Molecular Dynamics Simulations Of Proteins Research Articles

Evaluation of the mechanism of Sinomenii Caulis in treating ulcerative colitis based on network pharmacology and molecular docking.

Studies have indicated that Sinomenii Caulis (SC) has several physiological activities, such as anti-inflammatory, anti-cancer, immunosuppression, and so on. SC is currently widely used in the treatment of rheumatoid arthritis, skin disease, and other diseases. However, the mechanism of SC in the treatment of ulcerative colitis (UC) remains unclear. To predict the active components of SC and determine the mechanism of SC on UC. Active components and targets of SC were screened and obtained by TCMSP, PharmMapper, and CTD databases. The target genes of UC were searched from GEO (GSE9452), and DisGeNET databases. Based on the String database, Cytoscape 3.7.2 software, and David 6.7 database, we analyzed the relationship between SC active components and UC potential targets or pathways. Finally, identification of SC targets in anti-UC by molecular docking. GROMACS software was used to perform molecular dynamics simulations of protein and compound complexes and to perform free energy calculations. Six main active components, 61 potential anti-UC gene targets, and the top 5 targets with degree value are IL6, TNF, IL1β, CASP3, and SRC. According to GO enrichment analysis, the vascular endothelial growth factor receptor and vascular endothelial growth factor stimulus may be relevant biological processes implicated in the treatment of UC by SC. The KEGG pathway analysis result was mainly associated with the IL-17, AGE-RAGE, and TNF signaling pathways. Based on molecular docking results, beta-sitosterol, 16-epi-Isositsirikine, Sinomenine, and Stepholidine are strongly bound to the main targets. Molecular dynamics simulation results showed that IL1B/beta-sitosterol and TNF/16-epi-Isositsirikine binding was more stable. SC can play a therapeutic role in UC through multiple components, targets, and pathways. The specific mechanism of action needs to be further explored.

Enhancing MD simulations: ASGARD's automated analysis for GROMACS.

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.

Supporting the CIF file format of proteins in molecular dynamics simulations

Molecular dynamics (MD) simulations can capture the dynamic behavior of proteins in full atomic detail and at very fine temporal resolution, so they have become an important tool in the study of protein dynamics. To date, several MD packages are widely used. An MD simulation starts from an initial structure that is generally taken from the Protein Data Bank (PDB). Until 2014, the PDB format was the standard file format for protein structures. However, there are certain intrinsic limitations in the PDB format, such as the storage of structural information in a fixed-width format, which is an issue for very large protein complexes. Therefore, the CIF (crystallographic information framework) format has been proposed, which is characterized by its superior expansibility. To our knowledge, the current mainstream MD packages support only the PDB format but do not support the CIF format directly. In this study, we modified the source code of one of the MD packages, GROMACS, which enables it to support CIF-formatted structure files as input and subsequently generate molecular topology files. This work simplifies the preprocessing of large protein complexes for MD simulations.

The Energetic Origins of Pi-Pi Contacts in Proteins.

Accurate potential energy models of proteins must describe the many different types of noncovalent interactions that contribute to a protein's stability and structure. Pi-pi contacts are ubiquitous structural motifs in all proteins, occurring between aromatic and nonaromatic residues and play a nontrivial role in protein folding and in the formation of biomolecular condensates. Guided by a geometric criterion for isolating pi-pi contacts from classical molecular dynamics simulations of proteins, we use quantum mechanical energy decomposition analysis to determine the molecular interactions that stabilize different pi-pi contact motifs. We find that neutral pi-pi interactions in proteins are dominated by Pauli repulsion and London dispersion rather than repulsive quadrupole electrostatics, which is central to the textbook Hunter-Sanders model. This results in a notable lack of variability in the interaction profiles of neutral pi-pi contacts even with extreme changes in the dielectric medium, explaining the prevalence of pi-stacked arrangements in and between proteins. We also find interactions involving pi-containing anions and cations to be extremely malleable, interacting like neutral pi-pi contacts in polar media and like typical ion-pi interactions in nonpolar environments. Like-charged pairs such as arginine-arginine contacts are particularly sensitive to the polarity of their immediate surroundings and exhibit canonical pi-pi stacking behavior only if the interaction is mediated by environmental effects, such as aqueous solvation.

