE2 binding. A better understanding of this allosteric action could lead to the development of alternative mAbs for further variants of concern. In terms of computational techniques, the communicability matrix could serve as a tool to visualize the effects of allostery, as the pairs of amino acids or secondary structures with high communicability could pinpoint the possible sites to transfer the allosteric signal. Additionally, the communicability gain/loss matrix could help elucidate the consequences of allosteric actions, which could be employed along with other allostery quantification techniques in some previous studies.

Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database.

The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms, which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study, a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms. The mTORC1 active compound data set from the PubChem database was first filtered for only the compounds resided near the first-generation inhibitors (rapalogs) within the first two PCA coordinates of chemical features. A deep learning model trained by the filtered data set captured the main characteristics of rapalogs and displayed the importance of steroid cores. After that, another layer of virtual screening by molecular docking calculations was performed on ternary complexes of FKBP12-FRB domains and six compound candidates with high "active" probability scores predicted by the deep learning models. Finally, all-atom molecular dynamics simulations and MMPBSA binding energy analysis were performed on two selected candidates in comparison to rapamycin, which confirmed the importance of ring groups and steroid cores for interaction networks. Trihydroxysterol from Lentinus polychrous Lev. was predicted as an interesting candidate due to the small but effective interaction network that facilitated FKBP12-FRB interactions and further stabilized the ternary complex.

Biophysical Interpretation of Evolutionary Consequences on the SARS-CoV2 Main Protease through Molecular Dynamics Simulations and Network Topology Analysis.

In this study, we present a combined analysis procedure between atomistic molecular dynamics (MD) simulations and network topology to obtain more understanding on the evolutionary consequences on protein stability and substrate binding of the main protease enzyme of SARS-CoV2. Communicability matrices of the protein residue networks (PRNs) were extracted from MD trajectories of both Mpro enzymes in complex with the nsp8/9 peptide substrate to compare the local communicability within both proteases that would affect the enzyme function, along with biophysical details on global protein conformation, flexibility, and contribution of amino acid side chains to both intramolecular and intermolecular interactions. The analysis displayed the significance of the mutated residue 46 with the highest communicability gain to the binding pocket closure. Interestingly, the mutated residue 134 with the highest communicability loss corresponded to a local structural disruption of the adjacent peptide loop. The enhanced flexibility of the disrupted loop connecting to the catalytic residue Cys145 introduced an extra binding mode that brought the substrate in proximity and could facilitate the reaction. This understanding might provide further help in the drug development strategy against SARS-CoV2 and prove the capability of the combined techniques of MD simulations and network topology analysis as a "reverse" protein engineering tool.

Roles of Tryptophan and Charged Residues on the Polymorphisms of Amyloids Formed by K-Peptides of Hen Egg White Lysozyme Investigated through Molecular Dynamics Simulations.

Atomistic molecular dynamics simulations of amyloid models, consisting of the previously reported STDY-K-peptides and K-peptides from the hen egg white lysozyme (HEWL), were performed to address the effects of charged residues and pH observed in an in vitro study. Simulation results showed that amyloid models with antiparallel configurations possessed greater stability and compactness than those with parallel configurations. Then, peptide chain stretching and ordering were measured through the end-to-end distance and the order parameter, for which the amyloid models consisting of K-peptides and the STDY-K-peptides at pH 2 displayed a higher level of chain stretching and ordering. After that, the molecular mechanics energy decomposition and the radial distribution function (RDF) clearly displayed the importance of Trp62 to the K-peptide and the STDY-K-peptide models at pH 2. Moreover, the results also displayed how the negatively charged Asp52 disrupted the interaction networks and prevented the amyloid formation from STDY-K-peptide at pH 7. Finally, this study provided an insight into the interplay between pH conditions and molecular interactions underlying the formation of amyloid fibrils from short peptides contained within the HEWL. This served as a basis of understanding towards the design of other amyloids for biomaterial applications.

A computational study on the molecular mechanisms of panduratin A as a potential inhibitor on SARS-CoV-2 protein targets.

