Normal Mode Analysis Research Articles

Introducing electrostatic stimulation to normal mode analysis.

Normal Mode Analysis (NMA) has proven to be a powerful tool to understand the macromolecular movement of protein complexes. While less precise than an all-atom molecular dynamics simulation, it allows to predict movements on timescales that are not yet accessible to MD simulations on a routine basis. In this work, we build on the previous successful implementation EnCoM to include electrostatic interactions in the NMA analysis. The motivation behind this step is to study the conformational rearrangements of voltage-gated ion channels that are sensitive to the electric field over the membrane. We show here preliminary results on the extension of the inter-residue interactions to include electrostatics and first results on the effect of an electric field on the normal modes of a voltage-gated potassium (Kv) channel.

Instability mechanism of shear-layered fluid in the presence of a floating elastic plate

In this study, linear stability analysis in the two-dimensional Cartesian coordinate system is used to analyze the flow dynamics underneath a large floating elastic plate over a slippery surface in the presence of external shear. For both viscous and inviscid flows, the Orr–Sommerfeld equation and the Rayleigh equation, respectively, are obtained using normal mode analysis. The Chebyshev collocation method is used to solve both equations numerically. Analysis of the growth rate and energy distributions is performed to understand the flow instability at various flow and structural parameters. The study reveals that the flow below the floating elastic plate dampens for larger uniform mass and structural rigidity in the viscous fluid. On the other hand, there is no effect of structural rigidity on the flow stability in the case of inviscid flow. However, the plate of larger uniform mass stabilizes the growing disturbance generated due to the externally imposed shear at the surface of the plate. The present study is analogous to the simple geographical model of external shearing on the surface of a large ice cover zone caused by atmospheric air. This study can be extended to understand the flow stability below other large floating structures like a floating island and a floating airport.

Encoding dynamical information of protein from normal mode analysis as multigraphs for function prediction by graph neural networks and highlighting functionally-critical residues.

Dynamics of proteins are crucial for its ability to perform its functions, but are often overlooked in the plethora of protein function prediction methods. However, the need for faster screening over sequence-structure-function spaces is ever growing due to the increase in number of known peptide sequences and structures without function annotations. Machine learning methods are commonly implemented to meet the latter need to moderate success, with graph neural networks (GNN) gaining traction in recent years due to its rather intuitive mapping to protein structures. On the other hand, encoding the dynamics of proteins is yet a critical outstanding issue. In our attempt to close this gap, we encode the dynamical information of modal shapes and frequencies, along with the protein contact map, into multigraphs to aid graph neural networks in their prediction. This reduction of mode shapes into graphs is done by calculating the correlation between pairwise residue movements, then weight-averaging the correlation across multiple mode shapes, taking into consideration the equipartition theorem. The anisotropic network model (ANM) and the torsional network model (TNM) are implemented for modal analysis, and the two network models are compared within our framework. The method is tested on various datasets with different sequence similarity cutoffs, and the results compared with existing methods. Furthermore, we are able to extract from our model the dynamically-critical residues, such as hydrogen-binding and ligand-binding sites, for each of the protein's functions. As such, the proposed model provides insight to critical mutation points, facilitating further investigation and understanding of the protein.

Spectral signatures of excess-proton waiting and transfer-path dynamics.

Signatures of solvated excess protons in infrared difference absorption spectra, such as the continuum band between the water bend and stretch bands, have been experimentally known for a long time and have recently been used to analyze protonation dynamics in photoactive proteins. However, the theoretical basis for linking spectral signatures with the microscopic proton-transfer mechanism so far relied on normal-mode analysis. We analyze the excess-proton dynamics in ab initio molecular-dynamics simulations of aqueous hydrochloric acid solutions by trajectory-decomposition techniques. The continuum band in the 2000 cm−1 to 3000 cm−1 range is shown to be due to normal-mode oscillations of temporary H3O+ complexes. An additional prominent peak at 400 cm−1 reports on the coupling of excess-proton motion to the relative vibrations of the two flanking water molecules. The actual proton transfer between two water molecules, which for large water separations involves crossing of a barrier and thus is not a normal mode, is characterized by two time scales: Firstly, the waiting time for transfer to occur in the range of 200 fs to 300 fs, which leads to a broad weak shoulder around 100 cm−1, consistent with our experimental THz spectra. Secondly, the mean duration of a transfer event of about 14 fs, which produces a rather well-defined spectral contribution around 1200 cm−1 and agrees in location and width with previous experimental mid-infrared spectra.