Lipid Bicelles in the Study of Biomembrane Characteristics.

Simulations of lipid membranes typically make use of periodic boundary conditions to mimic macroscopically sized membranes and allow for comparison to experiments performed e.g. on planar lipid membranes or on unilamellar lipid vesicles. However, the lateral periodicity partly suppresses membrane fluctuations or membrane remodeling, processes that are of particular importance in the study of asymmetric membranes-i.e. membranes with integral or associated proteins and/or asymmetric lipid compositions. Here, we devised a simple albeit powerful lipid bicelle model system that (i) displays similar structural, dynamical, and mechanical properties compared to infinite periodic lipid membrane systems and allows (ii) for the study of asymmetric lipid bilayer systems and (iii) the unperturbed formation of local spontaneous curvature induced by lipids or proteins in molecular dynamics simulations. In addition, the system is characterized by largely unbiased thermal fluctuations as opposed to standard bilayer systems. Application of the bicelle system for an asymmetric lipid composition resembling the plasma membrane reveals that the cholesterol density for a tension-free plasma membrane with a vanishing spontaneous curvature is larger by 28% within the extracellular leaflet compared to the cytosolic leaflet.

Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation.

We apply moving root-mean-square deviation (mRMSD), which does not require a reference structure, as a method for analyzing protein dynamics. This method can be used to calculate the root-mean-square deviation (RMSD) of structure between two specified time points and to analyze protein dynamics behavior through time series analysis. We applied this method to the Trp-cage trajectory calculated by the Anton supercomputer and found that it shows regions of stable states as well as the conventional RMSD. In addition, we extracted a characteristic structure in which the side chains of Asp1 and Arg16 form hydrogen bonds near the most stable structure of the Trp-cage. We also determined that ≥20 ns is an appropriate time interval to investigate protein dynamics using mRMSD. Applying this method to NuG2 protein, we found that mRMSD can be used to detect regions of metastable states in addition to the stable state. This method can be applied to molecular dynamics simulations of proteins whose stable structures are unknown.

A Transfer Free Energy Based Implicit Solvent Model for Protein Simulations in Solvent Mixtures: Urea-Induced Denaturation as a Case Study.

We developed a method for implicit solvent molecular dynamics simulations of proteins in solvent mixtures (model with implicit solvation thermodynamics, MIST). The MIST method introduces experimental group transfer free energies to the generalized Born formulation for generating molecular trajectories without the need for developing rigorous explicit-solvent force fields for multicomponent solutions. As a test case, we studied the urea-induced denaturation of the Trp-cage miniprotein in water. We demonstrate that our method allows efficient exploration of the conformational space of the protein in only a few hundreds of nanoseconds of all-atom unbiased simulations. Furthermore, selective implementation of the transfer free energies of specific peptide groups, backbone, and side chains enables us to decouple their specific energetic contributions to the conformational changes of the protein. The approach herein developed can readily be extended to the investigation of complex matrices as well as to the characterization of protein aggregation. The MIST method is implemented in Plumed (ver. 2.8) as a separate module called SASA.

Analysis of Molecular Dynamics Simulations of Protein Folding.

Unbiased molecular dynamics simulations of proteins can now capture spontaneous folding events. This provides a wealth of data reflecting information on folding mechanism, but raises the challenge of interpreting it in a meaningful way. Here, I describe how such simulations can be used to identify reactive states and reaction coordinates for describing folding, and how folding dynamics can be captured by projection onto those coordinates. Methods are described for quantifying the interactions important for defining the folding mechanism, and for comparison of simulations with experimental mechanistic probes, such as ϕ-values.

Machine learning builds full-QM precision protein force fields in seconds.

Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significant advances in machine learning, we have designed a neural network-based two-body molecular fractionation with conjugate caps (NN-TMFCC) approach to accelerate the energy and atomic force calculations of proteins. The results show very high precision for the proposed NN potential energy surface models of residue-based fragments, with energy root-mean-squared errors (RMSEs) less than 1.0kcal/mol and force RMSEs less than 1.3kcal/mol/Å for both training and testing sets. The proposed NN-TMFCC method calculates the energies and atomic forces of 15 representative proteins with full-QM precision in 10-100s, which is thousands of times faster than the full-QM calculations. The computational complexity of the NN-TMFCC method is independent of the protein size and only depends on the number of residue species, which makes this method particularly suitable for rapid prediction of large systems with tens of thousands or even hundreds of thousands of times acceleration. This highly precise and efficient NN-TMFCC approach exhibits considerable potential for performing energy and force calculations, structure predictions and molecular dynamics simulations of proteins with full-QM precision.

On the use of multiple-time-step algorithms to save computing effort in molecular dynamics simulations of proteins.

Computer simulation of proteins in aqueous solution at the atomic level of resolution is still limited in time span and system size due to limited computing power available and thus employs a variety of time‐saving techniques that trade some accuracy against computational effort. Examples of such time‐saving techniques are the application of constraints to particular degrees of freedom or the use of a multiple‐time‐step (MTS) algorithm distinguishing between particular forces when integrating Newton's equations of motion. The application of two types of MTS algorithms to bond‐stretching forces versus the remaining forces in molecular dynamics (MD) simulations of a protein in aqueous solution or of liquid water is investigated and the results in terms of total energy conservation and the influence on various other properties are compared to those of MD simulations of the same systems using bond‐length, and for water bond‐angle, constraints. At comparable computational effort, the use of bond‐length constraints in proteins leads to better energy conservation and less distorted properties than the two MTS algorithms investigated.

Network Analysis and Kinetic Clustering Methods Applied to Molecular Dynamics Simulations of Proteins in Complex Environments

Molecular dynamics (MD) simulations are increasingly used to probe the structural dynamics of proteins in response to perturbations in their environment. Characterization of MD trajectories within the simulation time-scale of tens to hundreds of microseconds is hindered by the determination of relevant reaction coordinates that characterize macrostates and transition barriers among them. Over recent years, kinetic clustering methods have been successfully applied to analyze MD trajectories allowing to analyze and describe complex free-energy landscapes of proteins. These methods rely on the identification of a set of relevant features describing these landscapes; however, exhaustive feature identification is limited by the amount of memory available in standard computer clusters. Network analysis and community detection methods provide an alternative to generate coarse-grained features by identifying communities of protein residues that are grouped together according to their time-dependent association. Such communities can be used for coarse-graining the representation of relatively large proteins (several hundreds of residues) to a few communities, over a time-scale of several microseconds. Here, we implement the application of community detection methods to MD trajectories of proteins ranging from tens to several hundreds of residues solvated in explicit water and/or embedded in lipid bilayers. Kinetic clustering methods are applied on both the coarse-grained representation generated by network analysis and the all-atom representation and used to analyze the free-energy landscape of a diverse set of proteins.

Parameterization of Unnatural Amino Acids with Azido and Alkynyl R-Groups for Use in Molecular Simulations.

Recent new methods to functionalize proteins at specific amino acid locations use unnatural amino acids that contain azido and alkynyl groups. This capability is unprecedented and enables the creation of site-specific protein devices. Because of the high specificity of these devices, many protein configurations are possible and in silico screens have shown promise in predicting optimal attachment site locations. Therefore, there is significant interest in improving current molecular dynamics (MD) models to include the unique chemistries of these linear moieties. This work uses the force field tool kit to obtain the bonded and nonbonded CHARMM parameters for small molecules that contain azido and alkynyl groups. Next, the reliability of these parameters is tested by running simulated MD analysis to prove that the modeled structures match those found in the literature and quantum theory. Finally, the protein MD simulation compares this parameter set with crystallographic data to give a greater understanding of unnatural amino acid influence on the protein structure.

