Simulated Annealing Molecular Dynamics Research Articles

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer.

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Elucidating the effects of emulsification on the thermal performances of palm oil-based phase change materials by molecular dynamics simulations

The use of emulsification to tailor phase change materials (PCMs) for specific performances is essential for the efficient thermal management systems. In this study, the thermal performances of PCMs, prepared from hydroprocessing of crude palm oil and the addition of monoacylglycerols (MAGs) as an emulsifier, are investigated by using differential scanning calorimetry (DSC) and molecular dynamics simulated annealing. While the onset melting temperatures are unaffected by the emulsification, the peak as well as end-set melting temperatures are clearly enhanced with the increasing of MAGs. Therefore, the emulsification of PCMs by MAGs, resulting in the positive shift and the broadening of the absorption peak, could raise the operating temperature of the palm oil-based PCMs. Comparisons between thermal properties quantified by the DSC and the molecular simulations clearly demonstrated that the enhanced thermal performances and degradations of the palm oil-based PCMs are correlated with the molecular interactions between the MAGs and the alkanes (ALKs) of the palm oil derivatives (paraffin). The stronger ALKs-ALKs and MAGs-MAGs interactions relative to the ALKs-MAGs interactions provided explanations for both the faster crystallization rates of the emulsified PCMs and melting temperature drop after the second heating, which shed some light on further emulsification strategy to control the operation temperature and the qualities of the PCMs.

Molecular Dynamics Simulation of Biomimetic Biphasic Calcium Phosphate Nanoparticles.

Biphasic calcium phosphate (BCP) is used as a bone substitute and bone tissue repair material due to its better control over bioactivity and biodegradability. It is crucial to stabilize the implanted biomaterial while promoting bone ingrowth. However, a lack of standard experimental and theoretical protocols to characterize the physicochemical properties of BCP limits the optimization of its composition and properties. Computational simulations can help us better to learn BCP at a nanoscale level. Here, the Voronoi tessellation method was combined with simulated annealing molecular dynamics to construct BCP nanoparticle models of different sizes, which were used to understand the physicochemical properties of BCP (e.g., melting point, infrared spectrum, and mechanical properties). We observed a ∼20 to 30 Å layer of calcium-deficient hydroxyapatite at the HAP/β-TCP interface due to particle migration, which may contribute to BCP stability. The BCP model may stimulate further research into BCP ceramics and multiphasic ceramics. Moreover, our study may facilitate the optimization of compositions of BCP-based biomaterials.

A combined computational and experimental approach predicts thrombin adsorption to zeolites

Robust protein-nanomaterial surface analysis is important, but also a challenge. Thrombin plays an important role in the coagulant activity of protein corona mediated by Ca2+ ion exchanged zeolites. However, the mechanism for this modulation remains unresolved. In this study, we proposed a combined computational and experimental approach to determine the adsorbed sites and orientations of thrombin binding to Ca2+ -exchanged LTA-type (CaA) zeolite. Specifically, fourteen ensembles of simulated annealing molecular dynamics (SAMD) simulations and experimental surface residues microenvironment analysis were used to reduce the starting orientations needed for further molecular dynamics (MD) simulations. The combined MD simulations and procoagulant activity characterization also reveal the consequent corresponding deactivation of thrombin on CaA zeolite. It is mainly caused by two aspects: (1) the secondary structure of thrombin can change after its adsorption on the CaA zeolite. (2) The positively charged area of thrombin mediates the preferential interaction between thrombin and CaA zeolite. Some thrombin substrate sites are thus blocked by zeolite after its adsorption. This study not only provides a promising method for characterizing the protein-nanoparticle interaction, but also gives an insight into the design and application of zeolite with high procoagulant activity.

In silico method for identifying the key residues in a knotted protein: with MJ0366 as an example.