Flex-LZerD: Protein docking with extremely large conformational changes.

Large flexibility of proteins as they interact is key to their function in many systems, such as with calmodulin, importin, or interleukin receptors. To understand these mechanisms at the molecular level, it is necessary to consider the 3D atomic structures involved. When these have not been determined, protein docking can generate useful structure models. However, traditional docking typically considers flexibility limited to small movements on the order of ∼2 Å root mean square deviation (RMSD), or otherwise to small structures. These methods account for such minor backbone flexibility via conventional normal mode or other sampling, but modeling ordered proteins of arbitrary size which undergo large-scale conformational changes, on the order of 10 Å RMSD and above, requires different treatment to be generally feasible. Here, we present Flex-LZerD, a computational method for assembling such complexes with large-scale conformational changes on the order of 10 Å RMSD and above. Via an initial partial-assembly multidomain docking and an iterative anisotropic normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes. Also yielded are full-atom trajectories taking the input flexible protein from its initial conformation to its docked conformation, which could shed light on potential intermediate states. AlphaFold and other current deep learning-based methods predict static structure models, but do not explicitly address flexibility. AlphaFold is further limited to proteins, while here protein-nucleic acid complexes are also considered. Flex-LZerD successfully modeled protein-protein complexes for 5 of 9 unbound test targets. On a further unbound-bound study of 15 protein-protein complexes and 8 protein-nucleic acid complexes, Flex-LZerD likewise successfully modeled 9 and 8, respectively. Flex-LZerD is thus a step toward the explicit treatment of flexibility and multiple states that is the future of structure modeling.

Effect of Finite Ion Larmor Radius (FLR) Corrections on Thermal Instability of Rotating Radiative Porous Astrophysical Plasma in Interstellar Medium (ISM)

Infinite homogeneous plasma’s thermal instability has been studied in relation to finite ion Larmor radius (FLR) corrections, rotation, and porosity, as well as the impacts of radiative heat-loss function and thermal conductivity. With the aid of a normal mode analysis framework and the necessary difficulty-appropriate linearized perturbation equations, a universal dispersion relation is investigated. For the propagation of transverse waves, this dispersion relationship further condenses for rotation axes parallel to and at right angles to the magnetic field. It is proven that the presence of rotation, porosity, thermal conductivity, and radiative heat-loss function altered the thermal instability criterion. To show how different parameters affect the rate at which the thermal instability grows, numerical calculations have been carried out. We discover that rotation, FLR corrections, and medium porosity stabilised the growth rate of the thermal system in the transverse mode of propagation. The conclusion of this research states that the rotation, porosity, and FLR corrections have an impact on the configuration of dense molecular clouds and star formation.

Acoustic response in the transition region to transverse oscillations in a solar coronal loop

Context. Magnetohydrodynamic (MHD) waves play an important role in the dynamics and heating of the solar corona. Transverse (Alfvénic) oscillations of loops commonly occur in response to solar eruptions and are mostly studied in isolation. However, acoustic coupling has been shown to be readily observable in the form of propagating intensity variations at the loop footpoints. Aims. We extend the modelling of wave coupling between a transverse loop oscillation and slow magnetoacoustic waves in a structured loop to include a lower atmosphere. Methods. We achieve this with combined analytical modelling and fully non-linear MHD simulations. Results. Transverse loop oscillations result in the excitation of propagating slow waves from the top of the transition region and the lower boundary. The rate of excitation for the upward propagating waves at the lower boundary is smaller than for waves at the top of the transition region due to the reduced local sound speed. Additionally, slow waves are found to propagate downwards from the transition region, which reflect at the lower boundary and interfere with the upward propagating waves. Resonances are present in the normal mode analysis but these do not appear in the simulations. Due to the presence of the transition region, additional longitudinal harmonics lead to a narrower slow wave profile. The slow wave field is anti-symmetric in the direction of wave polarisation, which highlights the importance that the loop orientation has on the observability of these waves. The ponderomotive effect must be accounted for when interpreting intensity oscillations. Evidence is found for an additional short-period oscillation, which is likely a hybrid mode.

