Molecular Dynamics Technique Research Articles

Artificial Intelligence Approach for the Discovery of Autoantigen Recognition By B-Cell Lymphomas

Artificial Intelligence approach for the discovery of autoantigen recognition by B-cell lymphomas Introduction With a common origin from mature B-cells, B-cell lymphomas express a unique clonal surface immunoglobulin (Ig) that may transmit survival signals autonomously or following binding to cognate antigens. An increasing number of potential autoantigen targets is described for several mature B-cell lymphomas, however, the discovery of novel targets represents a very complex and expensive task. Thanks to the development of artificial intelligence (AI) tools such as AlphaFold and advances in natural language processing methods such as Large Language Models, the study of proteins has benefited, making it possible to accelerate studies of interactions between proteins. In this context, we explored the training of AI models to predict autoantigen recognition of lymphoma-derived Ig from linear protein information. Methods First, 45 lymphoma-derived Ig were sequenced, synthesized as recombinant proteins, and probed onto human proteome arrays generating 370,000 antigens-antibodies interactions. Next, statistical methods were designed and implemented to reduce noise and filter the autoantigen-antibody interactions to be processed. Subsequently, sequence-based methods were explored to implement and validate predictive models of autoantigen-antibody interaction intensity. Lastly, sequence similarity network strategies were analyzed to identify preference relationships between antibodies and autoantigens. Results and Discussion From the 370,000 interactions, designed filters allowed to reduce the noise, generating a total of 270,000 valid interactions, which were used to train predictive models. Large language model methods, amino acid coding strategies via physicochemical properties, and spatial transformation techniques through Fourier transforms were explored as methods for the numerical representation of autoantigen and antibody sequences. Concatenation strategies and linear and non-linear combinations were explored to represent the autoantigen-antibody interaction complexes. More than 1000 predictive models were explored. The best performances were obtained by applying the pre-trained bepler and esm1b models, concatenation strategies, and using Random Forest algorithms as a training strategy. In addition, cross-validation methods with k-fold (k=10) were applied to prevent overfitting. The best results achieve a performance of 0.9 of Pearsons' coefficient and an MSE of 0.08. Alternatively, training strategies based on deep learning architectures such as CNN or GCN were used, although they presented similar results to those achieved by the selected method. Finally, the model was validated using molecular dynamics techniques, studying the affinity of interactions in a selected random sample, with a correlation between the predicted affinity results and the affinity of the interaction complexes measured by molecular dynamics. Conclusions The large language model methods explored for the training of predictive models have been combined with sequence similarity network methods for constructing autoantigen and antibody interaction networks. Interaction networks helped to identify patterns of antibody recognition preferences. In future work, the incorporation of unsupervised learning algorithms, enrichment analysis, and simulation of interactions via the generated predictive model is proposed to build an efficient pattern detection strategy to facilitate the discovery of autoantigen interaction of lymphoma derived Ig.

Influence of silicon nanocone on cell membrane self-sealing capabilities for targeted drug delivery—Computer simulation study

Efficient and non-invasive techniques of cargo delivery to biological cells are the focus of biomedical research because of their great potential importance for targeted drug therapy. Therefore, much effort is being made to study the characteristics of using nano-based biocompatible materials as systems that can facilitate this task while ensuring appropriate self-sealing of the cell membrane. Here, we study the effects of indentation and withdrawal of nanocone on phospholipid membrane by applying steered molecular dynamics (SMD) technique. Our results show that the withdrawal process directly depends on the initial position of the nanocone. The average force and work are considerably more significant in case of the withdrawal starting from a larger depth. This result is attributed to stronger hydrophobic interactions between the nanocone and lipid tails of the membrane molecules. Furthermore, when the indenter was started from the lower initial depth, the number of lipids removed from the membrane was several times smaller than the deeper indentation. The choice of the least invasive method for nanostructure-assisted drug delivery is crucial for possible applications in medicine. Therefore, the results presented in this work might be helpful in efficient and safe drug delivery with nanomaterials.

