Advanced Sampling Method Research Articles

Full-field experiment-aided virtual modelling framework for inverse-based stochastic prediction of structures with elastoplasticity

Forecasting of stochastic ductile failures in fabrication and service stages are challenging tasks of advanced structures in practical engineering. Due to the prohibitive costs of repetitive experimental tests to quantify optimal failure-related parameters, numerous studies have turned to simulation-based uncertainty quantification. However, the credibility of these approaches is frequently doubted by the function-generated distribution of random data involved. Thus, an accurate assessment of the material parameters ascertains the appropriate estimation of uncertain parameters, which is essential for the risk/safety assessment of structures in different stages. In this paper, a full-field experiment-aided virtual modelling framework for inverse-based prediction of stochastic elastoplastic failure (EVMF-ISP) is proposed to deliver precise insights regarding the effective distribution range of mechanical parameters. To this end, all available experiment observations serve as the reference for assessing the prior and posterior probability density of the unknown parameters through the real-time inverse uncertainty quantification (UQ) module. The framework can be divided into three parts, where initially a pre-virtual model (PRVM) is formulated, and Bayesian inference is implemented to propagate the experiment observations backwards to ascertain the uncertain parameters. Then, an advanced multidimensional slice sampling method is developed to deal with the derived complex posterior probability density function (PDF) of mechanical parameters. In the end, a reliable stochastic elastoplastic analysis can be conducted with the revised uncertain samples and finalised with post-virtual models (POVMs) for the concerned structures. Such that, accurate and efficient determination of nonlinear response of structures can be directly predicted. The EVMF-ISP framework is logically presented and thoroughly illustrated with practical applications.

Advanced Sampling Technique in Radiology Free-Text Data for Efficiently Building Text Mining Models by Deep Learning in Vertebral Fracture.

This study aims to establish advanced sampling methods in free-text data for efficiently building semantic text mining models using deep learning, such as identifying vertebral compression fracture (VCF) in radiology reports. We enrolled a total of 27,401 radiology free-text reports of X-ray examinations of the spine. The predictive effects were compared between text mining models built using supervised long short-term memory networks, independently derived by four sampling methods: vector sum minimization, vector sum maximization, stratified, and simple random sampling, using four fixed percentages. The drawn samples were applied to the training set, and the remaining samples were used to validate each group using different sampling methods and ratios. The predictive accuracy was measured using the area under the receiver operating characteristics (AUROC) to identify VCF. At the sampling ratios of 1/10, 1/20, 1/30, and 1/40, the highest AUROC was revealed in the sampling methods of vector sum minimization as confidence intervals of 0.981 (95%CIs: 0.980-0.983)/0.963 (95%CIs: 0.961-0.965)/0.907 (95%CIs: 0.904-0.911)/0.895 (95%CIs: 0.891-0.899), respectively. The lowest AUROC was demonstrated in the vector sum maximization. This study proposes an advanced sampling method, vector sum minimization, in free-text data that can be efficiently applied to build the text mining models by smartly drawing a small amount of critical representative samples.

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes.

Understanding the impact of extractant functionalization on metal-binding energetics in liquid-liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal-ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. We generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.

The pH Response of a Peptoid Oligomer.

Polypeptoids are N-substituted glycine polymers, which differ from peptides in the placement of the side chain on the amide nitrogen rather than the Cα carbon. A peptoid with a chiral side chain containing both an aromatic group and carboxylic acid has a structure that responds to pH changes. All-atom molecular dynamics simulations using a force field specifically tuned for peptoids were carried out with an advanced sampling method for the peptoid (S)-N-(1-carboxy-2-phenylethyl)glycine in the high and low pH limits. The simulations show that the structure changes from mostly cis amide bonds at low pH to mostly trans bonds at high pH. The structural changes are driven by side chain-backbone hydrogen bonds at low pH and side chain repulsions and increased water contact at high pH.

Identification of novel inhibitors targeting KRAS-SOS1 interactions by structure-based drug design.

