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Статті в журналах з теми "Proteins Molecular Dynamics Computational Biophysics"
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Liang, Zhongjie, Gennady M. Verkhivker, and Guang Hu. "Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications." Briefings in Bioinformatics 21, no. 3 (March 21, 2019): 815–35. http://dx.doi.org/10.1093/bib/bbz029.
Повний текст джерелаАнотація:
Abstract Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.
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Yeggoni, Daniel Pushparaju, Aparna Rachamallu та Rajagopal Subramanyam. "A comparative binding mechanism between human serum albumin and α-1-acid glycoprotein with corilagin: biophysical and computational approach". RSC Advances 6, № 46 (2016): 40225–37. http://dx.doi.org/10.1039/c6ra06837e.
Повний текст джерелаАнотація:
The interaction between corilagin and serum proteins was studied by biophysical and molecular dynamics techniques which in turn provides valuable information about the interaction of phytochemical corilagin with serum proteins.
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Bardhan, Jaydeep P. "Gradient models in molecular biophysics: progress, challenges, opportunities." Journal of the Mechanical Behavior of Materials 22, no. 5-6 (December 1, 2013): 169–84. http://dx.doi.org/10.1515/jmbm-2013-0024.
Повний текст джерелаАнотація:
AbstractIn the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g., molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding nonlocal dielectric response. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain, and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost 40 years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The review concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.
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Ren, Pengyu, Jaehun Chun, Dennis G. Thomas, Michael J. Schnieders, Marcelo Marucho, Jiajing Zhang, and Nathan A. Baker. "Biomolecular electrostatics and solvation: a computational perspective." Quarterly Reviews of Biophysics 45, no. 4 (November 2012): 427–91. http://dx.doi.org/10.1017/s003358351200011x.
Повний текст джерелаАнотація:
AbstractAn understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.
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Molinski, Steven V., Zoltán Bozóky, Surtaj H. Iram, and Saumel Ahmadi. "Biophysical Approaches Facilitate Computational Drug Discovery for ATP-Binding Cassette Proteins." International Journal of Medicinal Chemistry 2017 (March 19, 2017): 1–9. http://dx.doi.org/10.1155/2017/1529402.
Повний текст джерелаАнотація:
Although membrane proteins represent most therapeutically relevant drug targets, the availability of atomic resolution structures for this class of proteins has been limited. Structural characterization has been hampered by the biophysical nature of these polytopic transporters, receptors, and channels, and recent innovations to in vitro techniques aim to mitigate these challenges. One such class of membrane proteins, the ATP-binding cassette (ABC) superfamily, are broadly expressed throughout the human body, required for normal physiology and disease-causing when mutated, yet lacks sufficient structural representation in the Protein Data Bank. However, recent improvements to biophysical techniques (e.g., cryo-electron microscopy) have allowed for previously “hard-to-study” ABC proteins to be characterized at high resolution, providing insight into molecular mechanisms-of-action as well as revealing novel druggable sites for therapy design. These new advances provide ample opportunity for computational methods (e.g., virtual screening, molecular dynamics simulations, and structure-based drug design) to catalyze the discovery of novel small molecule therapeutics that can be easily translated from computer to bench and subsequently to the patient’s bedside. In this review, we explore the utility of recent advances in biophysical methods coupled with well-established in silico techniques towards drug development for diseases caused by dysfunctional ABC proteins.
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Moffett, Alexander S., and Diwakar Shukla. "Using molecular simulation to explore the nanoscale dynamics of the plant kinome." Biochemical Journal 475, no. 5 (March 9, 2018): 905–21. http://dx.doi.org/10.1042/bcj20170299.
Повний текст джерелаАнотація:
Eukaryotic protein kinases (PKs) are a large family of proteins critical for cellular response to external signals, acting as molecular switches. PKs propagate biochemical signals by catalyzing phosphorylation of other proteins, including other PKs, which can undergo conformational changes upon phosphorylation and catalyze further phosphorylations. Although PKs have been studied thoroughly across the domains of life, the structures of these proteins are sparsely understood in numerous groups of organisms, including plants. In addition to efforts towards determining crystal structures of PKs, research on human PKs has incorporated molecular dynamics (MD) simulations to study the conformational dynamics underlying the switching of PK function. This approach of experimental structural biology coupled with computational biophysics has led to improved understanding of how PKs become catalytically active and why mutations cause pathological PK behavior, at spatial and temporal resolutions inaccessible to current experimental methods alone. In this review, we argue for the value of applying MD simulation to plant PKs. We review the basics of MD simulation methodology, the successes achieved through MD simulation in animal PKs, and current work on plant PKs using MD simulation. We conclude with a discussion of the future of MD simulations and plant PKs, arguing for the importance of molecular simulation in the future of plant PK research.