Effect of the bare and functionalized single-wall carbon nanotubes on inhibition of asphaltene molecules aggregation: A molecular dynamic simulation

A comprehensive study has been carried out by utilizing the molecular dynamics technique in order to investigate the behavior of the N-(1-hexylheptyl)-N'-(2-phenylpropanoicacid)-perylene-3,4,9,10-tetracarboxylicbisimide (PAP) molecules in the n-heptane/toluene solution as well as the role of the bare and functionalized single-wall carbon nanotubes (SWCNTs) with the carboxyl groups (-COOH) on the aggregation of PAP molecules. It was found that the CNTs benefit two mechanism of steric hindrance and adsorbing the PAP molecules to suppress the affinity of PAP molecules to association. The results ascertain that the constant amount of the carboxyl groups acts more efficiently in restricting the growth of aggregate size if they are distributed on the surface of a larger CNT. Both of the increased nonbonding interactions between the functionalized CNTs and the PAP molecules, and the number of formed hydrogen bonds between them clearly proved the efficiency of the –COOH groups in improving the stability of PAP molecules. The strength of the adsorption free energies revealed that the PAP molecule shows more tendency to be adsorbed on the surface of CNT modified with carboxyl groups. Increasing the dosage of the –COOH groups on the surface of the CNTs with constant dimension causes an increment in the PAP molecules' solvent accessible surface area (SASA) value, indicating enhanced stability of the PAP molecules. Finally, the results would facilitate future studies on manipulating the asphaltene precipitation in the oil industry.

Phonon non-equilibrium effects on interface thermal resistance between graphene and substrates

Accurate measurement and deep understanding of interfacial thermal transport through interfaces where two-dimensional (2D) materials are involved are crucial for efficient thermal management in nanodevices. However, there is still a large discrepancy in the measurement on the interfacial thermal resistance (ITR) at interfaces between 2D materials and substrates. In this study, two kinds of molecular dynamics techniques are utilized to investigate the ITR of graphene/silicon interfaces under steady-state and transient conditions. Our results indicate that, in both conditions, phonon non-equilibrium phenomena could lead to an overestimation of the interfacial thermal resistance. In the steady state, the overestimation of ITR arises from an exaggerated interfacial temperature difference when using specific vibrational modes as indicators of the sample temperature. In the transient regime, it is attributed to the extended thermal relaxation time of flexural acoustic modes in graphene caused by weak phonon coupling when recording the temperature evolution to extract the ITR. These findings provide a comprehensive understanding of thermal transport across the graphene/silicon interface, aiding in the optimization of graphene-based nanodevices. The study also underlines the necessity of considering the specific experimental setup and physical processes involved when interpreting ITR data obtained from measurements.

On the Wavelength-Dependent Photochemistry of the Atmospheric Molecule CF3COCl.

The wavelength control of photochemistry usually results from ultrafast dynamics following the excitation of different electronic states. Here, we investigate the CF3COCl molecule, exhibiting wavelength-dependent photochemistry both via (i) depositing increasing internal energy into a single state and (ii) populating different electronic states. We reveal the mechanism behind the photon-energy dependence by combining nonadiabatic ab initio molecular dynamics techniques with the velocity map imaging experiment. We describe a consecutive mechanism of photodissociation where an immediate release of Cl taking place in an excited electronic state is followed by a slower ground-state dissociation of the CO fragment. The CO release is subject to an activation barrier and is controlled by excess internal energy via the excitation wavelength. Therefore, a selective release of CO along with Cl can be achieved. The mechanism is fully supported by both the measured kinetic energy distributions and anisotropies of the angular distributions. Interestingly, the kinetic energy of the released Cl atom is sensitively modified by accounting for spin-orbit coupling. Given the atmospheric importance of CF3COCl, we discuss the consequences of our findings for atmospheric photochemistry.