Panduratin A from Boesebergia rotunda was recently reported as a potent anti-SARS-CoV-2 compound. However, the molecular mechanisms underlying the inhibition by Panduratin A and its target remained unclear. Molecular docking calculations were performed between panduratin A and five important proteins, i.e., main protease (Mpro), papain-like protease (PLpro), receptor binding domain (RBD) of spike proteins, RNA-dependent-RNA-polymerase (RdRp), and 2'-O-methyltransferase (MTase). The estimated binding free energy and the interaction networks extracted from the best docking mode for each complex suggested that MTase was the most probable target for panduratin A inhibition. To further validate the ability of panduratin A to inhibit MTase, molecular dynamics (MD) simulations and binding free energy calculations were performed for panduratin A-MTase complex, in comparison with another MTase complex with sinefungin as a positive control. Chemical features of panduratin A and sinefungin were compared for their contribution in MTase binding. It was found that both molecules could bind to the S-Adenosyl methionine (SAM) binding pocket and prevent the SAM entrance co-substrate, which could eventually halt the function of MTase. Despite a slightly weaker binding free energy, the equilibrated positional binding of panduratin A was found at a closer distance to the active sites. Therefore, this study proposed MTase as a possible target of panduratin A, along with the mechanisms of inhibition, prompting another future in vitro study as a verification.

A molecular dynamics study on the diffusion and imprint ability of spectinomycin under different sizes of aniline oligomers.

Molecularly imprinted polymers (MIP) are the polymers created by molecular imprinting techniques that leave cavities for the specific interactions with a template molecule and have been applied in molecular selectivity tasks. In this study, the molecular dynamics (MD) simulation technique was used to demonstrate that aniline oligomer could be developed as a potential MIP for detection and separation of the spectinomycin drug molecule for gonorrhea treatment. MD simulations were performed for the systems of a spectinomycin within aniline oligomers of different sizes. The mean square displacement (MSD) and the diffusivity calculated from MD simulations showed that the diffusion coefficient was significantly dropped when the length of aniline oligomer was greater than two. The diffusion coefficient of spectinomycin became the lowest within aniline trimers, corresponded to the highest atomic distribution of MIP around the template. Then, the specific cavity in MIP systems with and without spectinomycin was calculated to assess the stability of the cavity created by the template. The volume of a cavity created within the trimer system was closest to the spectinomycin volume and therefore became the optimal oligomer size for further development of MIP.

Molecularly Imprinted Polymer-Amyloid Fibril-Based Electrochemical Biosensor for Ultrasensitive Detection of Tryptophan.

A tryptophan (Trp) sensor was investigated based on electrochemical impedance spectroscopy (EIS) of a molecularly imprinted polymer on a lysozyme amyloid fibril (MIP-AF). The MIP-AF was composed of aniline as a monomer chemically polymerized in the presence of a Trp template molecule onto the AF surface. After extracting the template molecule, the MIP-AF had cavities with a high affinity for the Trp molecules. The obtained MIP-AF demonstrated rapid Trp adsorption and substantial binding capacity (50 µM mg-1). Trp determination was studied using non-Faradaic EIS by drop drying the MIP-AF on the working electrode of a screen-printed electrode. The MIP-AF provided a large linear range (10 pM-80 µM), a low detection limit (8 pM), and high selectivity for Trp determination. Furthermore, the proposed method also indicates that the MIP-AF can be used to determine Trp in real samples such as milk and cancer cell media.

In Silico Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations.

The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase-PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase-PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future.

Molecular dynamics study on the effects of charged amino acid distribution under low pH condition to the unfolding of hen egg white lysozyme and formation of beta strands.

Aggregation of unfolded or misfolded proteins into amyloid fibrils can cause various diseases in humans. However, the fibrils synthesized in vitro can be developed toward useful biomaterials under some physicochemical conditions. In this study, atomistic molecular dynamics simulations were performed to address the mechanism of beta-sheet formation of the unfolded hen egg-white lysozyme (HEWL) under a high temperature and low pH. Simulations of the protonated HEWL at pH 2 and the non-protonated HEWL at pH 7 were performed at the highly elevated temperature of 450 K to accelerate the unfolding, followed by the 333 K temperature to emulate some previous in vitro studies. The simulations showed that HEWL unfolded faster, and higher beta-strand contents were observed at pH 2. In addition, one of the simulation replicas at pH 2 showed that the beta-strand forming sequence was consistent with the 'K-peptide', proposed as the core region for amyloidosis in previous experimental studies. Beta-strand formation mechanisms at the earlier stage of amyloidosis were explained in terms of the radial distribution of the amino acids. The separation between groups of positively charged sidechains from the hydrophobic core corresponded to the clustering of the hydrophobic residues and beta-strand formation.