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Ramos, Javier, Juan Francisco Vega, Victor Cruz, Eduardo Sanchez-Sanchez, Javier Cortes, and Javier Martinez-Salazar. "Hydrodynamic and Electrophoretic Properties of Trastuzumab/HER2 Extracellular Domain Complexes as Revealed by Experimental Techniques and Computational Simulations." International Journal of Molecular Sciences 20, no. 5 (March 1, 2019): 1076. http://dx.doi.org/10.3390/ijms20051076.
Повний текст джерелаАнотація:
The combination of hydrodynamic and electrophoretic experiments and computer simulations is a powerful approach to study the interaction between proteins. In this work, we present hydrodynamic and electrophoretic experiments in an aqueous solution along with molecular dynamics and hydrodynamic modeling to monitor and compute biophysical properties of the interactions between the extracellular domain of the HER2 protein (eHER2) and the monoclonal antibody trastuzumab (TZM). The importance of this system relies on the fact that the overexpression of HER2 protein is related with the poor prognosis breast cancers (HER2++ positives), while the TZM is a monoclonal antibody for the treatment of this cancer. We have found and characterized two different complexes between the TZM and eHER2 proteins (1:1 and 1:2 TZM:eHER2 complexes). The conformational features of these complexes regulate their hydrodynamic and electrostatic properties. Thus, the results indicate a high degree of molecular flexibility in the systems that ultimately leads to higher values of the intrinsic viscosity, as well as lower values of diffusion coefficient than those expected for simple globular proteins. A highly asymmetric charge distribution is detected for the monovalent complex (1:1 complex), which has strong implications in correlations between the experimental electrophoretic mobility and the modeled net charge. In order to understand the dynamics of these systems and the role of the specific domains involved, it is essential to find biophysical correlations between dynamics, macroscopic transport and electrostatic properties. The results should be of general interest for researchers working in this area.
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Oldham, William M., and Heidi E. Hamm. "Structural basis of function in heterotrimeric G proteins." Quarterly Reviews of Biophysics 39, no. 2 (May 2006): 117–66. http://dx.doi.org/10.1017/s0033583506004306.
Повний текст джерелаАнотація:
1. Introduction 22. Heterotrimeric G-protein structure 32.1. G-protein α subunit 32.2. G-protein βγ dimer 82.3. Unique role of Gβ5 in complexes with RGS proteins 92.4. Heterotrimer structure 102.5. Lipid modifications direct membrane association 113. Receptor–G protein complex 113.1. Low affinity interactions between inactive receptors (R) and G proteins 113.2. Receptor activation exposes the high-affinity G-protein binding site 123.3. Receptor–G protein interface 143.4. Structural determinants of receptor–G protein specificity 153.5. Models of the receptor–G protein complex 173.6. Sequential interactions may form the receptor–G protein complex 194. Molecular basis for G-protein activation 194.1. Potential mechanisms of receptor-catalyzed GDP release 204.2. GTP-mediated alteration of the receptor–G protein complex 235. Activation of downstream effector proteins 245.1. Gα interactions with effectors 245.2. Gβγ interactions with effectors and regulatory proteins 266. G-protein inactivation 286.1. Intrinsic GTPase-activity of Gα 286.2. GTPase-activating proteins 307. Novel regulation of G-protein signaling 318. New approaches to study G-protein dynamics 328.1. Nuclear magnetic resonance spectroscopy 328.2. Site-directed labeling techniques 338.3. Mapping allosteric connectivity with computational approaches 348.4. Studies of G-protein function in living cells 369. Conclusions 3710. References 38Heterotrimeric guanine-nucleotide-binding proteins (G proteins) act as molecular switches in signaling pathways by coupling the activation of heptahelical receptors at the cell surface to intracellular responses. In the resting state, the G-protein α subunit (Gα) binds GDP and Gβγ. Receptors activate G proteins by catalyzing GTP for GDP exchange on Gα, leading to a structural change in the Gα(GTP) and Gβγ subunits that allows the activation of a variety of downstream effector proteins. The G protein returns to the resting conformation following GTP hydrolysis and subunit re-association. As the G-protein cycle progresses, the Gα subunit traverses through a series of conformational changes. Crystallographic studies of G proteins in many of these conformations have provided substantial insight into the structures of these proteins, the GTP-induced structural changes in Gα, how these changes may lead to subunit dissociation and allow Gα and Gβγ to activate effector proteins, as well as the mechanism of GTP hydrolysis. However, relatively little is known about the receptor–G protein complex and how this interaction leads to GDP release from Gα. This article reviews the structural determinants of the function of heterotrimeric G proteins in mammalian systems at each point in the G-protein cycle with special emphasis on the mechanism of receptor-mediated G-protein activation. The receptor–G protein complex has proven to be a difficult target for crystallography, and several biophysical and computational approaches are discussed that complement the currently available structural information to improve models of this interaction. Additionally, these approaches enable the study of G-protein dynamics in solution, which is becoming an increasingly appreciated component of all aspects of G-protein signaling.