In-silico studies of Brassica oleracea active compounds and their role in thyroid peroxidase activity

Cabbage, a leafy vegetable that is widely consumed across the globe, holds a significant place within the Brassica family. For almost a century, its potential anti-thyroid effects have captured attention. The presence of compounds such as thiocyanate and goitrin in cabbage has been extensively investigated for their ability to impede sodium-iodide symporter and thyroid peroxidase (TPO) activities. The present study is focused on uncovering the active constituents in cabbage that could interact with TPO, while also examining their stability under cooking temperatures. Employing molecular docking and molecular dynamic simulation techniques, we quantified the binding strength of phytochemicals present in cabbage with the target. Out of the 60 compounds identified in cabbage leaves, only 18 exhibited docking scores surpassing those of the commercially available anti-thyroid drug, methimazole. These chosen compounds were studied for binding free energy and pharmacokinetic properties. A specific compound, gamma-Terpinene, classified as a monoterpene, emerged as noteworthy due to its alignment with all criteria and the highest observed binding free energy compared to others. Furthermore, we explored the stability of gamma-Terpinene at 373.15K (cooking temperature) and observed its susceptibility to degradation. This might contribute to the relatively diminished anti-thyroid effects of cabbage when consumed in cooked form. Consequently, our findings suggest that the consumption of cooked cabbage could be more conducive to maintaining normal thyroid function, as opposed to its raw counterpart. Communicated by Ramaswamy H. Sarma

Studies on the interaction of 2,4-dibromophenol with human hemoglobin using multi-spectroscopic, molecular docking and molecular dynamics techniques

2,4-Dibromophenol (DBP) has several industrial applications, including as a wood preservative and flame retardant. This study investigated the interaction between DBP and human hemoglobin (Hb) using spectroscopic, molecular docking and molecular dynamic techniques. The UV-visible spectra showed ground-state complex formation between DBP and Hb. Fluorescence studies revealed that DBP binding caused significant quenching of Hb fluorescence by the static quenching mechanism. The binding of DBP to Hb is a spontaneous process that involves van der Waals forces and hydrogen bonds. There is one DBP binding site on each Hb molecule that is located at the α1β2 interface of Hb. DBP binding did not alter the microenvironment of tyrosine and tryptophan residues in Hb. Circular dichroism studies revealed that DBP increased the α-helical content of Hb. The intrinsic esterase activity of Hb was inhibited by DBP in a concentration-dependent manner. Molecular docking showed that DBP binds to Hb via hydrogen bonds, hydrophobic, van der Waals and π – π interactions. Molecular dynamics simulation confirmed that the Hb-DBP complex is stable. Overall, the results of this study clearly show that DBP induces structural changes and interferes with the function of Hb. This can have important implications for human health. Communicated by Ramaswamy H. Sarma

Assessing the interaction between hemoglobin and the receptor binding domain of SARS-CoV-2 spike protein through MARTINI coarse-grained molecular dynamics

The emergence of different coronavirus-related diseases in the 2000's (SARS, MERS, and Covid-19) warrants the need of a complete understanding of the pathological, biological, and biochemical behavior of this class of pathogens. Great attention has been paid to the SARS-CoV-2 Spike protein, and its interaction with the human ACE2 has been thoroughly investigated. Recent findings suggested that the SARS-CoV-2 components may interact with different human proteins, and hemoglobin has very recently been demonstrated as a potential target for the Spike protein. Here we have investigated the interaction between either adult or fetal hemoglobin and the receptor binding domain of the Spike protein at molecular level through advanced molecular dynamics techniques and proposed rational binding modes and energy estimations. Our results agree with biochemical data previously reported in literature. We also demonstrated that co-incubation of pulmonary epithelial cells with hemoglobin strongly reduces the pro-inflammatory effects exerted by the concomitant administration of Spike protein.

Luteolin is a potential inhibitor of COVID-19: An in silico analysis.

The severe respiratory syndrome 2019 novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread explosively, raising global health concerns. Luteolin shows antiviral properties, but its effect on SARS-CoV-2 and the associated mechanisms are not elucidated. We used network pharmacology, molecular docking and molecular dynamics to provide potential molecular support of luteolin (3,4,5,7-tetrahydroxyflavone) (LUT) against COVID-19. We employed network pharmacology, molecular docking, and molecular dynamics techniques to investigate how LUT affected COVID-19. Several databases were queried to determine potential target proteins related to LUT and COVID-19. Protein-protein interaction network was constructed, and core targets were filtered by degree value. Following that, functional enrichment was conducted. Molecular docking was utilized to ensure LUT was compatible with core target proteins. Finally, molecular dynamics was used to analyze the effects of the LUT on the optimal hub target. A total of 64 potential target genes for treating COVID-19 were identified, of which albumin, RAC-alpha serine/threonine-protein kinase, caspase-3, epidermal growth factor receptor, heat shock protein HSP 90-alpha, and mitogen-activated protein kinase 1 might be the most promising. In addition, molecular docking results showed that LUT could interact with SARS-CoV-2 major protease 3CL. LUT can bind to the active sites of 3CL protease and mitogen-activated protein kinase 1, showing an anti-SARS-CoV-2 potential.