Gravity effect in a piezo-thermoelastic diffusive medium with dual-phase-lag model

The model of generalized thermoelasticity, with the dual-phase-lag theory (DPL), is applied to study the influence of gravity on a piezo-thermoelastic diffusive medium. Normal mode analysis is used to obtain the exact expressions for different physical quantities. The derived expressions are computed numerically and the results are presented in graphical form. Comparisons are made with the results predicted by the Lord–Shulman theory (LS) and the DPL model in the presence and absence of gravity.

Influence of memory-dependent derivative on generalized thermoelastic rotating porous solid via three-phase-lag model

This paper is concerned with the influence of memory-dependent heat transport law on rotating thermoelastic medium with voids via three-phase-lag. The entire pervious medium is rotating with a invariant angular haste, where the bounding airplane is subordinated to a thermal shock and is free of tractions. By employing the normal mode analysis, the exact expressions for the displacement components, stresses, temperature distribution and change in volume fraction field have been depicted graphically in the presence and the absence of gyration and memory-dependent outgrowth. The effect of the voiding is also bandied in the literature review.

Revisiting the Ramachandran plot based on statistical analysis of static and dynamic characteristics of protein structures

Ramachandran plots, which describe protein structures by plotting the dihedral angle pairs of the backbone on a two-dimensional plane, have played an important role in structural biology over the past few decades. However, despite continued discovery of new protein structures to date, the Ramachandran plot is still constructed by only a small number of data points, and further it cannot reflect the steric information of proteins. Here, we investigated the secondary structure of proteins in terms of static and dynamic characteristics. As for static feature, the Ramachandran plot was revisited for the dataset consisting of 9,148 non-redundant high-resolution protein structures released in the protein data bank until April 1, 2022. By calculating amino acid propensities, it was found that the proportion of secondary structures with respect to residue depth is directly related to their hydrophobicity. As for dynamic feature, normal mode analysis (NMA) based on an elastic network model (ENM) was carried out for the dataset using our KOSMOS web server (http://bioengineering.skku.ac.kr/kosmos/). All ENM-based NMA results were stored in the KOSMOS database, allowing researchers to use them in various ways. In this process, it was commonly found that high B-factors appeared at the edge of the alpha helix region, which was elucidated by introducing residue depth. In addition, by investigating the change in dihedral angle, it was possible to quantitatively survey the contribution of structural change of protein on the Ramachandran plot. In conclusion, our statistical analysis of protein characteristics will provide insight into a range of protein structural studies.

Comparative Surface Electrostatics and Normal Mode Analysis of High and Low Pathogenic H7N7 Avian Influenza Viruses.

Influenza A viruses are rarely symptomatic in wild birds, while representing a higher threat to poultry and mammals, where they can cause a variety of symptoms, including death. H5 and H7 subtypes of influenza viruses are of particular interest because of their pathogenic potential and reported capacity to spread from poultry to mammals, including humans. The identification of molecular fingerprints for pathogenicity can help surveillance and early warning systems, which are crucial to prevention and protection from such potentially pandemic agents. In the past decade, comparative analysis of the surface features of hemagglutinin, the main protein antigen in influenza viruses, identified electrostatic fingerprints in the evolution and spreading of H5 and H9 subtypes. Electrostatic variation among viruses from avian or mammalian hosts was also associated with host jump. Recent findings of fingerprints associated with low and highly pathogenic H5N1 viruses, obtained by means of comparative electrostatics and normal modes analysis, prompted us to check whether such fingerprints can also be found in the H7 subtype. Indeed, evidence presented in this work showed that also in H7N7, hemagglutinin proteins from low and highly pathogenic strains present differences in surface electrostatics, while no meaningful variation was found in normal modes.

Immunoinformatic-Based Multi-Epitope Vaccine Design for Co-Infection of Mycobacterium tuberculosis and SARS-CoV-2.