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Gauthier, Louis, Rémicia Di Franco, and Adrian W. R. Serohijos. "SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes." Bioinformatics 35, no. 20 (March 14, 2019): 4053–62. http://dx.doi.org/10.1093/bioinformatics/btz175.
Повний текст джерелаАнотація:
Abstract Motivation Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution. Results To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution. Availability and implementation Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows. Supplementary information Supplementary data are available at Bioinformatics online.
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Tang, Wai Shing, Gabriel Monteiro da Silva, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, and Lorin Crawford. "A topological data analytic approach for discovering biophysical signatures in protein dynamics." PLOS Computational Biology 18, no. 5 (May 2, 2022): e1010045. http://dx.doi.org/10.1371/journal.pcbi.1010045.
Повний текст джерелаАнотація:
Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.
Дисертації з теми "Proteins Molecular Dynamics Computational Biophysics"
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Rigoli, Marta. "The structure-dynamics-function relation in proteins: bridging all-atom molecular dynamics, experiments, and simplified models." Doctoral thesis, Università degli studi di Trento, 2022. https://hdl.handle.net/11572/330870.
Повний текст джерелаАнотація:
Proteins are one of the most studied biological molecules of the last decades. A great amount of experimental techniques provide to researchers direct or indirect informations on proteins structure and function. In silico simulations can be used as a “computational microscope” giving the possibility to observe protein dynamic properties at atomistic resolution. In this work, various applications of computational methods to biological systems are presented. In particular, all-atom Molecular Dynamics (MD) simulations were employed to investigate the behaviour of proteins at atomstic resolution. The term “Molecular Dynamics” is usually referred to computational methods used for the simulation of classical many-body systems. These techniques are applied to microscopic systems and they represent a powerful approach for the study of physical processes, providing a tool for their interpretation. They have been widely used in the past decades to elucidate a large variety of molecular processes in different fields such as solid state physics, material science, chemistry, biochemistry and biophysics. Here, all-atom MD simulations were employed to observe equilibrium properties of several biologically relevant proteins. This allowed us to direct perform a comparison of molecular mechanisms occurring at the atomistic level as obtained from in silico studies with experimental data, which usually describe processes at larger length and time scales. These MD simulations were also meant as a starting point for the construction of simplified models, as they were processed through coarse-graining procedures to extrapolate crucial systems features, such as informative protein sites, on the basis of information theory approaches. Specifically we studied the dynamics of pembrolizumab, a humanized immunoglobulin of type G4 (IgG4) used as a therapeutic antibody. It is employed for the treatment of lung cancer, melanoma, stomach and head cancer and Hodgkin’s lymphoma. This antibody interacts with the programmed cell death protein 1 (PD-1) receptor, blocking the suppression of the immune response during cancer development. The studied systems are three: the apo state of pembrolizumab, the holo state (i.e. pembrolizumab bound to PD-1) and the glycosylated apo configuration. Each configuration was simulated for 2μs, for a total of 6μs. The analysis of the trajectories was carried out by combining standard structural analysis techniques and information theory-based measures of correlation. From MD trajectories we could extract valuable informations on the connectivity that exists among the structural domains that compose the antibody structure. Moreover, it was possible to infer which regions are involved in the structural rearrangement in the case of the antigen binding. We could observe that the presence of the antigen reduces the conformational variability of the molecule giving a greater stability to it. The second studied system is the P53 protein