The influence of cone structure and wettability on vaporization nucleation

Surface wettability and surface structure are critical factors that affect vaporization nucleation. However, their coupling effects on nucleation have yet to be thoroughly studied. This study employs molecular dynamics (MD) techniques to investigate the impact of nanostructures and walls with varying wettability on vaporization nucleation. Theoretical analysis verification demonstrate that increasing the hydrophobic ratio of the hybrid wall or adding a cone structure to the wall reduces the vaporization nucleation time.Previous studies did not separate the “surface area” and “cone height” factors when examining the relationship between cone surface structure and boiling. In this work, we analyzed the factors that affect vaporization nucleation. We found that increased surface area shortens nucleation time, and when the surface area is the same, higher cone heights lead to shorter nucleation times. Additionally, we examined the coupling effect of surface structure and wettability on nucleation. The results indicate that an increased proportion of hydrophobic walls with identical surface structures enhances the likelihood of nucleation. Furthermore, hydrophilic wall-hydrophobic cones with the same hydrophilic-hydrophobic ratio are more prone to nucleation than hydrophobic wall-hydrophilic cones. These findings provide insights into the design of nanostructures to enhance heat transfer involving boiling.

Applications of Molecular Dynamics Simulations in Drug Discovery.

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Role of crystallographic orientation in material behaviour under nanoindentation: Molecular Dynamics study

Abstract The mechanical properties of materials can be analysed under deformation conditions by various laboratory tests. However, such experimental investigations become extremely complicated and often even impossible at the lower length scales where the arrangement of the atomic planes is considered. In this case, computational materials science is a robust alternative to extend the capabilities of laboratory tests. Therefore, the molecular dynamics technique was selected in the current work to evaluate the role of the local grain crystallographic orientation during nanoindentation testing. A pure aluminium sample was selected as a case study. For the sake of clarity, two distinctively different crystallographic orientations cube {100}<001> and hard {110}<011> were investigated in a set of arrangements: monocrystalline, bicrystalline, and polycrystalline. The influence of the substrate and the neighbouring grains on the material response to local deformation was evaluated. The research used two types of indenters: spherical and sharp-tipped. Results obtained were analysed with respect to the arrangement of atoms and load-displacement curves. This research proved that the role of crystallographic orientation in material behaviour under nanoindentation should not be neglected during the interpretation of data from this test.

Toward Quantum Chemical Free Energy Simulations of Platinum Nanoparticles on Titania Support.

Platinum nanoparticles (Pt-NPs) supported on titania surfaces are costly but indispensable heterogeneous catalysts because of their highly effective and selective catalytic properties. Therefore, it is vital to understand their physicochemical processes during catalysis to optimize their use and to further develop better catalysts. However, simulating these dynamic processes is challenging due to the need for a reliable quantum chemical method to describe chemical bond breaking and bond formation during the processes but, at the same time, fast enough to sample a large number of configurations required to compute the corresponding free energy surfaces. Density functional theory (DFT) is often used to explore Pt-NPs; nonetheless, it is usually limited to some minimum-energy reaction pathways on static potential energy surfaces because of its high computational cost. We report here a combination of the density functional tight binding (DFTB) method as a fast but reliable approximation to DFT, the steered molecular dynamics (SMD) technique, and the Jarzynski equality to construct free energy surfaces of the temperature-dependent diffusion and growth of platinum particles on a titania surface. In particular, we present the parametrization for Pt-X (X = Pt, Ti, or O) interactions in the framework of the second-order DFTB method, using a previous parametrization for titania as a basis. The optimized parameter set was used to simulate the surface diffusion of a single platinum atom (Pt1) and the growth of Pt6 from Pt5 and Pt1 on the rutile (110) surface at three different temperatures (T = 400, 600, 800 K). The free energy profile was constructed by using over a hundred SMD trajectories for each process. We found that increasing the temperature has a minimal effect on the formation free energy; nevertheless, it significantly reduces the free energy barrier of Pt atom migration on the TiO2 surface and the transition state (TS) of its deposition. In a concluding remark, the methodology opens the pathway to quantum chemical free energy simulations of Pt-NPs' temperature-dependent growth and other transformation processes on the titania support.