The knotted proteins are a class of rare but biologically important proteins, due to the special topology of their native structure. Here we present a simple in silico method to identify the key residues for knotting and unknotting in a knotted protein, using the trefoil protein MJ0366 as an example. We first simulate the folding process via the annealing molecular dynamics (AMD) simulations in the coarse-grained "Go"-like model. From the folding trajectories, we monitor the knotting process using the quantity "length of knot tails". In the meantime, we analyze the evolution of the local geometry of the Cα trace with the help of the Discrete Frenet Frame (DFF). We identify the key residues by correlating the local geometry at each residue with the variable "length of knot tails" in the folding process, where a higher correlation coefficient indicates that the residue is more important for knotting. We validate our method by comparing with the experimental results in the literature. With the same method, we further predict the key residues for unknotting MJ0366 using the AMD simulations in both the coarse-grained "Go"-like model and all-atom (AA) force field model, respectively. We find that the key residues for unknotting are partially overlapped with those for knotting, indicating that the pathways for unknotting and knotting are generally similar except for the existence of some non-native contact interactions in the unknotting process. This in silico method can provide a new insight for understanding the knotting and unknotting processes of a knotted protein.

Molecular Simulations of Surfactant Adsorption on Iron Oxide from Hydrocarbon Solvents.

The performance of organic friction modifiers (OFMs) depends on their ability to adsorb onto surfaces and form protective monolayers. Understanding the relationship between OFM concentration in the base oil and the resulting surface coverage is important for improving lubricant formulations. Here, we use molecular dynamics (MD) simulations to study the adsorption of three OFMs─stearic acid (SA), glycerol monoostearate (GMS), and glycerol monooleate (GMO)─onto a hematite surface from two hydrocarbon solvents─n-hexadecane and poly(α-olefin) (PAO). We calculate the potential of mean force of the adsorption process using the adaptive biasing force algorithm, and the adsorption strength increases in the order SA < GMS < GMO. We estimate the minimum area occupied by OFM molecules on the surface using annealing MD simulations and obtained a similar hard-disk area for GMS and GMO but a lower value for SA. Using the MD results, we determine the adsorption isotherms using the molecular thermodynamic theory (MTT), which agree well with one previous experimental data set for SA on hematite. For two other experimental data sets for SA, lateral interactions between surfactant molecules need to be accounted for within the MTT framework. SA forms monolayers with lower surface coverage than GMO and GMS at low concentrations but also has the highest plateau coverage. We validate the adsorption energies from the MD simulations using high-frequency reciprocating rig friction experiments with different concentrations of the OFMs in PAO. For OFMs with saturated tailgroups (SA and GMS), we obtain good agreement between the simulations and the experiments. The results deviate for OFMs containing Z-unsaturated tailgroups (GMO) due to the additional steric hindrance, which is not accounted for in the current simulation framework. This study demonstrates that MD simulations, alongside MTT, are an accurate and efficient tool to predict adsorption isotherms at solid-liquid interfaces.

Elucidating doxycycline loading and release performance of imprinted hydrogels with different cross-linker concentrations: a computational and experimental study

Effective non-covalent molecular imprinting on a polymer depends on the extent of non-bonded interactions between the template and other molecules before polymerization. Here, we first determine functional monomers that can yield a doxycycline-imprinted hydrogel based on the hydrogen bond interactions at the prepolymerization step, revealed by molecular dynamics (MD) simulations, molecular docking, and simulated annealing methods. Then, acrylic acid (AA)-based doxycycline (DOX) imprinted (MIP) and non-imprinted (NIP) hydrogels are synthesized in cross-linker ethylene glycol dimethacrylate (EGDMA) ratios of 1.0, 1.5, 2.0, and 3.0 mol%. Here, molecularly imprinted polymer with 3.0 mol% EGDMA has the highest imprinting factor (1.58) and best controlled drug release performance. At this point, full-atom MD simulations of DOX–AA solutions at different EGDMA concentrations reveal that AA and EGDMA compete to interact with DOX. However, at 3.0 mol% EGDMA, AA attains numerous stable hydrogen bond interactions with the drug. This study demonstrates that the concentration of the cross-linker and functional monomer can be adjusted to increase the success of imprinting, where the interplay between these two parameters can be successfully revealed by MD simulations.Supplementary informationThe online version contains supplementary material available at 10.1007/s10965-021-02740-6.

Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme “SAUA-FFR”. The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

Hi-CO: 3D genome structure analysis with nucleosome resolution.