complex. In this case we focused on the tetramerization domain (TD) region that is composed by 2 identical dimers and has the function of bringing together the four monomers of the p53 complex. Starting from the observation that in case of the mutation of residue R337 several pathologies are developed in humans, we constructed computational models to reproduce the dynamics of the mutants and investigate their behaviour in silico. We performed simulations for a total of 16 μs divided in 8 different cases. In the first part of the study the wild type (WT) protein was compared to the R337C and the R337H mutant in three different protonation states: delta protonated Histidine, epsilon protonated Histidine ad double protonated Histidine. In the second part of the study we highlighted the differences between the WT configuration and three rationally designed mutants: R337D-352D, 337R-D352R, R337D-D352R. In this part of the investigation, the importance of the electrostatic interaction between residues R337 and D352 in the stability of the tetramerization do- main was discussed. Furthermore, we matched the obtained computational results of p53 tetramerization domain with functional experiments in yeasts (performed in collaboration with the CIBIO department) of all the simulated forms. The third simulated protein is the zinc sensing transcriptional repressor (CzrA), an homodimeric protein that binds DNA in Staphylococcus aureus. All-atom MD simulations of two different configurations were performed for a total of 4μs, the first one is the WT apo protein while the second is the WT holo system, where the protein is complexed with two Zn ions. In this case, in addition to standard analysis techniques, we applied the mapping entropy minimization protocol to highlight the most informative protein regions, from the perspective of information theory. Finally, our in silico results were compared to available NMR data of the protein itself.
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Parton, Daniel L. "Pushing the boundaries : molecular dynamics simulations of complex biological membranes." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:7ab91b51-a5ae-46b4-b6dc-3f0dd3f0b477.
Повний текст джерелаАнотація:
A range of simulations have been conducted to investigate the behaviour of a diverse set of complex biological membrane systems. The processes of interest have required simulations over extended time and length scales, but without sacrifice of molecular detail. For this reason, the primary technique used has been coarse-grained molecular dynamics (CG MD) simulations, in which small groups of atoms are combined into lower-resolution CG particles. The increased computational efficiency of this technique has allowed simulations with time scales of microseconds, and length scales of hundreds of nm. The membrane-permeabilizing action of the antimicrobial peptide maculatin 1.1 was investigated. This short α-helical peptide is thought to kill bacteria by permeabilizing the plasma membrane, but the exact mechanism has not been confirmed. Multiscale (CG and atomistic) simulations show that maculatin can insert into membranes to form disordered, water-permeable aggregates, while CG simulations of large numbers of peptides resulted in substantial deformation of lipid vesicles. The simulations imply that both pore-forming and lytic mechanisms are available to maculatin 1.1, and that the predominance of either depends on conditions such as peptide concentration and membrane composition. A generalized study of membrane protein aggregation was conducted via CG simulations of lipid bilayers containing multiple copies of model transmembrane proteins: either α-helical bundles or β-barrels. By varying the lipid tail length and the membrane type (planar bilayer or spherical vesicle), the simulations display protein aggregation ranging from negligible to extensive; they show how this biologically important process is modulated by hydrophobic mismatch, membrane curvature, and the structural class or orientation of the protein. The association of influenza hemagglutinin (HA) with putative lipid rafts was investigated by simulating aggregates of HA in a domain-forming membrane. The CG MD study addressed an important limitation of model membrane experiments by investigating the influence of high local protein concentration on membrane phase behaviour. The simulations showed attenuated diffusion of unsaturated lipids within HA aggregates, leading to spontaneous accumulation of raft-type lipids (saturated lipids and cholesterol). A CG model of the entire influenza viral envelope was constructed in realistic dimensions, comprising the three types of viral envelope protein (HA, neuraminidase and M2) inserted into a large lipid vesicle. The study represents one of the largest near-atomistic simulations of a biological membrane to date. It shows how the high concentration of proteins found in the viral envelope can attenuate formation of lipid domains, which may help to explain why lipid rafts do not form on large scales in vivo.