Molecular docking and dynamics of a dextranase derived from Penicillium cyclopium CICC-4022

This study aimed to investigate the recognition mechanism of dextranase (PC-Edex) produced by Penicillium cyclopium CICC-4022 on dextran. Whole genome information of P. cyclopium CICC-4022 was obtained through genome sequencing technology. The coding information of PC-Edex was determined based on the annotation of the protein-coding genes using protein databases. The three-dimensional structure of PC-Edex was obtained via homology modelling. The active site and binding free energy between PC-Edex and dextran were calculated by molecular docking and molecular dynamics techniques. The results showed that the total sequence length and GC content of P. cyclopium CICC-4022 were 29,710,801 bp and 47.02 %, respectively. The annotation of protein-encoding genes showed that P. cyclopium CICC-4022 is highly active and has many carbohydrate transport and metabolic functions, and most of its proteases are glycolytic anhydrases. Furthermore, the gene encoding PC-Edex was successfully annotated. Molecular dynamics simulations indicated that van der Waals interaction was the main driving force of interaction. Residues Ile114, Asp115, Tyr332, Lys344, and Gln403 significantly promoted the binding between dextran and PC-Edex. In summary, this study explored the active site catalyzed by PC-Edex based on the binding pattern of PC-Edex and dextran. Therefore, this study provides genomic information on dextranase and data supporting the rational modification and enhancement of PC-Edex.

Structural, chemical reactivity, topological, electronic and spectroscopic (Raman, SERS and UV-Vis) and antiviral investigation of Furo [3,2-c] pyridine-2-carbaldehyde

The current work is concerned with the theoretical discernment of adsorption processes between the Furo [3,2-c] pyridine-2-carbaldehyde and bimetallic clusters (Cu-Ag, Cu-Au and Cu-Ni). The ESP maps are used to detect the existence of reactive sites in the molecule. The HOMO/LUMO energy differential was used to calculate the energy band gap. The ELF maps and AIM (Atoms in Molecules) analysis are used to determine the molecule's topological properties. The non-covalent interactions present in the compound are determined using RDG, IRI maps and VdW surfaces. NLO analysis provides the information on molecule's optical activity. The stability of the compound is examined using the molecular dynamics technique. To explore the adsorption of bimetallic clusters on the compound, the adsorption energy and theoretical SERS spectra are employed. The absorption behaviour of the molecule is determined by the UV-Vis spectrum. The pharmacological investigation shows that the compound is physiologically active. The compound's antiviral efficacy against viruses such as Machupo virus and Marburg virus is demonstrated via protein-ligand docking.

Dynamic Structure of Organic Compounds in Solution According to NMR Data and Quantum Mechanical Calculations: IV. Benzamide

To study the structure and dynamics of nitrogen-containing compounds, NMR parameters with directly involved nitrogen can provide valuable structure information. However, this information can only be obtained using15N-enriched compounds due to low natural abundance of 15N and extremely short relaxation of 14N nuclei. For synthesis of these compounds from 15N-ammonium salts, 15N-enriched benzamides often used as intermediates. In the present work, we study the dynamic structure of benzamide caused by two independent factors: hindered internal rotation of the NH2 group around the C(O)-N bond and the amide group as a whole relative to the benzene ring. This deeper knowledge of mechanism and parameters of these processes in amides is important for meaningful interpretation and prediction of the biological activity of aromatic amides in living systems, the strength and conformation of its supramolecular complexes with lanthanide and actinide ions. Double enriched [2H5, 15N]benzamide was synthesized to avoid unwanted superposition of intense aromatic multiplet on the amide signals in the 1H NMR spectra. In the 1H spectrum of this compound observed only intense signals of amide protons, which allowed accurate quantitative characterization of the parameters of the dynamic processes under study. The experimental data obtained is in good agreement with our results of simulation by quantum molecular dynamics techniques.

