From GROMACS to LAMMPS: GRO2LAM : A converter for molecular dynamics software

Investigating the effect of welding tool length on mechanical strength of welded metallic matrix by molecular dynamics simulation

The welding process and the properties of welding instruments may improve the mechanical performance of an item. One of these properties is the length of the welding tool. This approach has a substantial effect on the mechanical strength of the metallic matrix. The current study used molecular dynamics modeling and LAMMPS software to evaluate the effect of welding tool length on the mechanical properties of a welded Cu-Ag metallic matrix. This simulation makes use of the Lennard-Jones potential function and the embedded atom model. First, the equilibrium phase of modeled samples was verified by changing the computation of kinetic and total energies. Next, the mechanical properties of the welded matrix were studied using the stated Young's modulus and ultimate strength. The stress-strain curve of samples demonstrated that the mechanical strength of atomic samples increased as the length of the welding tool (penetration depth) increased. Numerically, by increasing the tool penetration depth of Fe tools from 2 Å to 8 Å, Young's modulus and ultimate strength of the matrixes sample increase from 34.360 GPa to 1390.84 MPa to 38.44 GPa and 1510 MPa, respectively. This suggested that the length of the Fe welding tool significantly affected the mechanical properties of the welded metallic matrix. The longer the length of Fe welding tools, the more particles were involved, and consequently, more bonds were formed among the particles. Bonding among the particles caused changes in mechanical properties, such as greater ultimate strength. This method can optimize mechanical structures and be useful in various industries.

Boundary Lubrication Performance of Polyelectrolyte-Surfactant Complexes on Biomimetic Surfaces

Aqueous mixtures of oppositely charged polyelectrolytes and surfactants are useful in many industrial applications, such as shampoos and hair conditioners. In this work, we investigate the friction between biomimetic hair surfaces in the presence of adsorbed complexes formed from cationic polyelectrolytes and anionic surfactants in an aqueous solution. We apply nonequilibrium molecular dynamics (NEMD) simulations using the coarse-grained MARTINI model. We first developed new MARTINI parameters for cationic guar gum (CGG), a functionalized, plant-derived polysaccharide. The complexation of CGG and the anionic surfactant sodium dodecyl sulfate (SDS) on virgin and chemically damaged biomimetic hair surfaces was studied using a sequential adsorption approach. We then carried out squeeze-out and sliding NEMD simulations to assess the boundary lubrication performance of the CGG-SDS complex compressed between two hair surfaces. At low pressure, we observe a synergistic friction behavior for the CGG-SDS complex, which gives lower shear stress than either pure CGG or SDS. Here, friction is dominated by viscous dissipation in an interfacial layer comprising SDS and water. At higher pressures, which are probably beyond those usually experienced during hair manipulation, SDS and water are squeezed out, and friction increases due to interdigitation. The outcomes of this work are expected to be beneficial to fine-tune and screen sustainable hair care formulations to provide low friction and therefore a smooth feel and reduced entanglement.

Effects of π-π Stacking on Shale Gas Adsorption and Transport in Nanopores

The π-π interaction is a prevalent driving force in the formation of various organic porous media, including the shale matrix. The configuration of π-π stacking in the shale matrix significantly influences the properties of shale gas and plays a crucial role in understanding and exploiting gas resources. In this research, we investigate the impact of different π-π stacking configurations on the adsorption and transport of shale gas within the nanopores of the shale matrix. To achieve this, we construct kerogen nanopores using π-π stacked columns with varying stacking configurations, such as offset/parallel stacking types and different orientations of the stacked columns. Through molecular dynamics simulations, we examined the adsorption and transport of methane within these nanopores. Our findings reveal that methane exhibits stronger adsorption in smoother nanopores, with this adsorption remaining unaffected by the nanoflow. We observe a heterogeneous distribution of the 2D adsorption free energy, which correlates with the specific π-π stacking configurations. Additionally, we introduce the concept of "directional roughness" to describe the surface characteristics, finding that the nanoflow flux increases as the roughness decreases. This research contributes to the understanding of shale gas behavior in the shale matrix and provides insights into nanoflow properties in other porous materials containing π-π stackings.

Computational insight into stability-enhanced systems of anthocyanin with protein/peptide

Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of plants native to South and Central America. However, these pigments are unstable under conditions of varying pH, heat, etc., which limits their potential applications. One method for preserving the stability of anthocyanins is through encapsulation using proteins or peptides. Nevertheless, the complex and diverse structure of these molecules, as well as the limitation of experimental technologies, have hindered a comprehensive understanding of the encapsulation processes and the mechanisms by which stability is enhanced. To address these challenges, computational methods, such as molecular docking and molecular dynamics simulation have been used to study the binding affinity and dynamics of interactions between proteins/peptides and anthocyanins. This review summarizes the mechanisms of interaction between these systems, based on computational approaches, and highlights the role of proteins and peptides in the stability enhancement of anthocyanins. It also discusses the current limitations of these methods and suggests possible solutions.

PyL3dMD: Python LAMMPS 3D molecular descriptors package

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.