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Hirst-Dunton, Thomas Alexander. "Using molecular simulations to parameterize discrete models of protein movement in the membrane." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:893568e9-696f-47e7-8495-59ecfb810459.
Повний текст джерелаАнотація:
The work presented in this thesis centres on the development of a work-flow in which coarse-grained molecular dynamics (MD) simulations of a planar phospholipid bilayer, containing membrane proteins, is used to parameterize a larger-scale simplified bilayer model. Using this work-flow, repeat simulations and simulations of larger systems are possible, better enabling the calculation of bulk statistics for the system. The larger-scale simulations can be run on commercial hardware, once the initial parameterization has been performed. In the simplified representation, each protein was initially only represented by the position of its centre of mass and later with the inclusion of its orientation. The membrane protein used throughout most of this work was the bacterial outer membrane protein NanC, a member of the KdgM family of proteins. To parameterize the motion and interaction of proteins using MD, the potential of mean force (PMF) for the pairwise association of two proteins in a bilayer was calculated for a variety of orientational combinations, using a modified umbrella sampling procedure. The relative orientations chosen represented extreme examples of the contact regimes between the two proteins: they approximately corresponded to maxima and minima of the solvent inaccessible surface area, calculated when the proteins were in contact. These PMFs showed that there was a correlation between the buried surface area and the depth of the potential well in the PMF; this is something that, to date, has only been observed in these relatively-'featureless' membrane proteins (but is seen in globular proteins), where the effect of the interactions with lipids in the bilayer plays a larger role. Features in the PMF were observed that resulted from the preferential organization of lipids in the region between the two proteins. These features were small wells in the PMF, which occurred at protein separations that corresponded to the intervening lipids being optimally packed between the proteins. This result further highlighted the role that the lipids in the bilayer played in the interaction between the NanC proteins. The simplified bilayer model was parameterized using the PMFs and the relationship between buried surface area and potential well depth. The initial model included only the proteins' positions. A series of Monte Carlo simulations were performed in order to compare the system behaviour to that of an equivalent MD simulation. Initially, the MD simulation and our parameterized model did not show a good agreement, so a Monte Carlo scheme that incorporated cluster-based movements was implemented. The agreement between the MD simulation and the simulations of our model using the cluster-based scheme, when comparing diffusive and clustering behaviour, was good. Including the orientation-dependent features of the parameterization resulted in the emergence of behaviour that was not clearly detectable in the MD simulation. Finally, attempts were made to parameterize the model using PMFs for the association of rhodopsin from the literature. Rhodopsin was a much more complicated protein to represent: there was not a clear correlation between surface area and the features of the PMF, and the geometry of the interaction between two rhodopsins was more complicated. Simulations of the 'rows-of-dimers' system of rhodopsin, observed in disc membranes, was not entirely well represented by the model; for such a closely packed system, where the number of lipids is much closer to the number of proteins, the use of an implicit-lipid model meant that the effect of the reduced lipid mobility was not adequately captured. However, the model accurately captures the orientational composition of the system. Future work should be focussed on incorporating explicit representations of the lipid in the system so that the behaviour of close-packed systems are better represented.
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Dutta, Priyanka. "Computational Modeling of Allosteric Stimulation of Nipah Virus Host Binding Protein." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6227.
Повний текст джерелаАнотація:
Nipah belongs to the family of paramyxoviruses that cause numerous fatal diseases in humans and farm animals. There are no FDA approved drugs for Nipah or any of the paramyxoviruses. Designing antiviral therapies that are more resistant to viral mutations require understanding of molecular details underlying infection. This dissertation focuses on obtaining molecular insights into the very first step of infection by Nipah. Such details, in fact, remain unknown for all paramyxoviruses. Infection begins with the allosteric stimulation of Nipah virus host binding protein by host cell receptors. Understanding molecular details of this stimulation process have been challenging mainly because, just as in many eukaryotic proteins, including GPCRs, PDZ domains and T-cell receptors, host receptors induce only minor structural changes (< 2 Å) and, consequently, thermal fluctuations or dynamics play a key role. This work utilizes a powerful molecular dynamics based approach, which yields information on both structure and dynamics, laying the foundation for its future applications to other paramyxoviruses. It proposes a new model for the initial phase of stimulation of Nipah’s host binding protein, and in general, highlights that (a) interfacial waters can play a crucial role in the inception and propagation of allosteric signals; (b) extensive inter-domain rearrangements can be triggered by minor changes in the structures of individual domains; and (c) mutations in dynamically stimulated proteins can induce non-local changes that spread across entire domains.