Numerical assessment of transition in cutting mode and its effect on roughness creation

Ductile-to-brittle transition in cutting mode is an important phenomenon in the ultra-precision machining and surface finish of brittle materials. Using a newly-developed coarse-grained molecular dynamics technique, we systematically explore the effect of depth of cut on the cutting mode and the evolution of roughness. Our simulations demonstrate that the transitional depth of cut (TDoC) model accurately predicts the material removal mechanisms, which are affected by both the material’s properties and the cutting depth. When the cutting depth increases, the operating mechanism shifts from shearing-induced serrated chip formation to cleavage-cracking chip breakage. The RMS roughness study demonstrates that the roughness is a direct outcome of the chip formation mechanism and is, in general, higher when chip breakage occurs.

Molecular dynamics modeling of thermodiffusion in solids with charged defects using uranium dioxide as the case study

Although thermodiffusion is generally much less efficient than diffusion for chemical species transport, this phenomenon can sometimes have a significant impact on natural or technological systems. More specifically, oxygen thermodiffusion is reported as a key mechanism for nuclear fuel evolution during high power transients in nuclear plant operation.To contribute to our understanding and characterization of this phenomenon, oxygen thermodiffusion in UO2 is simulated by molecular dynamics (MD) in order to evaluate the oxygen heat of transport. Specific formulae for the heat flux are derived to take into account the non-pairwise embedded atom model (EAM) term of the empirical potential used. No electronic defects are considered and a specific strategy is built to address this flaw, including the way to ensure electroneutrality with a background charge compensating for the deviation from stoichiometry. A specific thermodynamic model is designed, including the effect of the background charge and each point defect contribution.Five MD techniques are tested and compared, among which the Green-Kubo formalism and the imposed electric field give similar results. The results are interpreted in terms of the defect heats of transport whose combination yields the complete oxygen heat of transport, which is a function of the oxygen potential. The calculated ranges of the heats of transport for the oxygen vacancy and interstitial are [0.20,0.86] eV and [-3.75,-2.60] eV, respectively.

A theoretical study of a light-powered artificial molecular motor in dichloromethane solution under equilibrium conditions

A light-powered molecular rotor, featuring a photo-switchable (E-Z) azobenzene unit in dichloromethane solution, is investigated by an integrated computational approach allowing a quantitative reproduction of UV/VIS absorption spectra detected in laboratory under equilibrium conditions. We have addressed the study of such a pseudorotaxane in two different photostationary isomers characterizing supramolecular contacts and the molecular orbitals involved in the absorption processes. Moreover, for the stable threaded device we also combine classical Molecular Dynamics technique and Density Functional Theory algorithms with the aim of addressing the conformational shaping and perturbed electronic properties driven by thermal fluctuation under equilibrium conditions and solvation effects.

Cheminformatics and machine learning approaches for repurposing anti-viral compounds against monkeypox virus thymidylate kinase.

One of the emerging epidemic concerns is Monkeypox disease which is spreading globally. This disease is caused by the monkeypox virus (MPXV), with an increasing global incidence with an outbreak in 2022. One of the novel targets for monkeypox disease is thymidylate kinase, which is involved in pyrimidine metabolism. In this study, docking-based virtual screening and molecular dynamics techniques were employed in addition to the machine learning (ML) model to investigate the potential anti-viral natural small compounds to inhibit thymidylate kinase of MPXV. Several potential hits were identified through high-throughput virtual screening, and further top three candidates were selected, which ranked using the ML model. These three compounds were then examined under molecular dynamics simulation and MM/GBSA-binding free energy analysis. Among these, Chlorhexidine HCl showed high potential for binding to the thymidylate kinase with stable and consistent conformation with RMSD < 0.3nm. The MM/GBSA analysis also showed the minimum binding free energy (ΔGTOTAL) of -62.41kcal/mol for this compound. Overall, this study used structure-based drug design complemented by machine learning-guided ligand-based drug design to screen potential hit compounds from the anti-viral natural compound database.