Nanocomposite of Fullerenes and Natural Rubbers: MARTINI Force Field Molecular Dynamics Simulations.

The mechanical properties of natural rubber (NR) composites depend on many factors, including the filler loading, filler size, filler dispersion, and filler-rubber interfacial interactions. Thus, NR composites with nano-sized fillers have attracted a great deal of attention for improving properties such as stiffness, chemical resistance, and high wear resistance. Here, a coarse-grained (CG) model based on the MARTINI force field version 2.1 has been developed and deployed for simulations of cis-1,4-polyisoprene (cis-PI). The model shows qualitative and quantitative agreement with the experiments and atomistic simulations. Interestingly, only a 0.5% difference with respect to the experimental result of the glass transition temperature (Tg) of the cis-PI in the melts was observed. In addition, the mechanical and thermodynamical properties of the cis-PI-fullerene(C60) composites were investigated. Coarse-grained molecular dynamics (MD) simulations of cis-PI-C60 composites with varying fullerene concentrations (0-32 parts per hundred of rubber; phr) were performed over 200 microseconds. The structural, mechanical, and thermal properties of the composites were determined. The density, bulk modulus, thermal expansion, heat capacity, and Tg of the NR composites were found to increase with increasing C60 concentration. The presence of C60 resulted in a slight increasing of the end-to-end distance and radius of the gyration of the cis-PI chains. The contribution of C60 and cis-PI interfacial interactions led to an enhancement of the bulk moduli of the composites. This model should be helpful in the investigations and design of effective fillers of NR-C60 composites for improving their properties.

Analysis of newly developed fractal-fractional derivative with power law kernel for MHD couple stress fluid in channel embedded in a porous medium.

Fractal-fractional derivative is a new class of fractional derivative with power Law kernel which has many applications in real world problems. This operator is used for the first time in such kind of fluid flow. The big advantage of this operator is that one can formulate models describing much better the systems with memory effects. Furthermore, in real world there are many problems where it is necessary to know that how much information the system carries. To explain the memory in a system fractal-fractional derivatives with power law kernel is analyzed in the present work. Keeping these motivation in mind in the present paper new concept of fractal-fractional derivative for the modeling of couple stress fluid (CSF) with the combined effect of heat and mass transfer have been used. The magnetohydrodynamics (MHD) flow of CSF is taken in channel with porous media in the presence of external pressure. The constant motion of the left plate generates the CSF motion while the right plate is kept stationary. The non-dimensional fractal-fractional model of couple stress fluid in Riemann-Liouville sense with power law is solved numerically by using the implicit finite difference method. The obtained solutions for the present problem have been shown through graphs. The effects of various parameters are shown through graphs on velocity, temperature and concentration fields. The velocity, temperature and concentration profiles of the MHD CSF in channel with porous media decreases for the greater values of both fractional parameter [Formula: see text] and fractal parameter [Formula: see text] respectively. From the graphical results it can be noticed that the fractal-fractional solutions are more general as compared to classical and fractional solutions of CSF motion in channel. Furthermore, the fractal-fractional model of CSF explains good memory effect on the dynamics of couple stress fluid in channel as compared to fractional model of CSF. Finally, the skin friction, Nusselt number and Sherwood number are evaluated and presented in tabular form.

Molecular Mechanisms on the Selectivity Enhancement of Ascorbic Acid, Dopamine, and Uric Acid by Serine Oligomers Decoration on Graphene Oxide: A Molecular Dynamics Study.

The selectivity in the simultaneous detection of ascorbic acid (AA), dopamine (DA), and uric acid (UA) has been an open problem in the biosensing field. Many surface modification methods were carried out for glassy carbon electrodes (GCE), including the use of graphene oxide and amino acids as a selective layer. In this work, molecular dynamics (MD) simulations were performed to investigate the role of serine oligomers on the selectivity of the AA, DA, and UA analytes. Our models consisted of a graphene oxide (GO) sheet under a solvent environment. Serine tetramers were added into the simulation box and were adsorbed on the GO surface. Then, the adsorption of each analyte on the mixed surface was monitored from MD trajectories. It was found that the adsorption of AA was preferred by serine oligomers due to the largest number of hydrogen-bond forming functional groups of AA, causing a 10-fold increase of hydrogen bonds by the tetraserine adsorption layer. UA was the least preferred due to its highest aromaticity. Finally, the role of hydrogen bonds on the electron transfer selectivity of biosensors was discussed with some previous studies. AA radicals received electrons from serine through hydrogen bonds that promoted oxidation reaction and caused the negative shifts and separation of the oxidation potential in experiments, as DA and UA were less affected by serine. Agreement of the in vitro and in silico results could lead to other in silico designs of selective layers to detect other types of analyte molecules.