(1) Background: Many co-infections of Mycobacterium tuberculosis (MTB) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have emerged since the occurrence of the SARS-CoV-2 pandemic. This study aims to design an effective preventive multi-epitope vaccine against the co-infection of MTB and SARS-CoV-2. (2) Methods: The three selected proteins (spike protein, diacylglycerol acyltransferase, and low molecular weight T-cell antigen TB8.4) were predicted using bioinformatics, and 16 epitopes with the highest ranks (10 helper T lymphocyte epitopes, 2 CD8+ T lymphocytes epitopes, and 4 B-cell epitopes) were selected and assembled into the candidate vaccine referred to as S7D5L4. The toxicity, sensitization, stability, solubility, antigenicity, and immunogenicity of the S7D5L4 vaccine were evaluated using bioinformatics tools. Subsequently, toll-like receptor 4 docking simulation and discontinuous B-cell epitope prediction were performed. Immune simulation and codon optimization were carried out using immunoinformatics and molecular biology tools. (3) Results: The S7D5L4 vaccine showed good physical properties, such as solubility, stability, non-sensitization, and non-toxicity. This vaccine had excellent antigenicity and immunogenicity and could successfully simulate immune responses in silico. Furthermore, the normal mode analysis of the S7D5L4 vaccine and toll-like receptor 4 docking simulation demonstrated that the vaccine had docking potential and a stable reaction. (4) Conclusions: The S7D5L4 vaccine designed to fight against the co-infection of MTB and SARS-CoV-2 may be safe and effective. The protective efficacy of this promising vaccine should be further verified using in vitro and in vivo experiments.

INFLUENCE OF LOCAL THERMAL NON-EQUILIBRIUM ON THE STABILITY OF NANOFLUID FLOW IN AN INCLINED CHANNEL FILLED WITH POROUS MEDIUM

The effect of local thermal nonequilibrium on the stability of nanofluid flow in an inclined channel filled with a porous medium is numerically investigated. The Buongiorno model for nanofluid and Darcy-Brinkman model for flow in a porous medium are utilized, along with a three-field model for temperature, with each field representing the fluid, particle, and solid-matrix phases individually. The Chebyshev spectral collocation approach is used to determine the solution of the eigenvalue problem, which is obtained for perturbed states using a normal mode analysis. The impacts of various local thermal nonequilibrium parameters, the critical Rayleigh number, and associated wavenumber are displayed through graphs. It is worth noting that the LTNE parameters have a major impact on convective instability. Also, the dynamics of the flow field, behavior of temperature, and volume fraction are presented through streamlines, isotherms, and isonanoconcentration at the critical level.

INSTABILITY INVESTIGATION OF THERMO-BIOCONVECTION OF OXYTACTIC MICROORGANISM IN JEFFREY NANOLIQUID WITH EFFECTS OF INTERNAL HEAT SOURCE

The intent of this article is to investigate the influence of internal heat source on the stability of a suspension containing oxytactic microorganisms in a shallow horizontal porous fluid layer saturated by Jeffrey nanoliquid. The Jeffrey-Buongiorno model governs the nanofluid bioconvection flow. Normal mode analysis is utilized, and the principle of exchange of stability is invoked due to the absence of opposing agencies. The stability criteria is defined in terms of critical thermal Rayleigh number as a function of various flow governing parameters by using the weighted residual Galerkin method. It is perceived that the increment of uniform heat supply and the presence of oxytactic microorganism cells as well as the nanoparticles enhances heat transfer and constitutes an unstable system that hastens bioconvection. The nanoparticle Lewis number is found to have a dual impact on the system stability that relies on the nanoparticle Rayleigh number and exhibits destabilizing nature for top-heavy nanoparticle concentration. It is also observed that the Jeffrey parameter produces nonoscillatory instability in the system.

THE EFFECT OF LOCAL THERMAL NON-EQUILIBRIUM AND MAGNETIC FIELD ON THE STABILITY IN A VERTICAL CHANNEL FILLED WITH NANOFLUID

In this investigation, the influence of local thermal non-equilibrium (LTNE) on the onset of convection in a channel occupied with nanofluid was examined. The flow took place in the presence of a transverse magnetic field. The Buongiorno and two-field models (each independently signifying the fluid and particle phases) were used for the nanofluid and energy equation, respectively. A normal mode analysis was applied to obtain the eigenvalue problem for the disturbed state, which was solved using the Chebyshev spectral collocation technique. The effects of the governing parameters on the Rayleigh number and corresponding wavenumber are presented graphically. It was noticed that the concentration Rayleigh number, inter-phase heat transfer parameter, and modified diffusivity ratio had a destabilizing effect, while the modified thermal capacity ratio, thermal diffusivity ratio, Lewis number, and modified particle density increment had a stabilizing effect on the system.