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Parra, Katherine Cristina. "Combination of the Computational Methods: Molecular dynamics, Homology Modeling and Docking to Design Novel Inhibitors and study Structural Changes in Target Proteins for Current Diseases." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5093.
Повний текст джерелаАнотація:
In this thesis, molecular dynamics simulations, molecular docking, and homology modeling methods have been used in combination to design possible inhibitors as well as to study the structural changes and function of target proteins related to diseases that today are in the spotlight of drug discovery. The inwardly rectifying potassium (Kir) channels constitute the first target in this study; they are involved in cardiac problems. On the other hand, tensin, a promising target in cancer research, is the second target studied here.
The first chapter includes a brief update on computational methods and the current proposal of the combination of MD simulations and docking techniques, a procedure that is applied for the engineering of a new blocker for Kir2.1 ion channels and for the design of possible inhibitors for Tensin.
Chapter two focuses in Kir ion channels that belong to the family of potassium-selective ion channels which have a wide range of physiological activity. The resolved crystal structure of a eukaryotic Kir channel was used as a secondary structure template to build the Kir-channels whose crystallographic structures are unavailable. Tertiapin (TPN), a 21 a.a. peptide toxin found in honey bee venom that blocks a type of Kir channels with high affinity was also used to design new Kir channel blockers. The computational methods homology modeling and protein-protein docking were employed to yield Kir channel-TPN complexes that showed good binding affinity scores for TPN-sensitive Kir channels, and less favorable for Kir channels insensitive to TPN block. The binding pocket of the insensitive Kir-channels was studied to engineer novel TPN-based peptides that show favorable binding scores via thermodynamic mutant-cycle analysis.
Chapter three is focused on the building of homology models for Tensin 1, 2 and 3 domains C2 and PTP using the PTEN X-ray crystallographic structure as a secondary structure template. Molecular docking was employed for the screening of druggable small molecules and molecular dynamics simulations were also used to study the tensin structure and function in order to give some new insights of structural data for experimental binding and enzymatic assays.
Chapter four describes the conformational changes of FixL, a protein of bradyrhizobia japonicum. FixL is a dimer known as oxygen sensor that is involved in the nitrogen fixation process of root plants regulating the expression of genes. Ligand behavior has been investigated after the dissociation event, also the structural changes that are involved in the relaxation to the deoxy state. Molecular dynamics simulations of the CO-bound and CO-unbound bjFixL heme domain were performed during 10 ns in crystal and solution environments then analyzed using Principal Component Analysis (PCA). Our results show that the diffusion of the ligand is influenced by internal motions of the bound structure of the protein before CO dissociation, implying an important role for Arg220. In turn, the location of the ligand after dissociation affects the conformational changes within the protein. The study suggests the presence of a cavity close to the methine bridge C of the heme group in agreement with spectroscopic probes and that Arg220 acts as a gate of the heme cavity.
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Guinto, Ferdiemar Cardenas Jr. "Investigating Secondary Structure Features of YAP1 Protein Fragments Using Molecular Dynamics (MD) and Steered Molecular Dynamics (SMD) Simulations." Scholarly Commons, 2017. https://scholarlycommons.pacific.edu/uop_etds/2973.
Повний текст джерелаАнотація:
Molecular dynamics (MD) is a powerful tool that can be applied to protein folding and protein structure. MD allows for the calculation of movement, and final position, of atoms in a biomolecule. These movements can be used to investigate the pathways that allow proteins to fold into energetically favorable structures. While MD is very useful, it still has its limitations. Most notable, computing power and time are of constant concern.