Unveiling the Fundamental Mechanisms of Graphene Oxide Selectivity on the Ascorbic Acid, Dopamine, and Uric Acid by Density Functional Theory Calculations and Charge Population Analysis.

The selectivity of electrochemical sensors to ascorbic acid (AA), dopamine (DA), and uric acid (UA) remains an open challenge in the field of biosensing. In this study, the selective mechanisms for detecting AA, DA, and UA molecules on the graphene and graphene oxide substrates were illustrated through the charge population analysis from the density functional theory (DFT) calculation results. Our substrate models contained the 1:10 oxygen per carbon ratio of reduced graphene oxide, and the functionalized configurations were selected according to the formation energy. Geometry optimizations were performed for the AA, DA, and UA on the pristine graphene, epoxy-functionalized graphene, and hydroxyl-functionalized graphene at the DFT level with vdW-DF2 corrections. From the calculations, AA was bound to both epoxy and hydroxyl-functionalized GO with relatively low adsorption energy, while DA was adsorbed stronger to the electronegative epoxy groups. The strongest adsorption of UA to both functional groups corresponded to the largest amount of electron transfer through the pi orbitals. Local electron loss created local electric fields that opposed the electron transfer during an oxidation reaction. Our analysis agreed with the results from previous experimental studies and provided insight into other electrode modifications for electrochemical sensing.

Antibacterial Potential of a Novel Peptide from the Consensus Sequence of Dermaseptin Related Peptides Secreted by Agalychnis annae.

BACKGROUND: The consistently increasing reports of bacterial resistance and the reemergence of bacterial epidemics have inspired the health and scientific community to discover new molecules with antibacterial potential continuously. Frog-skin secretions constitute bioactive compounds essential for finding new biopharmaceuticals. The exact antibacterial characterization of dermaseptin related peptides derived from Agalychnis annae, is limited. The resemblance in their conserved and functionally linked genomes indicates an unprecedented opportunity to obtain novel bioactive compounds. OBJECTIVE: In this study, we derived a novel peptide sequence and determined its antibacterial potentials. METHODS: Consensus sequence strategy was used to design the novel and active antibacterial peptide named 'AGAAN' from skin secretions of Agalychnis annae. The in-vitro activities of the novel peptide against some bacterial strains were investigated. Time kill studies, DNA retardation, cytotoxicity, betagalactosidase, and molecular computational studies were conducted. RESULTS: AGAAN inhibited P. aeruginosa, E. faecalis, and S. typhimurium at 20 µM concentration. E. coli and S. aureus were inhibited at 25 µM, and lastly, B. subtilis at 50 µM. Kinetics of inactivation against exponential and stationary growing bacteria was found to be rapid within 1-5 hours of peptide exposure, depending on time and concentration. The peptide displayed weak hemolytic activity between 0.01%-7.31% at the antibacterial concentrations. AGAAN efficiently induced bacterial membrane damage with subsequent cell lysis. The peptide's DNA binding shows that it also targets intracellular DNA by retarding its movement. Our in-silico molecular docking analysis displayed a strong affinity to the bacterial cytoplasmic membrane. CONCLUSION: AGAAN exhibits potential antibacterial properties that could be used to combat bacterial resistance.

Effect of N-terminal modification on the mode of action between the Xyn11A and Xylotetraose.

The purpose of this study was to gain an insight into the effects of mutation-induced binding pocket tilting of the Xyn11A xylanase from Bacillus firmus K-1 in producing a unique hydrolysis characteristic. In this study, the wildtype Xyn11A and its K40L mutant were compared for their hydrolysis patterns on beechwood xylan and xylooligosaccharides of sizes 2 to 6. According to our thin-layer chromatography experiment, the K40L mutant produced a larger amount of xylotetraose leftover than the wildtype. Kinetic determination of the WT and K40L mutant suggested that the higher X4 leftover on TLC was reflected in the decreasing catalytic efficiency (kcat/Km) between enzyme and X4. The mechanisms underlying this efficiency loss were examined through atomistic molecular dynamics (MD) simulations. The MD trajectory analysis showed that the mutation-induced binding pocket tilting resulted in an additional hydrophobic contact between the reducing end of X4 and Trp128. Meanwhile, the interactions between the non-reducing end and the Arg112 residue near the active site became lost, which could decrease the catalytic efficiency. This work suggested that the protein engineering to fine-tune the hydrolysis pattern for some desired xylooligosaccharide products was possible.