Ligand-based virtual screening, molecular dynamics, and biological evaluation of repurposed drugs as inhibitors of Trypanosoma cruzi proteasome

Chagas disease is a well-known Neglected Tropical Disease, mostly endemic in continental Latin America, but that has spread to North America and Europe. Unfortunately, current treatments against such disease are ineffective and produce known and undesirable side effects. To find novel effective drug candidates to treat Chagas disease, we uniquely explore the Trypanosoma cruzi proteasome as a recent biological target and, also, apply drug repurposing through different computational methodologies. For this, we initially applied protein homology modeling to build a robust model of proteasome β4/β5 subunits, since there is no crystallographic structure of this target. Then, we used it on a drug repurposing via a virtual screening campaign starting with more than 8,000 drugs and including the methodologies: ligand-based similarity, toxicity predictions, and molecular docking. Three drugs were selected concerning their favorable interactions at the protein binding site and subsequently submitted to molecular dynamics simulations, which allowed us to elucidate their behavior and compare such theoretical results with experimental ones, obtained in biological assays also described in this paper.Communicated by Ramaswamy H. Sarma.

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

The antiviral efficacies of small-molecule inhibitors against respiratory syncytial virus based on the F protein

OBJECTIVES

Respiratory syncytial virus (RSV) infection is one of the three most common causes of death in the infants, pre-schoolers, immunocompromised patients and elderly individuals due to many complications and lack of specific treatment. During RSV infection, the fusion protein (F protein) mediates the fusion of the virus envelope with the host cell membrane. Therefore, the F protein is an effective target for viral inhibition.

METHODS

We identified potential small-molecule inhibitors against RSV-F protein for the treatment of RSV infection using virtual screening and molecular dynamics (MD) simulations. The CCK8 assay was used to determine the cytotoxicity and quantitative RT-PCR and indirect fluorescence assay (IFA) were used to determine the viral replication and RSV-induced inflammation in vitro. An RSV-infected mouse model was established, and viral replication was assayed using real-time quantitative PCR and IFA. Virus-induced complications were also examined using histopathological analysis, airway resistance and the levels of IL-1β, IL-6 and TNF-α.

RESULTS

The top three potential inhibitors against the RSV-F protein were screened from the FDA-approved drug database. Z65, Z85 and Z74 significantly inhibited viral replication and RSV-induced inflammation. They also significantly alleviated RSV infection and RSV-induced complications in vivo. Z65 and Z85 had no cytotoxicity and better anti-RSV effects than Z74.

CONCLUSIONS

Z65 and Z85 may be suitable candidates for the treatment of RSV and serve as the basis for the development of new drugs.

Valence energy correction for electron reactive force field

Reactive force fields (ReaxFF) are a classical method to describe material properties based on a bond-order formalism, that allows bond dissociation and consequently investigations of reactive systems. Semiclassical treatment of electrons was introduced within ReaxFF simulations, better known as electron reactive force fields (eReaxFF), to explicitly treat electrons as spherical Gaussian waves. In the original version of eReaxFF, the electrons and electron-holes can lead to changes in both the bond energy and the Coulomb energy of the system. In the present study, the method was modified to allow an electron to modify the valence energy, therefore, permitting that the electron's presence modifies the three-body interactions, affecting the angle among three atoms. When a reaction path involving electron transfer is more sensitive to the geometric configuration of the molecules, corrections in the angular structure in the presence of electrons become more relevant; in this case, bond dissociation may not be enough to describe a reaction path. Consequently, the application of the extended eReaxFF method developed in this work should provide an improved description of a reaction path. As a first demonstration this semiclassical force field was parametrized for hydrogen and oxygen interactions, including water and water's ions. With the modified methodology both the overall accuracy of the force field but also the description of the angles within the molecules in presence of electrons could be improved.

Potential colchicine binding site inhibitors unraveled by virtual screening, molecular dynamics and MM/PBSA

Microtubules have been widely studied in recent decades as an important pharmacological target for the treatment of cancer especially due to its key role in the mitosis process. Among the constituents of the microtubules, αβ-tubulin dimers stand out in view of their four distinct interaction sites, including the so-called colchicine binding site (CBS) - a promising target for the development of new tubulin modulators. When compared to other tubulin sites, targeting the CBS is advantageous because this site is able to host ligands with lower molecular volume and lipophilicity, thus reducing the chances of entailing the phenomenon of multiple drug resistance (MDR) - one of the main reasons of failure in chemotherapy. However, colchicine, the first ligand ever discovered with affinity towards the CBS, despite modulating the action of microtubules, has shown toxicity in clinical studies. Therefore, in order to expand the known chemical space of scaffolds capable of interacting with CBS and to design non-toxic colchicine binding site inhibitors, we conducted a robust virtual screening pipeline. This has been rigorously validated and consisted of ligand- and structure-based methodologies, which allowed us to select four promising CBS inhibitors called tubLCQF1-4. These four compounds were also evaluated with long trajectories molecular dynamics simulations and respective results were used for the theoretical determination of the free energy released in the formation of the complexes, using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) methodology.