The Kristen Rat Sarcoma (KRAS) protein, encoded by the KRAS gene, is a signal transducer that plays a crucial role in regulating cell proliferation. KRAS mutations have been known to cause lung, colorectal, and pancreatic cancers. Previously, KRAS was considered an "undruggable" target. However, with a better understanding of the protein's biology paired with advanced new technologies, the FDA approved the first KRAS inhibitor (sotorasib) for lung cancer patients with the KRASG12C mutation in 2021, expressing the recent interest of targeted drugs for KRAS. Oncogenic KRAS requires activation by the guanine nucleotide exchange factor (GEF) Son of Sevenless 1 (SOS1). The goal of this study is to develop novel pan-KRAS inhibitors by targeting the KRAS-SOS1 interactions through computational modeling. To start, we applied Gaussian accelerated molecular dynamics (GaMD) simulation, an advanced conformational sampling method, to collect the nature of the KRAS protein motion in solution. Then, the clustering analysis method was used to assemble KRAS conformational structures into ten groups. Finally, virtual screening and docking simulations are performed using the ChemBridge molecule database to identify potential KRAS inhibitors and their binding locations. We have addressed 60 compounds that show a good binding score to KRAS for a later experimental assessment of their (1) ability to disrupt the KRAS-SOS1 interactions and (2) cytotoxicity in KRAS mutation-bearing cell lines. The work provides insights into the drug efficacy of KRAS inhibitors and gives rise to a novel set of KRAS candidates to be used in oncogenic clinical treatments.

Enhancing machining accuracy reliability of multi-axis CNC Indexed by: machine tools using an advanced importance sampling method

The purpose of this paper is to propose a general precision allocation method to improve machining performance of CNC machine tools based on certain design requirements. A comprehensive error model of machine tools is established by using the differential motion relation of coordinate frames. Based on the comprehensive error model, a reliability model is established by updating the primary reliability with an advanced importance sampling method, which is used to predict the machining accuracy reliability of machine tools. Besides, to identify and optimize geometric error parameters which have a great influence on machining accuracy reliability of machine tools, the sensitivity analysis of machining accuracy is carried out by improved first-order second-moment method. Taking a large CNC gantry guide rail grinder as an example, the optimization results show that the method is effective and can realize reliability optimization of machining accuracy.

Influence of a Ser111-phosphorylation on Rab1b GTPase conformational dynamics studied by advanced sampling simulations.

Rab GTPases constitute the largest branch of the Ras protein superfamily that regulate intra-cellular membrane trafficking. Their signaling activity is mediated by the transition between an active GTP-bound state and an inactive GDP-bound state. In the inactive state the switch I and II segments adopt largely disordered flexible conformations, whereas in the active state these regions are in well-defined conformations. The switch I and II states are central for recognition of Rab GTPases by interacting partners. Phosphorylation of the Rab1b-GTPase at residue Ser111 (pS111) results in modulation of the signaling activity due to alterations of the protein interaction interface and also due to modulation of the conformational flexibility. We have studied the flexibility of native and pS111-Rab1b in complex with GTP or GDP using extensive Molecular Dynamics (MD) simulations and an advanced sampling method called DIhedral Angle-biasing potential Replica-Exchange Molecular dynamics (DIA-REMD). The DIA-REMD method promotes backbone and side chain dihedral transitions along a series of replica simulations in selected protein segments and through exchanges also improves sampling in an unbiased reference simulation. Application to the Rab1b system results in significantly enhanced sampling of different switch I/II conformational states in the GDP-bound Rab1b state. The pS111 modification is found to reduce the conformational flexibility even in the presence of GDP, which may influence signaling activities. The stabilizing effect can be attributed to the formation of additional surface salt bridges between Arg-residues and pS111 not present in the native structure. The DIA-REMD method could be a valuable approach for studying also other signaling proteins that contain flexible segments.

Research Progresses of Uncertainty Quantification Methods for High Fidelity Numerical Nuclear Reactor

Numerical reactor based on high fidelity model and method is with the characteristics of high precision and high resolution, but the inherent uncertainty of nuclear data and other parameters will seriously affect the uncertainty of numerical reactor analysis results. Based on the review of the research progresses of numerical reactors and its uncertainty quantification in the world, this paper focuses on the research progresses in NECP Laboratory of Xi’an Jiaotong University in recent years, including the development of one-step high fidelity numerical reactor program NECP-X, the generation of nuclear data covariance database, the uncertainty propagation method based on deterministic method and sampling method, and the uncertainty quantification in transient calculation. An advanced sampling method COST is proposed. Based on the high fidelity numerical reactor program, the uncertainty propagation of covariance of various nuclear parameters in the steady-state and transient analysis of the reactor core is quantified for the first time, which is of great significance for engineering application of numerical reactors.