LINEAR CONVECTIVE STABILITY IN AN INCLINED CHANNEL FILLED WITH A NANOFLUID-SATURATED POROUS MEDIUM

In this study, convective stability of nanofluid flow in an inclined porous channel is being numerically investigated. The effects of Brownian motion and thermophoresis are accounted for in the nanofluid model. In addition, the flow in the porous region governs Brinkman's equation. A normal mode analysis is used to get the eigenvalue problem for the perturbed state, which is then solved using the Chebyshev spectral collocation approach. The influence of various governing parameters, the critical Rayleigh number, and associated wavenumber are graphically displayed.

Design of a multi-epitope vaccine using HA and M1 proteins from influenza and S, E, and M proteins from SARS-CoV-2 by in silico tools

Among various infections, acute respiratory tract infections (ARTIs) have the highest incidence and mortality in the world. These infections are caused by various microorganisms such as the influenza and SARS-CoV-2 viruses. The potential effect of influenza alongside COVID-19 on mortality, morbidity, and health-service capacity is a main concern, although, recently, little is understood about the interaction between these two pathogenic respiratory viruses. Thus, the creation and development of effective vaccines are urgently needed to stop the spread of these diseases. In this study, we created a novel multi-epitope vaccine to protect against SARS-CoV-2 and influenza infections. For usage in vaccine construction, the HA and M1 proteins from the influenza virus and the S, E, and M proteins from the SARS-CoV-2 virus were examined. Using various bioinformatics techniques, the discovery of B and T cell epitopes, energy minimization, prediction of Interferon-gamma response, allergenicity, and toxicity were carried out. The most effective peptides were chosen via molecular docking. To create a multi-epitope vaccine, the chosen epitopes were mixed with the appropriate linkers and adjuvant. The I-TASSER server created the 3D model of the vaccine candidate. Optimization and evaluation of this model were investigated by Ramachandran maps and the Swiss-pdb viewer server. The obtained results showed that the designed vaccine is non-allergenic, and non-toxic with high stability and immunogenicity. The remarkable stability of the final free vaccines and in complex with TLR3, TLR4, TLR5, and TLR8 was confirmed by experiments using normal mode analysis. High amounts of immunoglobulins, T-cell responses, and INF-γ secretions were strikingly visible in the immune simulation. Overall, the new multi-epitope vaccine is expected to be a suitable vaccine against influenza and SARS-CoV-2 infections.

Physicochemical and toxicological studies of some commonly used triazine-based herbicides; In-silico approach

Pesticides play a significant role in modern agriculture and public health by protecting crops, preventing the spread of diseases, and ensuring food safety. However, the use of pesticides also raises concerns due to their potential negative consequences on human health and the ecosystem. This study has concentrated on commonly used triazine-based herbicides (atraton, atrazine, prometon, secbumeton, terbuthylazine, and trietazine) due to their unfavorable side effects, which include endocrine disruption, teratogenicity, embryogenicity, carcinogenicity, and hypoplasia of the adrenal cortex. Our investigation encompasses a thorough examination of the physicochemical, spectral, biological, and toxicological properties of these triazine-based herbicides. Density functional theory (DFT) and time-dependent DFT along with the B3LYP/6-311+G (d, p) basis set were used to enumerate the geometry, chemical, and spectral properties. To evaluate the chemical reactivity of triazine compounds, the HOMO-LUMO gap is calculated. FT-IR and UV spectroscopy have been performed to identify functional groups, determine bond lengths, and investigate molecular conformations of triazine derivatives. In addition, binding affinities and modes against human estrogen-related and cancer-causing receptor proteins (PDB IDs: 4MNF and 2E2R, respectively) have been observed utilizing molecular docking and non-bonding interactions. We selected both proteins that are associated with abnormal cell growth and malignancy, particularly breast cancer and melanoma. Normal mode analysis (NMA) was performed to get the deformability and interactions of protein and amino acid residues, respectively. Pharmacokinetics and biological parameters were accomplished to anticipate their toxicological properties. Terbuthylazine and trietazine have the highest Gibbs free energies, and atrazine has the highest binding affinity for both receptor proteins. All triazine derivatives exhibit nephrotoxicity, carcinogenicity, hematotoxicity, hepatotoxicity, and CYP2C9 inhibitory properties. Therefore, the goal of our inquiry was to gain a deeper comprehension of the risks associated with exposure to triazine-based herbicides, which would be useful for the subsequent investigation. In addition, our research will raise public awareness of the negative impacts of triazine-based herbicides.