The nucleosome is the basic organizational unit of the genome. The folding structure of nucleosomes is closely related to genome functions, and has been reported to be in dynamic interplay with binding of various nuclear proteins to genomic loci. Here, we describe our high-throughput chromosome conformation capture with nucleosome orientation (Hi-CO) technology to derive 3D nucleosome positions with their orientations at every genomic locus in the nucleus. This technology consists of an experimental procedure for nucleosome proximity analysis and a computational procedure for 3D modeling. The experimental procedure is based on an improved method of high-throughput chromosome conformation capture (Hi-C) analysis. Whereas conventional Hi-C allows spatial proximity analysis among genomic loci with 1-10 kbp resolution, our Hi-CO allows proximity analysis among DNA entry or exit points at every nucleosome locus. This analysis is realized by carrying out ligations among the entry/exit points in every nucleosome in a micrococcal-nuclease-fragmented genome, and by quantifying frequencies of ligation products with next-generation sequencing. Our protocol has enabled this analysis by cleanly excluding unwanted non-ligation products that are abundant owing to the frequent genome fragmentation by micrococcal nuclease. The computational procedure is based on simulated annealing-molecular dynamics, which allows determination of optimized 3D positions and orientations of every nucleosome that satisfies the proximity ligation data sufficiently well. Typically, examination of the Saccharomyces cerevisiae genome with 130 million sequencing reads facilitates analysis of a total of 66,360 nucleosome loci with 6.8 nm resolution. The technique requires 2-3 weeks for sequencing library preparation and 2 weeks for simulation.

A molecular dynamics study on the local mobility of cis-polyisoprene within the mixed glassy/rubbery states induced by graphene planes

The glass transition behavior of a simulated model consisting of cis-1,4-polyisoprene nanofilm confined within parallel graphene planes was visualized through chain mobility statistics. Firstly, simulated annealing molecular dynamics (MD) simulations were performed for a system of bulk polyisoprene and the polyisoprene nanofilm within parallel graphene planes at two different gap sizes. The glass transition temperature (T g) and the critical density of bulk polyisoprene were then calculated. The critical density was used to compare with the local density of the confined polyisoprene segments. The mixed glassy/rubbery states were found at temperatures higher than the bulk T g. It was also shown that bulk modulus was increased when graphene planes are present and was further increased when the gap between graphene planes became narrow, and a ‘glassy bridge’ was formed by the highly confined nanofilm with highly-ordered polymer chains. Then, local mobility of each polyisoprene chain segment was quantified through the square displacement (SD) after a 1 ns motion. Local glass transition within any region was determined by whether local SD was lower than the critical mean square displacement (MSD0) of bulk polyisoprene, which was in the ‘glassy regime.’ The analysis results showed that decreasing the temperature and reducing the gap between graphene planes increased the proportion of chain segments in the glassy regime and were in concurrence with the increased bulk modulus.

De Novo Designed Heterochiral Blue Fluorescent Protein.

Diversification of chain stereochemistry offers a tremendous increase in protein design space. We have designed a minimal fluorescent protein, pregnant with β-(1-azulenyl)-l-alanine in the hydrophobic core of a heterotactic protein scaffold, employing automated design tools such as automated repetitive simulated annealing molecular dynamics and IDeAS. The de novo designed heterochiral protein can be selectively excited at 342 nm, quite distant from the intrinsic fluorophore, and emits in the blue region. The structure and stability of the designed proteins were evaluated by established spectroscopic and calorimetric methods.

Evidence for protein misfolding in the presence of nanoplastics

AbstractThe possible effect of plastic nanoparticles of waste origin on biological systems is still unclear and could pose a severe threat. Model studies at the molecular level are urgently needed in order to help reveal the interplay between these particles and biological systems, and thereby to indicate the direction of further research. In the present study, simulated annealing molecular dynamics is adjusted and applied to generate an array of conformations for a sample peptide oligoalanine possibly binding to polyethylene and nylon 6,6 nanoplastics. The resulting structures, with a diameter of up to 5 nm, were investigated with the aid of static quantum chemical calculations. The obtained data unequivocally show that both plastic nanoparticles influence the relative stability of α‐helix, β‐hairpin, and other conformations strongly. Polyethylene nanoparticle increases the stability of the helical foldamer. Nylon 6,6 nanoplastic offers strong plastic‐peptide interactions on its surface, making the unfolding of the peptide thermodynamically highly favorable. These results further underscore that nanoplastics can do significant molecular‐level damage to living organisms via facilitating the misfolding and denaturation of proteins. Furthermore, it is apparent that plastics can have very different effects on living matter depending upon their composition, and hence experiments with any single kind of plastics (eg, polystyrene) should not be considered generally valid for all nanoplastics.