Theoretical mechanistic study of CO catalytic oxidation by O2 on an ultra-small 13-atom bimetallic Ag7Au6 cluster

We report an advanced configurational sampling method that uses density functional theory (DFT) to design a highly active catalyst for conversion of CO into less-harmful products, under ambient conditions. The reaction pathway for CO oxidation by O2 on ultra-small 13-Atom bimetallic Ag7Au6 cluster has two possible mechanisms, namely, stepwise adsorption and co-adsorption. The rate-determining step involving with COO association via a co-adsorption process shows a significantly small barrier of 0.21 eV. Furthermore, microkinetic simulation results suggest that CO oxidation rates and the optimal temperature for CO oxidation exhibit both greater performances for the co-adsorption pathway, compared to that for a stepwise-adsorption mechanism. Our new proposed mechanism suggests that the bimetallic Ag7Au6 catalyst is active for CO oxidation at room temperatures. Thus, it has potential application as a highly-active catalyst for conversion of carbon monoxide into less toxic CO2.

Accurate Determination of Cavitand Binding Free Energies via Unrestrained Advanced Sampling.

Calculation of binding free energies, which characterize the stability and specificity of molecular interactions (e.g., drug formulations), has been established as an important application of modern computational chemistry. Though several methodologies currently exist within the literature, there is always a need for improvements in speed and accuracy that allow for efficient and accurate chemical screening. In this paper we explore the application of an unrestrained advanced sampling method, adaptive biasing force (ABF), to the calculation of standard binding free energies for a set of molecular host-guest systems for which the binding free energy is well-known: the cucurbit[7]uril-hosted systems utilized in the HYDROPHOBE challenge [ Assaf et al. J. Phys. Chem. B 2017 , 121 , 11144 - 11162 ]. We demonstrate that the use of the ABF advanced sampling method yields values systematically closer to experiment, with a lower uncertainty in the calculation, than thermodynamic integration and restrained sampling techniques commonly used in the literature. We hypothesize that this is due to the algorithm's ability to drive multiple binding and dissociation events through applied bias forces. These results show the promise of unrestrained advanced sampling methods along well-defined reaction coordinates to the calculation of binding energies in host-guest systems.

Shear-solvo defect annihilation of diblock copolymer thin films over a large area.

Achieving defect-free block copolymer (BCP) nanopatterns with a long-ranged orientation over a large area remains a persistent challenge, impeding the successful and widespread application of BCP self-assembly. Here, we demonstrate a new experimental strategy for defect annihilation while conserving structural order and enhancing uniformity of nanopatterns. Sequential shear alignment and solvent vapor annealing generate perfectly aligned nanopatterns with a low defect density over centimeter-scale areas, outperforming previous single or sequential combinations of annealing. The enhanced order quality and pattern uniformity were characterized in unprecedented detail via scattering analysis and incorporating new mathematical indices using elaborate image processing algorithms. In addition, using an advanced sampling method combined with a coarse-grained molecular simulation, we found that domain swelling is the driving force for enhanced defect annihilation. The superior quality of large-scale nanopatterns was further confirmed with diffraction and optical properties after metallized patterns, suggesting strong potential for application in optoelectrical devices.

Partial groups ranked set sampling and mean estimation

ABSTRACTRanked set sampling (RSS) is an advanced sampling method which is very effective for estimating mean of the population when exact measurement of observation is difficult and/or expensive. Balanced Groups RSS (BGRSS) is one of the modification of RSS where only the lowest, the median and the largest ranked units are taken into account. Although BGRSS is advantageous and useful for some specific cases, it has strict restrictions regarding the set size which could be problematic for sampling plans. In this study, we make an improvement on BGRSS and propose a new design called Partial Groups RSS which offers a more flexible sampling plan providing the independence of the set size and sample size. Partial Groups RSS also has a cost advantage over BGRSS. We construct a Monte Carlo simulation study comparing the performance of the mean estimators of the proposed sampling design and BGRSS according to their sampling costs and mean squared errors for various type of distributions. In addition, we give a biometric data application for investigating the efficiency of Partial Groups RSS in real life applications.