Effect of water nanoconfinement on the dynamic properties of paramagnetic colloidal complexes

The anomalous behavior of confined water at the nanoscale has remarkable implications in a number of nanotechnological applications. In this work, we analyze the effect of water self-diffusion on the dynamic properties of a solvated gadolinium-based paramagnetic complex, typically used for contrast enhancement in magnetic resonance imaging. In particular, we examine the effect of silica-based nanostructures on water behavior in the proximity of the paramagnetic complex via atomistic simulations, and interpret the resulting tumbling dynamics in the light of the local solvent modification based on the Lipari-Szabo formalism and of the fractional Stokes-Einstein relation. It is found that the local water confinement induces an increased "stiffness" on the outer sphere of the paramagnetic complex, which eventually reduces its tumbling properties. These model predictions are found to explain well the relaxivity enhancement observed experimentally by confining paramagnetic complexes into porous nanoconstructs, and thus offer mechanistic guidelines to design improved contrast agents for imaging applications.

Revisiting the Proposition of Binding Pockets and Bioactive Poses for GSK-3β Allosteric Modulators Addressed to Neurodegenerative Diseases

Glycogen synthase kinase-3 beta (GSK-3β) is an enzyme pertinently linked to neurodegenerative diseases since it is associated with the regulation of key neuropathological features in the central nervous system. Among the different kinds of inhibitors of this kinase, the allosteric ones stand out due to their selective and subtle modulation, lowering the chance of producing side effects. The mechanism of GSK-3β allosteric modulators may be considered still vague in terms of elucidating a well-defined binding pocket and a bioactive pose for them. In this context, we propose to reinvestigate and reinforce such knowledge by the application of an extensive set of in silico methodologies, such as cavity detection, ligand 3D shape analysis and docking (with robust validation of corresponding protocols), and molecular dynamics. The results here obtained were consensually consistent in furnishing new structural data, in particular by providing a solid bioactive pose of one of the most representative GSK-3β allosteric modulators. We further applied this to the prospect for new compounds by ligand-based virtual screening and analyzed the potential of the two obtained virtual hits by quantum chemical calculations. All potential hits achieved will be subsequently tested by in vitro assays in order to validate our approaches as well as to unveil novel chemical entities as GSK-3β allosteric modulators.

In Vitro and In Silico Anti-Arboviral Activities of Dihalogenated Phenolic Derivates of L-Tyrosine

Despite the serious public health problem represented by the diseases caused by dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) viruses, there are still no specific licensed antivirals available for their treatment. Here, we examined the potential anti-arbovirus activity of ten di-halogenated compounds derived from L-tyrosine with modifications in amine and carboxyl groups. The activity of compounds on VERO cell line infection and the possible mechanism of action of the most promising compounds were evaluated. Finally, molecular docking between the compounds and viral and cellular proteins was evaluated in silico with Autodock Vina®, and the molecular dynamic with Gromacs®. Only two compounds (TDC-2M-ME and TDB-2M-ME) inhibited both ZIKV and CHIKV. Within the possible mechanism, in CHIKV, the two compounds decreased the number of genome copies and in the pre-treatment strategy the infectious viral particles. In the ZIKV model, only TDB-2M-ME inhibited the viral protein and demonstrate a virucidal effect. Moreover, in the U937 cell line infected with CHIKV, both compounds inhibited the viral protein and TDB-2M-ME inhibited the viral genome too. Finally, the in silico results showed a favorable binding energy between the compounds and the helicases of both viral models, the NSP3 of CHIKV and cellular proteins DDC and β2 adrenoreceptor.

Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world, with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by 23 November 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structural proteins (NSP). Three non-structural proteins, main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp), are believed to have a crucial role in replication of the virus. We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics rolapitant and ondansetron for Mpro; labetalol and levomefolic acid for PLpro; and leucal and antifungal natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The results of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins-Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.

Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5′-ends of viral genomic RNA and sub genomic RNAs, to escape the host’s innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5’,5’-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5’-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.

Communicated by Ramaswamy H. Sarma.

Mechanistic correlation between water infiltration and framework hydrophilicity in MFI zeolites

Hydrophobic zeolites are nanoporous materials that are attracting an increasing interest, especially for catalysis, desalination, energy storage and biomedical applications. Nevertheless, a more profound understanding and control of water infiltration in their nanopores is still desirable to rationally design zeolite-based materials with tailored properties. In this work, both atomistic simulations and previous experimental data are employed to investigate water infiltration in hydrophobic MFI zeolites with different concentration of hydrophilic defects. Results show that limited concentrations of defects (e.g. 1%) induce a change in the shape of infiltration isotherms (from type-V to type-I), which denotes a sharp passage from typical hydrophobic to hydrophilic behavior. A correlation parametrized on both energy and geometric characteristics of the zeolite (infiltration model) is then adopted to interpolate the infiltration isotherms data by means of a limited number of physically-meaningful parameters. Finally, the infiltration model is combined with the water-zeolite interaction energy computed by simulations to correlate the water intrusion mechanism with the atomistic details of the zeolite crystal, such as defects concentration, distribution and hydrophilicity. The suggested methodology may allow a faster (more than one order of magnitude) and more systematic preliminary computational screening of innovative zeolite-based materials for energy storage, desalination and biomedical purposes.