EnzyDock: Protein–Ligand Docking of Multiple Reactive States along a Reaction Coordinate in Enzymes

Enzymes play a pivotal role in all biological systems. These biomachines are the most effective catalysts known, dramatically enhancing the rate of reactions by more than 10 orders of magnitude relative to the uncatalyzed reactions in solution. Predicting the correct, mechanistically appropriate binding modes for substrate and product, as well as all reaction intermediates and transition states, along a reaction pathway is immensely challenging and remains an unsolved problem. In the present work, we developed an effective methodology for identifying probable binding modes of multiple ligand states along a reaction coordinate in an enzyme active site. The program is called EnzyDock and is a CHARMM-based multistate consensus docking program that includes a series of protocols to predict the chemically relevant orientation of substrate, reaction intermediates, transition states, product, and inhibitors. EnzyDock is based on simulated annealing molecular dynamics and Monte Carlo sampling and allows ligand, as well as enzyme side-chain and backbone flexibility. The program can employ many user-defined constraints and restraints and classical force field potentials, as well as a range of hybrid quantum mechanics-molecular mechanics potentials. Herein, we apply EnzyDock to several different kinds of problems. First, we study two terpene synthase reactions, namely bornyl diphosphate synthase and the bacterial diterpene synthase CotB2. Second, we use EnzyDock to predict reaction coordinate states in a pair of Diels-Alder reactions in the enzymes spirotetronate AbyU and LepI. Third, we study a couple of racemases: the cofactor-dependent serine racemase and the cofactor independent proline racemase. Finally, we study several cases of covalent docking involving the Michael addition reaction. For all systems we predict binding modes that are consistent with available experimental observations, as well as with theoretical modeling studies from the literature. EnzyDock provides a platform for generating mechanistic insight into enzyme reactions, useful and reliable starting points for in-depth multiscale modeling projects, and rational design of noncovalent and covalent enzyme inhibitors.

Sub-nucleosomal Genome Structure Reveals Distinct Nucleosome Folding Motifs

Elucidating the global and local rules that govern genome-wide, hierarchical chromatin architecture remains a critical challenge. Current high-throughput chromosome conformation capture (Hi-C) technologies have identified large-scale chromatin structural motifs, such as topologically associating domains and looping. However, structural rules at the smallest or nucleosome scale remain poorly understood. Here, we coupled nucleosome-resolved Hi-C technology with simulated annealing-molecular dynamics (SA-MD) simulation to reveal 3D spatial distributions of nucleosomes and their genome-wide orientation in chromatin. Our method, called Hi-CO, revealed distinct nucleosome folding motifs across the yeast genome. Our results uncovered two types of basic secondary structural motifs in nucleosome folding: α-tetrahedron and β-rhombus analogous to α helix and β sheet motifs in protein folding. Using mutants and cell-cycle-synchronized cells, we further uncovered motifs with specific nucleosome positioning and orientation coupled to epigenetic features at individual loci. By illuminating molecular-level structure-function relationships in eukaryotic chromatin, our findings establish organizational principles of nucleosome folding.

Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar IC50s.

Automated design evolution of stereochemically randomized protein foldamers

Diversification of chain stereochemistry opens up the possibilities of an ‘in principle’ increase in the design space of proteins. This huge increase in the sequence and consequent structural variation is aimed at the generation of smart materials. To diversify protein structure stereochemically, we introduced L- and D-α-amino acids as the design alphabet. With a sequence design algorithm, we explored the usage of specific variables such as chirality and the sequence of this alphabet in independent steps. With molecular dynamics, we folded stereochemically diverse homopolypeptides and evaluated their ‘fitness’ for possible design as protein-like foldamers. We propose a fitness function to prune the most optimal fold among 1000 structures simulated with an automated repetitive simulated annealing molecular dynamics (AR-SAMD) approach. The highly scored poly-leucine fold with sequence lengths of 24 and 30 amino acids were later sequence-optimized using a Dead End Elimination cum Monte Carlo based optimization tool. This paper demonstrates a novel approach for the de novo design of protein-like foldamers.

Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals.

Elucidation of protein native states from amino acid sequences is a primary computational challenge. Modern computational and experimental methodologies, such as molecular coevolution and chemical cross-linking mass-spectrometry allowed protein structural characterization to previously intangible systems. Despite several independent successful examples, data from these distinct methodologies have not been systematically studied in conjunction. One challenge of structural inference using coevolution is that it is limited to sequence fragments within a conserved and unique domain for which sufficient sequence datasets are available. Therefore, coupling coevolutionary data with complimentary distance constraints from orthogonal sources can provide additional precision to structure prediction methodologies. In this work, we present a methodology to combine residue interaction data obtained from coevolutionary information and cross-linking/mass spectrometry distance constraints in order to identify functional states of proteins. Using a combination of structure-based models (SBMs) with optimized Gaussian-like potentials, secondary structure estimation and simulated annealing molecular dynamics, we provide an automated methodology to integrate constraint data from diverse sources in order to elucidate the native conformation of full protein systems with distinct complexity and structural topologies. We show that cross-linking mass spectrometry constraints improve the structure predictions obtained from SBMs and coevolution signals, and that the constraints obtained by each method have a useful degree of complementarity that promotes enhanced fold estimates. Scripts and procedures to implement the methodology presented herein are available at https://github.com/mcubeg/DCAXL. Supplementary data are available at Bioinformatics online.

Evaluating the peptide structure prediction capabilities of a purely ab-initio method.

DEEPSAM is a relatively new global optimization algorithm aimed to predict the structure of bio-molecules from sequence, without any additional preliminary assumption. It is an evolutionary algorithm whose mutation operators are built by hybridizing the diffusion equation method, molecular dynamics simulated annealing, and a quasi-Newton local minimization method. The goal of this study was to evaluate the structure prediction capabilities of DEEPSAM by running it upon NMR structures of linear peptides (10-20 residues). The results indicate that DEEPSAM successfully predicted the conformations of these peptides, using modest computing resources.

Development of a molecularly imprinted polymer for a pharmaceutical impurity in supercritical CO2: Rational design using computational approach

Supercritical fluid technology is a green and promising alternative for the development of molecularly imprinted polymers. These affinity polymers are obtained ready-to-use, without organic solvent residues, and with controlled properties which could prompt their use in several areas. In this work, a molecularly imprinted polymer (MIP) with affinity for a model pharmaceutical impurity, acetamide, was pre-designed using a user-friendly computational approach in order to optimize MIP synthesis in supercritical CO2. Molecular Modelling was performed using SYBYL™ software, introducing for the first time CO2 as solvent in the rational design of MIPs. A virtual library of functional monomers was created and screened against acetamide. The monomers giving the highest binding energy were selected and used in a simulated annealing (molecular dynamics) process to investigate their interaction with the template acetamide in the presence of CO2 as the porogen. Itaconic acid and 2-hydroxyethyl methacrylate were selected as the best monomers to interact with acetamide and the molar ratios generated were used in the MIP synthesis in supercritical carbon dioxide. Binding and selectivity experiments were performed to evaluate the affinity performance of the polymers. The experimental results indicate that itaconic acid-MIP, as predicted by SYBYL™, has higher affinity and selectivity to acetamide, highlighting the value of this computational tool in MIP optimization using supercritical fluid technology.

Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling.

Ligand-based pharmacophore modeling requires long list of inhibitors, while pharmacophores based on single ligand-receptor crystallographic structure can be too restricted or promiscuous. This prompted us to combine simulated annealing molecular dynamics (SAMD) with ligand-receptor contacts analysis as means to construct pharmacophore model(s) from single ligand-receptor complex. Ligand-receptor contacts that survive numerous heating-cooling SAMD cycles are considered critical and are used to guide pharmacophore development. This methodology was implemented to develop pharmacophores for acetylcholinesterase and protein kinase C-θ. The resulting models were validated by receiver-operating characteristic analysis and in vitro bioassay. Assay identified four new protein kinase C-θ inhibitors among captured hits, two of which exhibited nanomolar potencies. The results illustrate the ability of the new method to extract valid pharmacophores from single ligand-protein complex.