Fast-Folding Pathways of the Thrombin-Binding Aptamer G-Quadruplex Revealed by a Markov State Model

G-quadruplex structures participate in many important cellular processes. For a better understanding of their functions, knowledge of the mechanism by which they fold into the functional native structures is necessary. In this work, we studied the folding process of the thrombin-binding aptamer G-quadruplex. Enabled by a computational paradigm that couples an advanced sampling method and a Markov state model, four folding intermediates were identified, including an antiparallel G-hairpin, two G-triplex structures, and a double-hairpin conformation. Likewise, a misfolded structure with a nonnative distribution of syn/anti guanines was also observed. Based on these states, a transition path analysis revealed three fast-folding pathways, along which the thrombin-binding aptamer would fold to the native state directly, with no evidence of potential nonnative competing conformations. The results also showed that the TGT-loop plays an important role in the folding process. The findings of this research may provide general insight about the folding of other G-quadruplex structures.

Application of advanced sampling and analysis methods to predict the structure of adsorbed protein on a material surface.

The use of standard molecular dynamics simulation methods to predict the interactions of a protein with a material surface have the inherent limitations of lacking the ability to determine the most likely conformations and orientations of the adsorbed protein on the surface and to determine the level of convergence attained by the simulation. In addition, standard mixing rules are typically applied to combine the nonbonded force field parameters of the solution and solid phases the system to represent interfacial behavior without validation. As a means to circumvent these problems, the authors demonstrate the application of an efficient advanced sampling method (TIGER2A) for the simulation of the adsorption of hen egg-white lysozyme on a crystalline (110) high-density polyethylene surface plane. Simulations are conducted to generate a Boltzmann-weighted ensemble of sampled states using force field parameters that were validated to represent interfacial behavior for this system. The resulting ensembles of sampled states were then analyzed using an in-house-developed cluster analysis method to predict the most probable orientations and conformations of the protein on the surface based on the amount of sampling performed, from which free energy differences between the adsorbed states were able to be calculated. In addition, by conducting two independent sets of TIGER2A simulations combined with cluster analyses, the authors demonstrate a method to estimate the degree of convergence achieved for a given amount of sampling. The results from these simulations demonstrate that these methods enable the most probable orientations and conformations of an adsorbed protein to be predicted and that the use of our validated interfacial force field parameter set provides closer agreement to available experimental results compared to using standard CHARMM force field parameterization to represent molecular behavior at the interface.

Untwisting of Double-Stranded DNA and RNA Investigated by Molecular Dynamics Simulations

The response of dsDNA and dsRNA to torsional stress influences many of their biological functions, including binding by proteins, transcription initiation and genome packaging. So far, the twist flexibility of DNA and RNA has been studied comparatively with single-molecule experiments. However, while these studies capture the global deformability of duplexes with a length of several thousand base pairs (bp), detailed insight into conformational changes on the base pair level remains elusive. We performed all-atom Molecular Dynamics (MD) simulations on 15 bp long DNA and RNA duplexes at femtosecond resolution. Employing an advanced sampling method based on a torsion-like restraining potential, we were able to untwist the molecules to induce localized melting. The simulations allowed us to determine the relative free energy and structural changes as a function of the mean twist. Suppression of local bending has a strong influence on the onset of DNA melting. Significant differences in the response were also observed for DNA and RNA. The results can have important implications for understanding the mechanism of replication and transcription of DNA the function of RNA molecules.

What is important for ant assemblages in temperate forest soils?

Ant assemblages in the soil have been studied at eight forest sites (4 oak forest sites, and 4 pine forest sites) in four study areas (1 seminatural area, and 3 industrialized areas) in South Korea for 6 years from 2002 to 2010. Soil cores and Tullgren funnel were used for the ant survey. Ant surveys were carried out once per year in autumn (from late September to mid-October). The soil pH was lower in the industrialized than in the seminatural area, showing the acidified soils in the industrialized areas. However, the soil acidification did not influence the ant assemblages. The results from the nonmetric multidimensional scaling ordination and from the community temperature index values indicate that temperature is a key determinant for structures of the soil ant assemblages. The ant assemblages were not different according to the forest types (oak forests vs. pine forests). Occurrence of ant species varied greatly among years, indicating that more replicates and advanced sampling method are needed for the monitoring of the soil ant assemblages.

