scholarly journals Protocols for fast simulations of protein structure flexibility using CABS-flex and SURPASS

2019 ◽  
Author(s):  
Aleksandra Badaczewska-Dawid ◽  
Andrzej Kolinski ◽  
Sebastian Kmiecik
Keyword(s):  
Protein Function ◽  
Md Simulation ◽  
Protein Structures ◽  
Md Simulations ◽  
System Size ◽  
Coarse Grained ◽  
Simulation Tools ◽  
Speed Up ◽  
Structure Flexibility ◽  
Computational Resources

SummaryConformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods, since experimental characterization can be difficult. Depending on protein system size; computational tools may require large computational resources or significant simplifications in the modeled systems to speed-up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: probing the binding pathway of dasatinib to Src-kinase

2020 ◽  
Vol 36 (18) ◽  
pp. 4714-4720
Author(s):  
Farzin Sohraby ◽  
Mostafa Javaheri Moghadam ◽  
Masoud Aliyar ◽  
Hassan Aryapour
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Md Simulation ◽  
Src Kinase ◽  
Deep Understanding ◽  
Md Simulations ◽  
Binding Mode ◽  
Supplementary Information ◽  
Computational Resources ◽  
Binding Mechanisms

Abstract Summary Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein–ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding’s process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. Availability and implementation The links of the tutorial and technical documents are accessible in the article. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Nanoindentation simulations for large atomic systems : an analysis of molecular dynamics and bridged finite element - molecular dynamics methodologies

2021 ◽  
Author(s):  
Jonathan Vandersluis
Keyword(s):  
Molecular Dynamics ◽  
Finite Element ◽  
Md Simulation ◽  
Computational Simulation ◽  
Md Simulations ◽  
Single Step ◽  
Depth Interval ◽  
Simulation Tool ◽  
Custom Made ◽  
Computational Resources

This thesis develops a molecular dynamics (MD) custom made computational tool to perform nanoindentation simulations on copper nanomaterials, a Face Centred Cubic (FCC) metal. The Embedding Atom Method (EAM) is used to model the interatomic forces with the substrate. Further, a bridged finite element - molecular dynamics (FE-MD) simulation tool is also adapted to perform nanoindentation experimentation. Using this bridged FE-MD simulation tool, nanoindentations are performed much more effectively than the MD simulations while saving substantial computational simulation time. While the MD simulation experienced difficulties capturing the behaviour of the system during indentation especially at faster indentation speeds, the bridged FE-MD method is capable of reaching a state of equilibrium within a single step for each indentation depth interval analyzed throughout the nanoindentation. Although the hardness values for these simulations cannot be obtained without larger scale simulations using more powerful computational resources, the simulations provide insight into the behaviour of the copper nanomaterial during nanoindentation. As a result, it is clear that the bridged FE-MD nanoindentation tool is much more effective for executing nanoindentation simulations than the traditional MD methodologies.

scholarly journals Forging tools for refining predicted protein structures

2019 ◽  
Vol 116 (19) ◽  
pp. 9400-9409 ◽  
Author(s):  
Xingcheng Lin ◽  
Nicholas P. Schafer ◽  
Wei Lu ◽  
Shikai Jin ◽  
Xun Chen ◽  
...  
Keyword(s):  
Protein Structures ◽  
Low Frequency ◽  
Coarse Grained ◽  
Side Chain ◽  
X Ray Diffraction ◽  
Collective Variables ◽  
Coarse Grained Model ◽  
Mechanical Deformations ◽  
Speed Up ◽  
Dynamics Simulations

Refining predicted protein structures with all-atom molecular dynamics simulations is one route to producing, entirely by computational means, structural models of proteins that rival in quality those that are determined by X-ray diffraction experiments. Slow rearrangements within the compact folded state, however, make routine refinement of predicted structures by unrestrained simulations infeasible. In this work, we draw inspiration from the fields of metallurgy and blacksmithing, where practitioners have worked out practical means of controlling equilibration by mechanically deforming their samples. We describe a two-step refinement procedure that involves identifying collective variables for mechanical deformations using a coarse-grained model and then sampling along these deformation modes in all-atom simulations. Identifying those low-frequency collective modes that change the contact map the most proves to be an effective strategy for choosing which deformations to use for sampling. The method is tested on 20 refinement targets from the CASP12 competition and is found to induce large structural rearrangements that drive the structures closer to the experimentally determined structures during relatively short all-atom simulations of 50 ns. By examining the accuracy of side-chain rotamer states in subensembles of structures that have varying degrees of similarity to the experimental structure, we identified the reorientation of aromatic side chains as a step that remains slow even when encouraging global mechanical deformations in the all-atom simulations. Reducing the side-chain rotamer isomerization barriers in the all-atom force field is found to further speed up refinement.

