Replica Exchange Monte Carlo Sampling Research Articles

Probing Local Environments of Oxygen Vacancies Responsible for Hydration in Sc-Doped Barium Zirconates at Elevated Temperatures: In Situ X-ray Absorption Spectroscopy, Thermogravimetry, and Active Learning Ab Initio Replica Exchange Monte Carlo Simulations

Proton-conducting oxides, specifically heavily Sc-doped barium zirconate perovskite, have attracted attention as electrolytes for intermediate-temperature protonic ceramic fuel cells because of their high proton conductivity and high chemical stability against carbon dioxide in that temperature regime. Hydration is a key reaction for incorporating protons by filling oxygen vacancies, VO, with hydroxyl groups and activating proton conduction in the perovskite. However, probing the local environment of oxygen vacancies responsible for hydration is challenging because the behavior depends on the temperature and water partial pressure, which necessitates in situ observations and calculations of the local environments at elevated temperatures. To obtain such information, we combined in situ X-ray absorption spectroscopy (XAS) for both the Sc and Zr K-edges, thermogravimetry, X-ray diffractometry, and active learning ab initio replica exchange Monte Carlo (RXMC) simulations in undoped and 20–40 at % Sc-doped barium zirconates at and below 800 °C. The presence of oxygen vacancies adjacent to Sc and Zr in the dehydrated samples and the hydration of these oxygen vacancies under a wet atmosphere were probed by in situ XAS for Sc and Zr pre-edges at elevated temperatures. Here, the microscopic hydration linearly responds to the macroscopic degree of hydration. RXMC sampling further supports the presence of Sc-VO-Zr and Sc-VO-Sc environments. An initial hydration occurs in the Sc-VO-Zr environment at and above 600 °C, but the Sc-VO-Sc environment contribution is greater at higher degrees of hydration. The Zr-Vo-Zr environment is the least abundant among them for the whole temperature range examined and thus has a negligible impact.

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides.

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem: the sampling of the conformational landscape of polypeptides at finite temperature. We develop a local kernel regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type neural networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.

EDock: blind protein\u2013ligand docking by replica-exchange monte carlo simulation

Protein–ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the receptor and a user-specified binding site to start. This information is, however, not available for the majority of unknown proteins, including many pharmaceutically important targets. Developing blind docking methods without predefined binding sites and working with low-resolution receptor models from protein structure prediction is thus essential. In this manuscript, we propose a novel Monte Carlo based method, EDock, for blind protein–ligand docking. For a given protein, binding sites are first predicted by sequence-profile and substructure-based comparison searches with initial ligand poses generated by graph matching. Next, replica-exchange Monte Carlo (REMC) simulations are performed for ligand conformation refinement under the guidance of a physical force field coupled with binding-site distance constraints. The method was tested on two large-scale datasets containing 535 protein–ligand pairs. Without specifying binding pockets on the experimental receptor structures, EDock achieves on average a ligand RMSD of 2.03 Å, which compares favorably with state-of-the-art docking methods including DOCK6 (2.68 Å) and AutoDock Vina (3.92 Å). When starting with predicted models from I-TASSER, EDock still generates reasonable docking models, with a success rate 159% and 67% higher than DOCK6 and AutoDock Vina, respectively. Detailed data analyses show that the major advantage of EDock lies in reliable ligand binding site predictions and extensive REMC sampling, which allows for the implementation of multiple van der Waals weightings to accommodate different levels of steric clashes and cavity distortions and therefore enhances the robustness of low-resolution docking with predicted protein structures.

Direct coupling of first-principles calculations with replica exchange Monte Carlo sampling of ion disorder in solids

We demonstrate the feasibility of performing sufficient configurational sampling of disordered oxides directly from first-principles without resorting to the use of fitted models such as cluster expansion. This is achieved by harnessing the power of modern-day cluster supercomputers using the replica exchange Monte Carlo method coupled directly with structural relaxation and energy calculation performed by density functional codes. The idea is applied successfully to the calculation of the temperature-dependence of the degree of inversion in the cation sublattice of MgAl2O4 spinel oxide. The possibility of bypassing fitting models will lead to investigation of disordered systems where cluster expansion is known to perform badly, for example, systems with large lattice deformation due to defects, or systems where long-range interactions dominate such as electrochemical interfaces.