Conformational Equilibrium between the Sub States of the Acidic Denatured State of ACBP Determined by NMR Chemical Shifts and Metadynamics

NMR Chemical shifts have recently gain renovated attention as a probe for both the structure and the dynamics of proteins. The structure and the dynamics of folded proteins can be elucidated straightforwardly from the backbone chemical shifts at least for small to medium sized protein. In the denatured state and for intrinsically disordered proteins chemical shifts are routinely used as probe for secondary structure propensities and populations and have been also used as a probe for tertiary structure contact propensities. In this work we show how chemical shifts, together with an appropriate advanced sampling method, can be successfully used to determine the structure and the dynamics of the denatured state of ACBP, an 86 residues long proteins, in acidic conditions. Chemical shifts are used as replica-averaged restrained to improve the quality of the molecular mechanic force field in the Maximum Entropy framework and Bias-Exchange Metadynamics is used to achieve a converged sampling of the large conformational space of an unfolded protein.View Large Image | View Hi-Res Image | Download PowerPoint Slide

The CDF and its sensitivity analysis of stochastic structure with stochastic excitation by advanced stratified line sampling

For the stochastic structure with stochastic excitation, an advanced stratified line sampling (SLS) method is presented to obtain the cumulative distribution function (CDF) of the structural response and its sensitivity. The advanced stratified line sampling method introduces a set of middle failure subsets firstly. And for each subset, the conventional line sampling can be used to obtain the corresponding value of the response’s CDF. At the same time, the sensitivity estimations of each failure subset can also be computed by modifying the important direction and corresponding reliability coefficients. The properties of CDF sensitivity are proved while the performance function is linear with normal random variables. After two simple examples are used to demonstrate the properties of CDF sensitivity and the feasibility of the presented method, the method employed to analyze the CDF and corresponding sensitivity of root bending moment (RBM) responses for the stochastic BAH is wing with gust excitation to a square-edged gust and to a Dryden gust. The results show that the parameters of the second and the fifth order modals exert more influence on the CDF of response than the other ones, and the presented SLS method can more significantly reduce the computational cost compared with Monte Carlo simulation (MCS).

Identification of Potential Small Molecule Binding Pockets on Rho Family GTPases

Rho GTPases are conformational switches that control a wide variety of signaling pathways critical for eukaryotic cell development and proliferation. They represent attractive targets for drug design as their aberrant function and deregulated activity is associated with many human diseases including cancer. Extensive high-resolution structures (>100) and recent mutagenesis studies have laid the foundation for the design of new structure-based chemotherapeutic strategies. Although the inhibition of Rho signaling with drug-like compounds is an active area of current research, very little attention has been devoted to directly inhibiting Rho by targeting potential allosteric non-nucleotide binding sites. By avoiding the nucleotide binding site, compounds may minimize the potential for undesirable off-target interactions with other ubiquitous GTP and ATP binding proteins. Here we describe the application of molecular dynamics simulations, principal component analysis, sequence conservation analysis, and ensemble small-molecule fragment mapping to provide an extensive mapping of potential small-molecule binding pockets on Rho family members. Characterized sites include novel pockets in the vicinity of the conformationaly responsive switch regions as well as distal sites that appear to be related to the conformations of the nucleotide binding region. Furthermore the use of accelerated molecular dynamics simulation, an advanced sampling method that extends the accessible time-scale of conventional simulations, is found to enhance the characterization of novel binding sites when conformational changes are important for the protein mechanism.

BayesMD: Flexible Biological Modeling for Motif Discovery

We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fashion. A mixture of Dirichlets is used as prior over nucleotide probabilities in binding sites. It is trained on transcription factor (TF) databases in order to extract the typical properties of TF binding sites. In a similar fashion we train organism-specific priors for the background sequences. Lastly, we use a prior over the position of binding sites. This prior represents information complementary to the motif and background priors coming from conservation, local sequence complexity, nucleosome occupancy, etc. and assumptions about the number of occurrences. The Bayesian inference is carried out using a combination of exact marginalization (multinomial parameters) and sampling (over the position of sites). Robust sampling results are achieved using the advanced sampling method parallel tempering. In a post-analysis step candidate motifs with high marginal probability are found by searching among those motifs that contain sites that occur frequently. Thereby, maximum a posteriori inference for the motifs is avoided and the marginal probabilities can be used directly to assess the significance of the findings. The framework is benchmarked against other methods on a number of real and artificial data sets. The accompanying prediction server, documentation, software, models and data are available from http://bayesmd.binf.ku.dk/.