CLUSTERING MULTI-DOMAIN PROTEIN STRUCTURES IN THE ESSENTIAL DYNAMICS SUBSPACE

2013 ◽  
Vol 12 (08) ◽  
pp. 1341008 ◽  
Author(s):  
BIN WEN ◽  
YUNYU SHI ◽  
ZHIYONG ZHANG
Keyword(s):  
Cluster Analysis ◽  
Md Simulation ◽  
Clustering Algorithm ◽  
Protein Structures ◽  
Principal Component ◽  
Md Simulations ◽  
Essential Dynamics ◽  
Ww Domains ◽  
Domain Protein ◽  
Domain Motions

A multi-domain protein is able to exist as equilibrium of different conformations in solution, which may be critical to its biological function. Besides experimental techniques, computational methods like molecular dynamics (MD) simulations are suitable to study inter-domain motions of the protein and sample different conformational states. A MD simulation usually generates a trajectory containing large amount of protein structures, and a post-processing cluster analysis would be necessary to group similar structures into clusters and identify these typical conformations of the multi-domain protein. In this paper, the widely used k-means clustering algorithm is implemented in the protein essential dynamics (ED) subspace defined by principal component analysis on the MD trajectory. Cluster analysis of the formin binding protein 21 (FBP21) tandem WW domains demonstrate that the k-means clustering results by measuring distances between structures in the ED subspace are superior to those by using other metrics like pairwise inter-domain residue distances.

scholarly journals Nanoindentation simulations for large atomic systems : an analysis of molecular dynamics and bridged finite element - molecular dynamics methodologies

2021 ◽  
Author(s):  
Jonathan Vandersluis
Keyword(s):  
Molecular Dynamics ◽  
Finite Element ◽  
Md Simulation ◽  
Computational Simulation ◽  
Md Simulations ◽  
Single Step ◽  
Depth Interval ◽  
Simulation Tool ◽  
Custom Made ◽  
Computational Resources

This thesis develops a molecular dynamics (MD) custom made computational tool to perform nanoindentation simulations on copper nanomaterials, a Face Centred Cubic (FCC) metal. The Embedding Atom Method (EAM) is used to model the interatomic forces with the substrate. Further, a bridged finite element - molecular dynamics (FE-MD) simulation tool is also adapted to perform nanoindentation experimentation. Using this bridged FE-MD simulation tool, nanoindentations are performed much more effectively than the MD simulations while saving substantial computational simulation time. While the MD simulation experienced difficulties capturing the behaviour of the system during indentation especially at faster indentation speeds, the bridged FE-MD method is capable of reaching a state of equilibrium within a single step for each indentation depth interval analyzed throughout the nanoindentation. Although the hardness values for these simulations cannot be obtained without larger scale simulations using more powerful computational resources, the simulations provide insight into the behaviour of the copper nanomaterial during nanoindentation. As a result, it is clear that the bridged FE-MD nanoindentation tool is much more effective for executing nanoindentation simulations than the traditional MD methodologies.

scholarly journals Relative Affinities of Protein-Cholesterol Interactions from Equilibrium Molecular Dynamics Simulations

2021 ◽  
Author(s):  
T. Bertie Ansell ◽  
Luke Curran ◽  
Michael R Horrell ◽  
Tanadet Pipatpolkai ◽  
Suzanne C Letham ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Specific Interaction ◽  
Protein Structures ◽  
Md Simulations ◽  
Lipid Binding ◽  
Coarse Grained ◽  
Binding Affinities ◽  
Interaction Sites

Specific interactions of lipids with membrane proteins contribute to protein stability and function. Multiple lipid interactions surrounding a membrane protein are often identified in molecular dynamics (MD) simulations and are, increasingly, resolved in cryo-EM densities. Determining the relative importance of specific interaction sites is aided by determination of lipid binding affinities by experimental or simulation methods. Here, we develop a method for determining protein-lipid binding affinities from equilibrium coarse-grained MD simulations using binding saturation curves, designed to mimic experimental protocols. We apply this method to directly obtain affinities for cholesterol binding to multiple sites on a range of membrane proteins and compare our results with free energies obtained from density-based equilibrium methods and with potential of mean force calculations, getting good agreement with respect to the ranking of affinities for different sites. Thus, our binding saturation method provides a robust, high-throughput alternative for determining the relative consequence of individual sites seen in e.g. cryo-EM derived membrane protein structures surrounded by a plethora of ancillary lipid densities.

pourRNA—a time- and memory-efficient approach for the guided exploration of RNA energy landscapes

2019 ◽  
Author(s):  
Gregor Entzian ◽  
Martin Raden
Keyword(s):  
Coarse Grained ◽  
Supplementary Information ◽  
Transition Rates ◽  
Folding Kinetics ◽  
Model Quality ◽  
Ribonucleic Acids ◽  
Structure Space ◽  
Folding Dynamics ◽  
Speed Up ◽  
Computational Resources