Coarse-Grained Modeling of the Interplay between Secondary Structure Propensities and Protein Fold Assembly.

We recently developed a new coarse-grained model of protein structure and dynamics [ Dawid et al. J. Chem. Theory Comput. 2017 , 13 ( 11 ), 5766 - 5779 ]. The model assumed a single bead representation of amino acid residues, where positions of such united residues were defined by centers of mass of four amino acid fragments. Replica exchange Monte Carlo sampling of the model chains provided good pictures of modeled structures and their dynamics. In its generic form the statistical knowledge-based force field of the model has been dedicated for single-domain globular proteins. Sequence-specific interactions are defined by three-letter secondary structure data. In the present work we demonstrate that different assignments and/or predictions of secondary structures are usually sufficient for enforcing cooperative formation of native-like folds of SURPASS chains for the majority of single-domain globular proteins. Simulations of native-like structure assembly for a representative set of globular proteins have shown that the accuracy of secondary structure data is usually not crucial for model performance, although some specific errors can strongly distort the obtained three-dimensional structures.

SURPASS Low-Resolution Coarse-Grained Protein Modeling.

Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex interaction patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed.

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein\u2013small molecule docking

BackgroundIn this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein–small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange.ResultsThe Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies.ConclusionsOur findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein–small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

Coarse-Grained Modeling of Mucus Barrier Properties

We designed a simple coarse-grained model of the glycocalyx layer, or adhesive mucus layer (AML), covered by mucus gel (luminal mucus layer) using a polymer lattice model and stochastic sampling (replica exchange Monte Carlo) for canonical ensemble simulations. We assumed that mucin MUC16 is responsible for the structural properties of the AML. Other mucins that are much smaller in size and less relevant for layer structure formation were not included. We further assumed that the system was in quasi-equilibrium. For systems with surface coverage and concentrations of model mucins mimicking physiological conditions, we determined the equilibrium distribution of inert nanoparticles within the mucus layers using an efficient replica exchange Monte Carlo sampling procedure. The results show that the two mucus layers penetrate each other only marginally, and the bilayer imposes a strong barrier for nanoparticles, with the AML layer playing a crucial role in the mucus barrier.

Simulation of Coarse-Grained Protein-Protein Interactions with Graphics Processing Units.

We report a hybrid parallel central and graphics processing units (CPU-GPU) implementation of a coarse-grained model for replica exchange Monte Carlo (REMC) simulations of protein assemblies. We describe the design, optimization, validation, and benchmarking of our algorithms, particularly the parallelization strategy, which is specific to the requirements of GPU hardware. Performance evaluation of our hybrid implementation shows scaled speedup as compared to a single-core CPU; reference simulations of small 100 residue proteins have a modest speedup of 4, while large simulations with thousands of residues are up to 1400 times faster. Importantly, the combination of coarse-grained models with highly parallel GPU hardware vastly increases the length- and time-scales accessible for protein simulation, making it possible to simulate much larger systems of interacting proteins than have previously been attempted. As a first step toward the simulation of the assembly of an entire viral capsid, we have demonstrated that the chosen coarse-grained model, together with REMC sampling, is capable of identifying the correctly bound structure, for a pair of fragments from the human hepatitis B virus capsid. Our parallel solution can easily be generalized to other interaction functions and other types of macromolecules and has implications for the parallelization of similar N-body problems that require random access lookups.

Protein Folding with a Reduced Model and Inaccurate Short-Range Restraints

Summary: A reduced high-coordination lattice protein model and the Replica Exchange Monte Carlo sampling were employed in de novo folding simulations of a set of representative small proteins. Three distinct situations were analyzed. In the first series of simulations, the folding was controlled purely by the generic force field of the model. In the second, a bias was introduced towards the theoretically predicted secondary structure. Finally, we superimposed soft restraints towards the native-like local conformation of the backbone. The short-range restraints used in these simulations are based on approximate values of ϕ and ψ dihedral angles, which may simulate restraints derived from inaccurate experimental measurements. Incorporating such data into the reduced model required developing a procedure, which transforms the ϕ and ψ coordinates into coordinates of the protein alpha carbon trace. It has been shown that such limited data are sufficient for de novo determination of three-dimensional structures of small and topologically not too complex proteins. Protein folding based on secondary structure prediction and simulated torsion angles data.

