Umbrella Sampling Simulations Research Articles

Quantitative Prediction of Dissociation Rates of PYK2 Ligands Using Umbrella Sampling and Milestoning.

We used umbrella sampling and the milestoning simulation method to study the dissociation of multiple ligands from protein kinase PYK2. The activation barriers obtained from the potential of mean force of the umbrella sampling simulations correlated well with the experimental dissociation rates. Using the zero-temperature string method, we obtained optimized paths along the free-energy surfaces for milestoning simulations of three ligands with a similar structure. The milestoning simulations gave an absolute dissociation rate within 2 orders of magnitude of the experimental value for two ligands but at least 3 orders of magnitude too high for the third. Despite the similarity in their structures, the ligands took different pathways to exit from the binding site of PYK2, making contact with different sets of residues. In addition, the protein experienced different conformational changes for dissociation of the three ligands.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

Structure-based virtual screening of novel USP5 inhibitors targeting the zinc finger ubiquitin-binding domain

The equilibrium of cellular protein levels is pivotal for maintaining normal physiological functions. USP5 belongs to the deubiquitination enzyme (DUBs) family, controlling protein degradation and preserving cellular protein homeostasis. Aberrant expression of USP5 is implicated in a variety of diseases, including cancer, neurodegenerative diseases, and inflammatory diseases. In this paper, a multi-level virtual screening (VS) approach was employed to target the zinc finger ubiquitin-binding domain (ZnF-UBD) of USP5, leading to the identification of a highly promising candidate compound 0456–0049. Molecular dynamics (MD) simulations were then employed to assess the stability of complex binding and predict hotspot residues in interactions. The results indicated that the candidate stably binds to the ZnF-UBD of USP5 through crucial interactions with residues ARG221, TRP209, GLY220, ASN207, TYR261, TYR259, and MET266. Binding free energy calculations, along with umbrella sampling (US) simulations, underscored a superior binding affinity of the candidate relative to known inhibitors. Moreover, US simulations revealed conformational changes of USP5 during ligand dissociation. These insights provide a valuable foundation for the development of novel inhibitors targeting USP5.

Free-energy landscape and spinodals for the liquid-liquid transition of the TIP4P/2005 and TIP4P/Ice models of water.

Continued increases in computational power now make it possible to evaluate the free-energy landscape associated with the first-order liquid-liquid transition in realistic models of water for which an accurate estimate of the liquid-liquid critical point exists, and to explore its change with pressure near the coexistence line. We report the results of 50 μs-long NPT umbrella sampling simulations for two realistic models for water, TIP4P/2005 and TIP4P/ice, 3-9K below their critical temperatures. The free energy profile at different pressures clearly shows the presence of two well-defined free energy basins and makes it possible to identify the liquid-liquid spinodal points, the limits of stability that define the (temperature dependent) pressure range within which two distinct free energy basins exist. The results show that for temperatures less than 10K below the critical temperature, metastable states are possible across a very limited pressure interval, information that is relevant to the interpretation of experiments probing the metastable phase behavior of deeply supercooled water in the so-called no-man's land.

Viral RNA Replication Suppression of SARS-CoV-2: Atomistic Insights into Inhibition Mechanisms of RdRp Machinery by ddhCTP.

The nonstructural protein 12, known as RNA-dependent RNA polymerase (RdRp), is essential for both replication and repair of the viral genome. The RdRp of SARS-CoV-2 has been used as a promising candidate for drug development since the inception of the COVID-19 spread. In this work, we performed an in silico investigation on the insertion of the naturally modified pyrimidine nucleobase ddhCTP into the SARS-CoV-2 RdRp active site, in a comparative analysis with the natural one (CTP). The modification in ddhCTP involves the removal of the 3'-hydroxyl group that prevents the addition of subsequent nucleotides into the nascent strand, acting as an RNA chain terminator inhibitor. Quantum mechanical investigations helped to shed light on the mechanistic source of RdRp activity on the selected nucleobases, and comprehensive all-atom simulations provided insights about the structural rearrangements occurring in the active-site region when inorganic pyrophosphate (PPi) is formed. Subsequently, the intricate pathways for the release of PPi, the catalytic product of RdRp, were investigated using Umbrella Sampling simulations. The results are in line with the available experimental data and contribute to a more comprehensive point of view on such an important viral enzyme.

Nuclear Quantum Effects in Hydroxide Hydrate Along the H-Bond Bifurcation Pathway.