Choice of Force Field for Proteins Containing Structured and Intrinsically Disordered Regions

Biomolecular force fields optimized for globular proteins fail to properly reproduce properties of intrinsically disordered proteins. In particular, parameters of the water model need to be modified to improve applicability of the force fields to both ordered and disordered proteins. Here, we compared performance of force fields recommended for intrinsically disordered proteins in molecular dynamics simulations of three proteins differing in the content of ordered and disordered regions (two proteins consisting of a well-structured domain and of a disordered region with and without a transient helical motif and one disordered protein containing a region of increased helical propensity). The obtained molecular dynamics trajectories were used to predict measurable parameters, including radii of gyration of the proteins and chemical shifts, residual dipolar couplings, paramagnetic relaxation enhancement, and NMR relaxation data of their individual residues. The predicted quantities were compared with experimental data obtained within this study or published previously. The results showed that the NMR relaxation parameters, rarely used for benchmarking, are particularly sensitive to the choice of force-field parameters, especially those defining the water model. Interestingly, the TIP3P water model, leading to an artificial structural collapse, also resulted in unrealistic relaxation properties. The TIP4P-D water model, combined with three biomolecular force-field parameters for the protein part, significantly improved reliability of the simulations. Additional analysis revealed only one particular force field capable of retaining the transient helical motif observed in NMR experiments. The benchmarking protocol used in our study, being more sensitive to imperfections than the commonly used tests, is well suited to evaluate the performance of newly developed force fields.

Detecting Functional Dynamics in Proteins with Comparative Perturbed-ensembles Analysis

Recent advances have made all-atom molecular dynamics (MD) a powerful tool to sample the conformational energy landscape. There are still however three major challenges in the application of MD to biological systems: accuracy of force field, time scale, and the analysis of simulation trajectories. Significant progress in addressing the first two challenges has been made and extensively reviewed previously. This Account focuses on strategies of analyzing simulation data of biomolecules that also covers ways to properly design simulations and validate simulation results. In particular, we examine an approach named comparative perturbed-ensembles analysis, which we developed to efficiently detect dynamics in protein MD simulations that can be linked to biological functions. In our recent studies, we implemented this approach to understand allosteric regulations in several disease-associated human proteins. The central task of a comparative perturbed-ensembles analysis is to compare two or more conformational ensembles of a system generated by MD simulations under distinct perturbation conditions. Perturbations can be different sequence variations, ligand-binding conditions, and other physical/chemical modifications of the system. Each simulation is long enough (e.g., microsecond-long) to ensure sufficient sampling of the local substate. Then, sophisticated bioinformatic and statistical tools are applied to extract function-related information from the simulation data, including principal component analysis, residue-residue contact analysis, difference contact network analysis (dCNA) based on the graph theory, and statistical analysis of side-chain conformations. Computational findings are further validated with experimental data. By comparing distinct conformational ensembles, functional micro- to millisecond dynamics can be inferred. In contrast, such a time scale is difficult to reach in a single simulation; even when reached for a single condition of a system, it is elusive as to what dynamical motions are related to functions without, for example, comparing free and substrate-bound proteins at the minimum. We illustrate our approach with three examples. First, we discuss using the approach to identify allosteric pathways in cyclophilin A (CypA), a member of a ubiquitous class of peptidyl-prolyl cis-trans isomerase enzymes. By comparing side-chain torsion-angle distributions of CypA in wild-type and mutant forms, we identified three pathways: two are consistent with recent nuclear magnetic resonance experiments, whereas the third is a novel pathway. Second, we show how the approach enables a dynamical-evolution analysis of the human cyclophilin family. In the analysis, both conserved and divergent conformational dynamics across three cyclophilin isoforms (CypA, CypD, and CypE) were summarized. The conserved dynamics led to the discovery of allosteric networks resembling those found in CypA. A residue wise determinant underlying the unique dynamics in CypD was also detected and validated with additional mutational MD simulations. In the third example, we applied the approach to elucidate a peptide sequence-dependent allosteric mechanism in human Pin 1, a phosphorylation-dependent peptidyl-prolyl isomerase. We finally present our outlook of future directions. Especially, we envisage how the approach could help open a new avenue in drug discovery.

Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field.

Molecular dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against experimental data or high-quality quantum calculations. Here, we develop search algorithms based on Monte Carlo and steepest descent calculations to optimize the backbone dihedral angle parameters from a single reference simulation. We apply these tools to improve the agreement between simulations of single, capped amino acids and experimentally determined J values and secondary structure propensities of these molecules. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are observed, and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small molecules and for protein simulations.

Detecting Functional Dynamics in Proteins with Comparative Perturbed-Ensembles Analysis.

Recent advances have made all-atom molecular dynamics (MD) a powerful tool to sample the conformational energy landscape. There are still however three major challenges in the application of MD to biological systems: accuracy of force field, time scale, and the analysis of simulation trajectories. Significant progress in addressing the first two challenges has been made and extensively reviewed previously. This Account focuses on strategies of analyzing simulation data of biomolecules that also covers ways to properly design simulations and validate simulation results. In particular, we examine an approach named comparative perturbed-ensembles analysis, which we developed to efficiently detect dynamics in protein MD simulations that can be linked to biological functions. In our recent studies, we implemented this approach to understand allosteric regulations in several disease-associated human proteins. The central task of a comparative perturbed-ensembles analysis is to compare two or more conformational ensembles of a system generated by MD simulations under distinct perturbation conditions. Perturbations can be different sequence variations, ligand-binding conditions, and other physical/chemical modifications of the system. Each simulation is long enough (e.g., microsecond-long) to ensure sufficient sampling of the local substate. Then, sophisticated bioinformatic and statistical tools are applied to extract function-related information from the simulation data, including principal component analysis, residue-residue contact analysis, difference contact network analysis (dCNA) based on the graph theory, and statistical analysis of side-chain conformations. Computational findings are further validated with experimental data. By comparing distinct conformational ensembles, functional micro- to millisecond dynamics can be inferred. In contrast, such a time scale is difficult to reach in a single simulation; even when reached for a single condition of a system, it is elusive as to what dynamical motions are related to functions without, for example, comparing free and substrate-bound proteins at the minimum. We illustrate our approach with three examples. First, we discuss using the approach to identify allosteric pathways in cyclophilin A (CypA), a member of a ubiquitous class of peptidyl-prolyl cis-trans isomerase enzymes. By comparing side-chain torsion-angle distributions of CypA in wild-type and mutant forms, we identified three pathways: two are consistent with recent nuclear magnetic resonance experiments, whereas the third is a novel pathway. Second, we show how the approach enables a dynamical-evolution analysis of the human cyclophilin family. In the analysis, both conserved and divergent conformational dynamics across three cyclophilin isoforms (CypA, CypD, and CypE) were summarized. The conserved dynamics led to the discovery of allosteric networks resembling those found in CypA. A residue wise determinant underlying the unique dynamics in CypD was also detected and validated with additional mutational MD simulations. In the third example, we applied the approach to elucidate a peptide sequence-dependent allosteric mechanism in human Pin 1, a phosphorylation-dependent peptidyl-prolyl isomerase. We finally present our outlook of future directions. Especially, we envisage how the approach could help open a new avenue in drug discovery.

Molecular Dynamics Simulation of Proteins.

Molecular dynamics simulations allow the conformational motion of a molecule such as a protein to be followed over time at atomic-level detail. Several choices need to be made prior to running a simulation, including the software, which molecules to include in the simulation, and the force field used to describe their behavior. Guidance on making these choices and other important aspects of running MD simulations is outlined here.

Improved Modeling of Cation-π and Anion-Ring Interactions Using the Drude Polarizable Empirical Force Field for Proteins.