Abstract Motivation The folding dynamics of ribonucleic acids (RNAs) are typically studied via coarse-grained models of the underlying energy landscape to face the exponential growths of the RNA secondary structure space. Still, studies of exact folding kinetics based on gradient basin abstractions are currently limited to short sequence lengths due to vast memory requirements. In order to compute exact transition rates between gradient basins, state-of-the-art approaches apply global flooding schemes that require to memorize the whole structure space at once. pourRNA tackles this problem via local flooding techniques where memorization is limited to the structure ensembles of individual gradient basins. Results Compared to the only available tool for exact gradient basin-based macro-state transition rates (namely barriers), pourRNA computes the same exact transition rates up to 10 times faster and requires two orders of magnitude less memory for sequences that are still computationally accessible for exhaustive enumeration. Parallelized computation as well as additional heuristics further speed up computations while still producing high-quality transition model approximations. The introduced heuristics enable a guided trade-off between model quality and required computational resources. We introduce and evaluate a macroscopic direct path heuristics to efficiently compute refolding energy barrier estimations for the co-transcriptionally trapped RNA sv11 of length 115 nt. Finally, we also show how pourRNA can be used to identify folding funnels and their respective energetically lowest minima. Availability and implementation pourRNA is freely available at https://github.com/ViennaRNA/pourRNA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Biomolecules ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 482
Author(s):  
Ryo Kanada ◽  
Atsushi Tokuhisa ◽  
Koji Tsuda ◽  
Yasushi Okuno ◽  
Kei Terayama
Keyword(s):  
Active Learning ◽  
Conformational Changes ◽  
Md Simulation ◽  
Md Simulations ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Environmental Sensitivity ◽  
Learning Techniques ◽  
And Behavior

Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies.

A comprehensive in silico study towards understanding the inhibitory mechanism of Lactoperoxidase by Dapsone and Propofol

2020 ◽  
Vol 16 ◽  
Author(s):  
Rameez Jabeer Khan ◽  
Rajat Kumar Jha ◽  
Gizachew Muluneh Amera ◽  
Jayaraman Muthukumaran ◽  
Rashmi Prabha Singh ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Md Simulation ◽  
Binding Free Energy ◽  
Protoporphyrin Ix ◽  
Md Simulations ◽  
Prosthetic Group ◽  
Drug Molecules ◽  
Group A ◽  
Complex Forms ◽  
Covalently Linked

Introduction: Lactoperoxidase (LPO) is a member of mammalian heme peroxidase family and is an enzyme of innate immune system. It possesses a covalently linked heme prosthetic group (a derivative of protoporphyrin IX) in its active site. LPO catalyzes the oxidation of halides and pseudohalides in the presence of hydrogen peroxide (H2O2) and shows a broad range of antimicrobial activity. Methods: In this study, we have used two pharmaceutically important drug molecules, namely dapsone and propofol, which are earlier reported as potent inhibitors of LPO. Whereas the stereochemistry and mode of binding of dapsone and propofol to LPO is still not known because of the lack of the crystal structure of LPO with these two drugs. In order to fill this gap, we utilized molecular docking and molecular dynamics (MD) simulation studies of LPO in native and complex forms with dapsone and propofol. Results: From the docking results, the estimated binding free energy (ΔG) of -9.25 kcal/mol (Ki = 0.16 μM) and -7.05 kcal/mol (Ki = 6.79 μM) was observed for dapsone, and propofol, respectively. The standard error of Auto Dock program is 2.5 kcal/mol; therefore, molecular docking results alone were inconclusive. Conclusion: To further validate the docking results, we performed MD simulation on unbound, and two drugs bounded LPO structures. Interestingly, MD simulations results explained that the structural stability of LPO-Propofol complex was higher than LPO-Dapsone complex. The results obtained from this study establish the mode of binding and interaction pattern of the dapsone and propofol to LPO as inhibitors.

scholarly journals Structure of the full-length human Pannexin1 channel and insights into its role in pyroptosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sensen Zhang ◽  
Baolei Yuan ◽  
Jordy Homing Lam ◽  
Jun Zhou ◽  
Xuan Zhou ◽  
...  
Keyword(s):  
Md Simulation ◽  
Potential Role ◽  
Md Simulations ◽  
Full Length ◽  
Inflammasome Activation ◽  
Simulation Studies ◽  
Cryo Electron Microscopy ◽  
Gating Mechanism ◽  
Atp Efflux

AbstractPannexin1 (PANX1) is a large-pore ATP efflux channel with a broad distribution, which allows the exchange of molecules and ions smaller than 1 kDa between the cytoplasm and extracellular space. In this study, we show that in human macrophages PANX1 expression is upregulated by diverse stimuli that promote pyroptosis, which is reminiscent of the previously reported lipopolysaccharide-induced upregulation of PANX1 during inflammasome activation. To further elucidate the function of PANX1, we propose the full-length human Pannexin1 (hPANX1) model through cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulation studies, establishing hPANX1 as a homo-heptamer and revealing that both the N-termini and C-termini protrude deeply into the channel pore funnel. MD simulations also elucidate key energetic features governing the channel that lay a foundation to understand the channel gating mechanism. Structural analyses, functional characterizations, and computational studies support the current hPANX1-MD model, suggesting the potential role of hPANX1 in pyroptosis during immune responses.

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