Path integral (PI) simulations are used to explore nuclear quantum effects (NQEs) in hydroxide hydrate and its perdeuterated isotopomer along the H-bond bifurcation pathway. Toward this, a new potential energy surface using the symmetric gradient domain machine learning method with ab initio data at the CCSD(T)/aug-cc-pVTZ level is built. From PI umbrella sampling (US) simulations, free energy profiles along the bifurcation coordinate are explored as a function of temperature. At ambient temperature, the bifurcation barrier is increased upon inclusion of NQEs. At low temperatures in the deep tunneling regime, the barrier is strongly decreased and flattened. These trends are examined, and the role of the O-O distance is also investigated through two-dimensional US simulations.

Graphene Exfoliation in Binary NMP/Water Mixtures by Molecular Dynamics Simulations.

We investigate the molecular mechanism underlying the liquid-phase exfoliation of graphene in aqueous/N-methyl-2-pyrrolidone (NMP) solvent mixtures and calculate the associated free energies, considering different NMP concentrations and exfoliation temperatures. We employ steered molecular dynamics to establish a path for the exfoliation of a graphene sheet from graphite within each solvent environment. Then, we conduct umbrella sampling simulations throughout the created paths to compute the potential of mean force (PMF) of the graphene sheet. As the exfoliated nanosheet disperses into the liquid, it becomes fully covered by an adsorbed solvent monolayer. We analyze the composition of the monolayer by measuring the direct contacts of either NMP or water molecules with the carbon surface. The carbon surface exhibits a preference for adsorbing NMP over water. The NMP molecules form a hydrophobic compact monolayer structure, effectively protecting the carbon interface from unfavorable interactions with water. The creation of the hydrophobic monolayer is a key factor in the exfoliation process, as it effectively inhibits the restacking of exfoliated nanosheets. An adequate level of graphene solubility is achieved through the addition of 20 % to 30 % water by weight to the NMP solvent. This finding holds significant importance for improving production efficiency and reducing dependence on organic solvents in the industrial manufacturing of graphene.

Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis.

In the present work a combination of traditional and steered molecular dynamics based techniques were employed to identify potential inhibitors against the human BRD4 protein (BRD4- BD1); an established drug target for multiple illnesses including various malignancies. Quinoline derivatives that were synthesized in-house were tested for their potential as new BRD4-BD1 inhibitors. Initially molecular docking experiments were performed to determine the binding poses of BRD4-BD1 inhibitors. To learn more about the thermodynamics of inhibitor binding to the BRD4-BD1 active site, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) free energy calculations were conducted afterwards. The findings of the MM-PBSA analysis were further reinforced by performing steered umbrella sampling simulations which revealed crucial details about the binding/unbinding process of the most potent quinoline derivatives at the BRD4-BD1 active site. We report a novel quinoline derivative which can be developed into a fully functional BRD4-BD1 inhibitor after experimental validation. The identified compound (4 g) shows better properties than the standard BRD4-BD1 inhibitors considered in the study. The study also highlights the crucial role of Gln78, Phe79, Trp81, Pro82, Phe83, Gln84, Gln85, Val87, Leu92, Leu94, Tyr97, Met105, Cys136, Asn140, Ile146 and Met149 in inhibitor binding. The study provides a possible lead candidate and key amino acids involved in inhibitor recognition and binding at the active site of BRD4-BD1 protein. The findings might be of significance to medicinal chemists involved in the development of potent BRD4-BD1 inhibitors.

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

Umbrella sampling simulations of cardiac thin filament reveal thermodynamic consequences of troponin I inhibitory peptide mutations

Umbrella sampling simulations of cardiac thin filament reveal thermodynamic consequences of troponin I inhibitory peptide mutations

Free fatty acid receptors beyond fatty acids: A computational journey to explore peptides as possible binders of GPR120

Free fatty acids receptors, with members among G protein-coupled receptors (GPCRs), are crucial for biological signaling, including the perception of the so called “fatty taste”. In recent years, GPR120, a protein belonging to the GPCR family, drew attention as an interesting pharmacological target to cope with obesity, satiety and diabetes. Apart from long chain fatty acids, which are GPR120 natural agonists, other synthetic molecules were identified as agonists expanding the chemical space of GPR120's ligands. In this scenario, we unveiled peptides as possible GPR120 binders toward a better understanding of this multifaceted and relevant target. This study analyzed a virtual library collecting 531 441 low-polar hexapeptides, providing mechanistic insights on the GPR120 activation and further extending the possible chemical space of GPR120 agonists. The computational pipeline started with a narrow filtering of hexapeptides based on their chemical similarity with known GPR120 agonists. The best hits were tested through docking studies, molecular dynamics and umbrella sampling simulations, which pointed to G[I,L]FGGG as a promising GPR120 agonist sequence. The presence of both peptides in food-related proteins was thoroughly assessed, revealing they may occur in mushrooms, food-grade bacteria and rice. Simulations on the counterparts with D-amino acids were also performed. Umbrella sampling simulations described that GdIFGGG may have a better interaction compared to its all-L counterpart (−13 kCal/mol ΔG and −6 kCal/mol ΔG, respectively). Overall, we obtained a predictive model to better understand the underpinning mechanism of GPR120-hexapeptides interaction, hierarchizing novel potential agonist peptides for further analysis and describing promising food sources worth of further dedicated investigations.