Cation-π interactions are noncovalent interactions between a π-electron system and a positively charged ion that are regarded as a strong noncovalent interaction and are ubiquitous in biological systems. Similarly, though less studied, anion-ring interactions are present in proteins along with in-plane interactions of anions with aromatic rings. As these interactions are between a polarizing ion and a polarizable π system, the accuracy of the treatment of these interactions in molecular dynamics (MD) simulations using additive force fields (FFs) may be limited. In the present work, to allow for a better description of ion-π interactions in proteins in the Drude-2013 protein polarizable FF, we systematically optimized the parameters for these interactions targeting model compound quantum mechanical (QM) interaction energies with atom pair-specific Lennard-Jones parameters along with virtual particles as selected ring centroids introduced to target the QM interaction energies and geometries. Subsequently, MD simulations were performed on a series of protein structures where ion-π pairs occur to evaluate the optimized parameters in the context of the Drude-2013 FF. The resulting FF leads to a significant improvement in reproducing the ion-π pair distances observed in experimental protein structures, as well as a smaller root-mean-square differences and fluctuations of the overall protein structures from experimental structures. Accordingly, the optimized Drude-2013 protein polarizable FF is suggested for use in MD simulations of proteins where cation-π and anion-ring interactions are critical. © 2019 Wiley Periodicals, Inc.

Cell size effects in the molecular dynamics of the intrinsically disordered Aβ peptide.

Periodic molecular dynamics simulations of proteins may suffer from image interactions. Similarly, the hydrophobic effect required to keep a protein folded may not be enforced by small simulation cells. Accordingly, errors may arise both from the water concentration per se and the image interactions. Intrinsically disordered proteins are particularly sensitive, providing a worst-case estimate of the errors. Following this reasoning, we studied Aβ40 (Aβ), a disordered peptide central to Alzheimer's disease, by 100 different simulations with variable cell size from very large (20 Å) to very small (3 Å). Even for this very disordered peptide, most properties are not cell-size dependent, justifying the common use of modest-sized (10 Å) cells for simulating proteins. The radius of gyration, secondary structure, intrapeptide, and peptide-water hydrogen bonds are similar relative to standard deviations at any cell size. However, hydrophobic surface area increases significantly in small cells (confidence 95%, two-tailed t-test), as does the standard deviation in exposure and backbone conformations (>40% and >27%). Similar results were obtained for the force fields OPLS3e, Ambersb99-ILDN, and Charmm22*. The similar prevalence of structures and α-β transitions in long and short simulations indicate small diffusion barriers, which we suggest is a defining hallmark of intrinsically disordered proteins. Whereas hydrophilic exposure dominates in large cells, hydrophobic exposure dominates in small cells, suggesting a weakening of the hydrophobic effect by image interactions and the few water layers available to keep the protein compact, with a critical limit of 2-3 water layers required to enforce the hydrophobic effect.

Molecular dynamics simulation of proteins under high pressure: Structure, function and thermodynamics

BackgroundMolecular dynamics (MD) simulation is well-recognized as a powerful tool to investigate protein structure, function, and thermodynamics. MD simulation is also used to investigate high pressure effects on proteins. For conducting better MD simulation under high pressure, the main issues to be addressed are: (i) protein force fields and water models were originally developed to reproduce experimental properties obtained at ambient pressure; and (ii) the timescale to observe the pressure effect is often much longer than that of conventional MD simulations. Scope of reviewFirst, we describe recent developments in MD simulation methodologies for studying the high-pressure structure and dynamics of protein molecules. These developments include force fields for proteins and water molecules, and enhanced simulation techniques. Then, we summarize recent studies of MD simulations of proteins in water under high pressure. Major conclusionsRecent MD simulations of proteins in solution under pressure have reproduced various phenomena identified by experiments using high pressure, such as hydration, water penetration, conformational change, helix stabilization, and molecular stiffening. General significanceMD simulations demonstrate differences in the properties of proteins and water molecules between ambient and high-pressure conditions. Comparing the results obtained by MD calculations with those obtained experimentally could reveal the mechanism by which biological molecular machines work well in collaboration with water molecules.