Effects of L-serine amino acid functionalization on electronic properties of a graphene plane in comparison with oxygen functionalization.

The effects of L-serine amino acid functionalization on a graphene plane were investigated through density functional theory calculations compared with those of oxygen functionalization. Three systems of 4 × 4 graphene supercells were created and functionalized by an epoxy group, a hydroxyl group, and an L-serine molecule. From the geometry optimization of the system containing a 4 × 4 graphene supercell and an L-serine molecule, it was found that a by-product hydroxyl group was formed by the dissociation of the -OH from the parental -COOH group and two covalent bonds forming at a couple of adjacent atoms on the graphene plane. The adsorption energy of the L-serine functionalization was weaker than that of the epoxy functionalization but was stronger than that of the hydroxyl functionalization. Electronic properties of this new L-serine functionalization were similar to epoxy functionalization at low functionalization density, as the Dirac cone was preserved with shifted wave vector due to the double sp3 vacancies. The C2v type of two-fold symmetry was observed through the local density of states (LDOS) and the gamma-point HOMO electron density analysis. However, the improved binding surface area of serine-functionalized graphene was observed, as four polar groups emerged from a single functionalization. Therefore, serine functionalization is a promising way to improve the properties of graphene-based electrodes. Graphical abstract.

Effects of oligolignol sizes and binding modes on a GH11 xylanase inhibition revealed by molecular modeling techniques.

Lignin and phenolic compounds have been shown as the main recalcitrance for biomass decomposition, as they inhibit a number of lignocellulose-degrading enzymes. Understanding the inhibition mechanisms and energetic competitions with the native substrate is essential for the development of lignin resistive enzymes. In this study, atomistic detail of the size-dependent effects and binding modes of monomeric coniferyl alcohol, dimeric oligolignol, and tetrameric oligolignol made from coniferyl alcohols on the GH11 xylanase from Bacillus firmus strain K-1 was investigated by using molecular docking and atomistic molecular dynamics (MD) simulations. From the MD simulation results on the docked conformation of oligolignol binding within the "Cleft" and the "N-terminal," changes were observed both for protein conformations and positional binding of ligands, as binding with "Thumb" regions was found for all oligolignin models. Moreover, the uniquely stable "N-terminal" binding of the coniferyl alcohol monomer had no effect on the highly fluctuated Thumb region, showing no sign of inhibitory effect, and was in good agreement with recent studies. However, the inhibitory effect of oligolignols was size dependent, as the estimated binding energy of the tetrameric oligolignol became stronger than that of the xylohexaose substrate, and the important binding residues were identified for future protein engineering attempts to enhance the lignin resistivity of GH11. Graphical Abstract Size-dependent binding modes of coniferyl alcohol monomers (upper panels) and the dimers (lower panels). Uniquely stable "N-terminal" binding of the monomer is shown to have no effect on the binding pocket, and hence no sign of inhibition, which was in good agreement with some recent studies.

Molecular dynamics study of natural rubber-fullerene composites: connecting microscopic properties to macroscopic behavior.

Macroscopic and microscopic properties of fullerene (C60)-cis-polyisoprene (cis-PI) composites at varying fullerene concentrations were investigated using atomistic molecular dynamics (MD) simulations over microsecond time scales. Results show that the introduction of fullerenes into a polymer matrix increases density, bulk modulus and heat capacity while thermal expansivity decreases. The presence of fullerenes slowed the diffusion of both C60 and cis-PI. Moreover, increasing fullerene concentration results in ordering of the cis-PI chains at the cis-PI-fullerene interfaces and shrinking of bulk PI regions. Free energy calculations of fullerene dimerization suggest that fullerenes disperse at low and aggregate at high fullerene concentrations. Our multi-scaled analysis approach demonstrates the role of 'ordered' regions adjacent to the interface between cis-PI and fullerene in controlling the level of order and mobility of the cis-PI chains. The relationship between the microscopic behavior and the changes in mechanical and thermal properties are discussed. Our study is beneficial for further studies and development of advanced rubber technology for novel, cost-effective, material with very high stiffness and thermal endurance with optimizing conditions of filler contents.