Separation of protein corona from nanoparticles under intracellular acidic conditions: effect of protonation on nanoparticle-protein and protein-protein interactions.

Protein coronas separate from nanoparticles under intracellular acidic conditions however, competitive adsorption of multiple proteins and their protein network formation under different pH conditions have not yet been systematically studied at the atomic scale. Herein, we report all-atom molecular dynamics simulations of plasma proteins (human serum albumin and immunoglobulin gamma-1 chain C) adsorbed to 10 nm-sized carboxyl-terminated polystyrene (PS) nanoparticles at different protonation states that mimic extracellular and intracellular pH conditions of 7, 6-5, and 4.5. Binding free energies are calculated from umbrella sampling simulations, showing the significantly weakened binding between PS particles and proteins at the protonation state at pH 4.5, in agreement with experiments showing the separation of protein corona from nanoparticles at pH 4.5. Mixtures of multiple proteins and PS particles are also simulated, showing much less protein adsorption and protein cluster formation at the protonation state at pH 4.5 than that at higher pH values, which are further confirmed by calculating the diffusivities and hydrodynamic radii of individual proteins. In particular, electrostatic particle-protein and protein-protein interactions are significantly weakened by a combination of particle and protein protonation rather than by particle protonation alone, to an extent dependent on different proteins. These findings help explain the experimental observations regarding separation of protein corona from nanoparticles under intracellular acidic conditions at pH 4.5 but not at higher pH, supporting that acidification cannot be the only reason for this separation during the process of endosome maturation.

Dissecting the chiral recognition of TLR4/MD2 with Neoseptin-3 enantiomers by molecular dynamics simulations.

Toll-like receptor 4 (TLR4) is a pivotal innate immune recognition receptor that regulates intricate signaling pathways within the immune system. Neoseptin-3 (Neo-3), a recently identified small-molecule agonist for mouse TLR4/MD2, exhibits chiral recognition properties. Specifically, the L-enantiomer of Neo-3 (L-Neo-3) effectively activates the TLR4 signaling pathway, while D-Neo-3 fails to induce TLR4 activation. However, the underlying mechanism by which TLR4 enantioselectively recognizes Neo-3 enantiomers remains poorly understood. In this study, in silico simulations were performed to investigate the mechanism of chiral recognition of Neo-3 enantiomers by TLR4/MD2. Two L-Neo-3 molecules stably resided within the cavity of MD2 as a dimer, and the L-Neo-3 binding stabilized the (TLR4/MD2)2 dimerization state. However, the strong electrostatic repulsion between the hydrogen atoms on the chiral carbon of D-Neo-3 molecules caused the relative positions of two D-Neo-3 molecules to continuously shift during the simulation process, thus preventing the formation of D-Neo-3 dimer as well as their stable interactions with the surrounding residues in (TLR4/MD2)2. Considering that L-Neo-3 could not sustain a stable dimeric state in the bulk aqueous environment, it is unlikely that L-Neo-3 entered the cavity of MD2 as a dimeric unit. Umbrella sampling simulations revealed that the second L-Neo-3 molecule entering the cavity of MD2 exhibited a lower binding energy (-25.75 kcal mol-1) than that of the first L-Neo-3 molecule (-14.31 kcal mol-1). These results imply that two L-Neo-3 molecules enter the cavity of MD2 sequentially, with the binding of the first L-Neo-3 molecule facilitating the entry of the second one. This study dissects the binding process of Neo-3 enantiomers, offering a comprehensive understanding of the atomic-level mechanism underlying TLR4's chiral recognition of Neo-3 molecules.

Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison

Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles.Graphical abstractAll-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.

OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking.

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.

Solvent-Assisted Graphene Exfoliation from Graphite Using Umbrella Sampling Simulations.