Transverse thermal instability of partially ionized radiative plasma with impact of neutral collisions and finite electron inertia effect in ISM

The impact of neutral friction, finite electron inertia and radiative heat-loss function on the thermal instability of viscous, partially-ionized plasma has been explored integrating the impacts of finite electrical resistivity and thermal conductivity. Normal mode analysis scheme is used to derive universal dispersion relation by means of the related linearized perturbation equations of the difficulty and a tailored thermal situation of instability is explored. It is finding that the thermal condition of instability is modified owing the occurrence of radiative heat-loss function, thermal conductivity, finite electron inertia and neutral particle. For transverse mode of propagation, we discover that the condition of thermal instability depends on the finite electron inertia, magnetic field strength, radiative heat-loss function, thermal conductivity, neutral particle and finite electrical resistivity, but independent viscosity of two-components. From the graphs, we explored that the temperature dependent heat-loss function, thermal conductivity, neutral collision frequency and viscosity of two-components demonstrates stabilizing effect, while finite electron inertia and finite electrical resistivity demonstrates destabilizing effect. Our results help’s to understand the process of molecular cloud formation in ISM.

Chemical reaction, Dufour and Soret effects on the stability of magnetohydrodynamic blood flow conveying magnetic nanoparticle in presence of thermal radiation: A biomedical application

Nowadays ferrofluids (magnetic nanofluids) are at the center of many researches because of their major biomedical applications such as drug delivery and cancer treatment. The effects of chemical reaction, temperature gradient induced mass transfer and concentration gradient induced heat transfer on the stability of ferrofluid flow are of great importance. This paper deals with a stability analysis of a ferrofluid composed of blood as base fluid and magnetic nanoparticles. The study integrates the effects of chemical reactions, the effects of mass transfer (Soret effect), the effects of heat transfer (Dufour effect) and the effects of the Buoyancy force. The flow is exposed to a magnetic field and thermal radiation. A system of eigenvalue equations governing the evolution of disturbances is derived by assuming a normal mode analysis. This system of equations is then solved numerically by the method of collocation. It appears from this study that the addition of nanoparticles to the blood increases its inertia, which dampens the amplitude of the disturbances and stabilizes the flow. The Casson parameter affects the stability of the flow by increasing the amplitude of the disturbances, which reflects its destabilizing effect. It appears from this study that taking into account the non-Newtonian nature of blood is very important when modeling the dynamics of the system because it shows more important and very different results than when blood is treated as a Newtonian fluid. The chemical reaction between the fluid and the nanoparticles leads to the redistribution of disturbances within the flow, which amplifies the instabilities and reflects the destabilizing character of the chemical reaction. On the other hand, temperature gradient induced mass transfer effects and concentration gradient induced heat transfer effects play an essential role on the stability of the flow because they attenuate the amplitude of the disturbances in the flow. The Darcy number exhibits a stabilizing effect on the flow. It appears from this analysis that the porosity of the medium increases the contact surface between the fluid and the nanoparticles. Buoyancy forces, thermal radiation parameter and wave number contribute to the stability of the flow. The magnetic field through the Lorentz force decreases the kinetic energy of the flow, which dissipates the disturbances and thus reflects the stabilizing character of the magnetic field. It should be noted that heat and mass transfer on magnetohydrodynamic flows through porous media taking into consideration the effect of chemical reaction appears in many natural and artificial transport processes in several branches of science and engineering applications. This phenomenon plays an important role in the chemical industry, power and cooling industry for drying, chemical vapor deposition on surfaces, cooling of nuclear reactors and petroleum industry. The effects of thermal radiation, mass and heat transfer are used in many situations in biomedical engineering and aerospace engineering.