Theoretical model of prion propagation: A misfolded protein induces misfolding

There is a hypothesis that dangerous diseases such as bovine spongiform encephalopathy, Creutzfeldt-Jakob, Alzheimer's, fatal familial insomnia, and several others are induced by propagation of wrong or misfolded conformations of some vital proteins. If for some reason the misfolded conformations were acquired by many such protein molecules it might lead to a "conformational" disease of the organism. Here, a theoretical model of the molecular mechanism of such a conformational disease is proposed, in which a metastable (or misfolded) form of a protein induces a similar misfolding of another protein molecule (conformational autocatalysis). First, a number of amino acid sequences composed of 32 aa have been designed that fold rapidly into a well defined native-like alpha-helical conformation. From a large number of such sequences a subset of 14 had a specific feature of their energy landscape, a well defined local energy minimum (higher than the global minimum for the alpha-helical fold) corresponding to beta-type structure. Only one of these 14 sequences exhibited a strong autocatalytic tendency to form a beta-sheet dimer capable of further propagation of protofibril-like structure. Simulations were done by using a reduced, although of high resolution, protein model and the replica exchange Monte Carlo sampling procedure.

Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models.

To predict the tertiary structure of full-length sequences of all targets in CASP6, regardless of their potential category (from easy comparative modeling to fold recognition to apparent new folds) we used a novel combination of two very different approaches developed independently in our laboratories, which ranked quite well in different categories in CASP5. First, the GeneSilico metaserver was used to identify domains, predict secondary structure, and generate fold recognition (FR) alignments, which were converted to full-atom models using the "FRankenstein's Monster" approach for comparative modeling (CM) by recombination of protein fragments. Additional models generated "de novo" by fully automated servers were obtained from the CASP website. All these models were evaluated by VERIFY3D, and residues with scores better than 0.2 were used as a source of spatial restraints. Second, a new implementation of the lattice-based protein modeling tool CABS was used to carry out folding guided by the above-mentioned restraints with the Replica Exchange Monte Carlo sampling technique. Decoys generated in the course of simulation were subject to the average linkage hierarchical clustering. For a representative decoy from each cluster, a full-atom model was rebuilt. Finally, five models were selected for submission based on combination of various criteria, including the size, density, and average energy of the corresponding cluster, and the visual evaluation of the full-atom structures and their relationship to the original templates. The combination of FRankenstein and CABS was one of the best-performing algorithms over all categories in CASP6 (it is important to note that our human intervention was very limited, and all steps in our method can be easily automated). We were able to generate a number of very good models, especially in the Comparative Modeling and New Folds categories. Frequently, the best models were closer to the native structure than any of the templates used. The main problem we encountered was in the ranking of the final models (the only step of significant human intervention), due to the insufficient computational power, which precluded the possibility of full-atom refinement and energy-based evaluation.

A COMBINATION OF REPLICA EXCHANGE MONTE CARLO AND ENERGY LANDSCAPE PAVING ALGORITHMS TO INCREASE THE EFFECTIVENESS OF CONFORMATIONAL SAMPLING

A combination of replica exchange Monte Carlo sampling techniques and energy landscape paving approach is presented. This hybrid algorithm combines the features of the energy landscape paving (ELP) and replica exchange methods (REM). I have tested its performance in studying the low-energy conformations of the benchmark peptide Met-enkephalin.

A new combination of replica exchange Monte Carlo and histogram analysis for protein folding and thermodynamics

A novel combination of replica exchange Monte Carlo sampling techniques with a histogram analysis approach is developed and applied to study the thermodynamics of the folding transition in a face-centered cubic lattice chain protein model. Sequences of hydrophobic (H) and polar (P) topology residues were designed to fold into various β-barrel type proteins. The interaction scheme includes the short-range propensity to form extended conformations, residue-dependent long-range contact potentials, and orientation-dependent hydrogen bonds. Weakly cooperative folding transitions could be observed for properly designed HP. (Hydrophobic and polar residue sequences with cooperative long-range interaction methods were proposed and tested.) Based on the study of these systems, the computational cost of such an approach is many times less than the cost of other Monte Carlo procedures. This opens up the possibility for efficient studies of the folding thermodynamics of more detailed protein models.