We employed molecular dynamics (MD) simulations coupled with umbrella sampling to explore the thermodynamics governing the exfoliation of a single graphene layer from a graphitic substrate in five different solvents such as dimethylacetamide (DMA), N-methyl-2-pyrrolidone (NMP), dimethyl sulfoxide (DMSO), cyclohexane (CHX), and water. The substrate was modeled as a stack of three identical graphene layers with the graphene sheet undergoing exfoliation positioned on top of this stack. The initial configurations for each umbrella simulation were generated through steered MD simulations carried out along two distinct coordinates: one parallel and the other perpendicular to the graphene layers. Our analyses revealed a uniform wetting behavior for both the nanosheet and the graphitic substrate in all of the tested solvents. Consistent with experimental observations, the steered simulations confirmed that exfoliation is more favorable along the parallel direction than along the perpendicular one. All non-water solvents exhibit comparable effectiveness in the exfoliation of graphene. The calculated free energies of these solvents in parallel exfoliation consistently fell within the range of 90-100 kJ/mol/nm2. In perpendicular exfoliation, however, the corresponding energies converge to lower values. This difference is attributed to the nonequilibrium nature of the perpendicular exfoliation, primarily caused by the great steering velocity of the graphene sheet immediately after detachment from the substrate. This rapid motion of the nanosheet along the perpendicular coordinate results in an elevated system energy and heating.

High-throughput virtual screening of potential inhibitors of GPR52 using docking and biased sampling method for Huntington's disease therapy.

Huntington's disease (HD) is a rare and progressive neurodegenerative disorder caused by polyglutamine (poly-Q) mutations of the huntingtin (HTT) gene resulting in chorea, cognitive, and psychiatric dysfunctions. Being a monogenic condition, reducing the levels of the mutated huntingtin protein (mHTT) holds promise as an effective therapeutic approach. GPR52, an orphan G-protein coupled receptor (GPCR), enriched in the striatum, is a novel target for slowing down the progression of HD by lowering the mHTT levels. Therefore, the study focuses on identifying potent small-molecule inhibitors for GPR52 using a combination of robust high-throughput virtual screening (HTVS) and pharmacokinetics profiling followed by fast pulling of ligand (FPL) and umbrella sampling (US) simulations. Initially, screening a library of 2,36,545 compounds was done against the binding pocket of GPR52. Based on binding affinity, stereochemical and non-bonded interactions, and pharmacokinetic profiling, 50 compounds were shortlisted. Selected hit compounds 1, 2, and 3 were subjected to FPL simulations with applied external bias potential to investigate their unique dissociation pathways and intermolecular interactions over time. Subsequently, the US simulations were performed on the selected hit compounds to estimate their binding free energy (ΔG). The analysis of the trajectories obtained from simulations revealed that the residues TYR34, TYR185, GLY187, ASP188, ILE189, SER299, PHE300, and THR303 within the active site of GPR52 were significant for efficient ligand binding through the formation of various hydrogen bond interactions and hydrophobic contacts. Out of the three hit compounds, compound 3 had the lowest ΔG of - 20.82 ± 0.44kcal/mol. The study identified compounds 1, 2, and 3 as potential molecules that can be developed as GPR52 inhibitors holding promise for lowering mHTT levels.

Dynamics behavior of PE and PET oligomers in lipid bilayer simulations

In recent years many investigators have been concerned about the toxicity and potential health hazards of micro- and nanoplastics. However, we are still lacking a good understanding of the methods of their transport into the human body and subsequently within cells. This is especially true at the lower nanometer scale; these particles are potentially more dangerous than their micrometer counterparts due to their easier permeation into cells. In this study we used both unbiased molecular dynamics simulations and steered umbrella sampling simulations to explore the interactions of polyethylene terephthalate (PET) and polyethylene (PE) oligomers in phospholipid bilayers. Our simulations revealed that the bilayers did not represent significant energy barriers to the small oligomers; not only did they readily enter the cell membrane but they also became concentrated into specific parts of the membrane. The larger PET tetramers exhibited a strong aggregation in water but were the least likely to permeate through or into the membranes. It is possible that PE monomers and tetramers can become concentrated into membranes while PET monomers are more likely to pass through or concentrate just inside the membrane surface. Passive transport of microplastics into cells is, however, likely limited to particles of a few nanometers in diameter.

Design of alkyl-substituted aminothiazoles to optimise corrosion inhibition for galvanised steel: A combined experimental and molecular modelling approach

This work proposes a combined experimental and molecular modelling approach to clarify the complex substitution effect of aminothiazole as corrosion inhibitor for galvanised steel in saline aqueous environments. Electrochemical results have shown that introducing alkyl residues ameliorates inhibition efficiency to varied degrees. Umbrella sampling simulations supplemented by a newly developed GFN-force-field-based molecular approach reveal that the particular adsorption state renders a hydrophobic environment at the outer surface, where increased alkyl size leads to greater hydrophobicity for corrosion inhibition. Such an effect becomes less prominent with increasing concentration, attributed to the enhanced intermolecular interaction destabilising the adsorption process.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity.

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.