Protein structure is inherently important due to the direct link between the structure of a protein and its function. One of the four levels of protein structure, the secondary structure, is the first level to accommodate for the three-dimensional shape of a protein. The main driving force behind secondary structure is hydrogen bonding, which occurs between the carboxyl oxygen and the amine hydrogen of the backbone of a peptide. Determining a greater link between hydrogen bond patterns and types of secondary structure can provide more insight on how proteins fold.
Because molecular dynamics allows for an atomic level view of the dynamics behind protein folding/unfolding, it becomes very useful in observing the effects of particular hydrogen bond patterns on the folding pathway and final structure formed of a protein. Using molecular dynamic simulations, a series of experiments in an attempt to alter structure, hydrogen bonding, and folding patterns, can be performed. This information can be used to better understand the driving force of secondary structure, and use the knowledge gained to manipulate these simulations to force folding events, and with that, desired secondary structure features.
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Lumb, Craig Nicholas. "Computational studies of signalling at the cell membrane." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5b2db00-1050-4191-8eff-3521a4885a0c.
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In order to associate with the cytoplasmic leaflet of the plasma membrane, many cytosolic signalling proteins possess a distinct lipid binding domain as part of their overall fold. Here, a multiscale simulation approach has been used to investigate three membrane-binding proteins involved in cellular processes such as growth and proliferation. The pleckstrin homology (PH) domain from the general receptor for phosphoinositides 1 (GRP1-PH) binds phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P₃) with high affinity and specificity. To investigate how this peripheral protein is able to locate its target lipid in the complex membrane environment, Brownian dynamics (BD) simulations were employed to explore association pathways for GRP1-PH binding to PI(3,4,5)P₃ embedded in membranes with different surface charge densities and distributions. The results indicated that non-PI(3,4,5)P₃ lipids can act as decoys to disrupt PI(3,4,5)P₃ binding, but that at approximately physiological anionic lipid concentrations steering towards PI(3,4,5)P₃ is actually enhanced. Atomistic molecular dynamics (MD) simulations revealed substantial membrane penetration of membrane-bound GRP1-PH, evident when non-equilibrium, steered MD simulations were used to forcibly dissociate the protein from the membrane surface. Atomistic and coarse grained (CG) MD simulations of the phosphatase and tensin homologue deleted on chromosome ten (PTEN) tumour suppressor, which also binds PI(3,4,5)P₃, detected numerous non-specific protein-lipid contacts and anionic lipid clustering around PTEN that can be modulated by selective in silico mutagenesis. These results suggested a dual recognition model of membrane binding, with non-specific membrane interactions complementing the protein-ligand interaction. Molecular docking and MD simulations were used to characterise the lipid binding properties of kindlin-1 PH. Simulations demonstrated that a dynamic salt bridge was responsible for controlling the accessibility of the binding site. Electrostatics calculations applied to a variety of PH domains suggested that their molecular dipole moments are typically aligned with their ligand binding sites, which has implications for steering and ligand electrostatic funnelling.
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Pavlovicz, Ryan Elliott. "Investigation of Protein/Ligand Interactions Relating Structural Dynamics to Function: Combined Computational and Experimental Approaches." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397220613.
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Cardoch, Sebastian. "Computational study of single protein sensing using nanopores." Thesis, Uppsala universitet, Materialteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-423441.
Повний текст джерелаАнотація:
Identifying the protein content in a cell in a fast and reliable manner has become a relevant goal in the field of proteomics. This thesis computationally explores the potential for silicon nitride nanopores to sense and distinguish single miniproteins, which are small domains that promise to facilitate the systematic study of larger proteins. Sensing and identification of these biomolecules using nanopores happens by studying modulations in ionic current during translocation. The approach taken in this work was to study two miniproteins of similar geometry, using a cylindrical-shaped pore. I employed molecular mechanics to compare occupied pore currents computed based on the trajectory of ions. I further used density functional theory along with relative surface accessibility values to compute changes in interaction energies for single amino acids and obtain relative dwell times. While the protein remained inside the nanopore, I found no noticeable differences in the occupied pore currents of the two miniproteins for systems subject to 0.5 and 1.0 V bias voltages. Dwell times were estimated based on the translocation time of a protein that exhibits no interaction with the pore walls. I found that both miniproteins feel an attractive force to the pore wall and estimated their relative dwell times to differ by one order of magnitude. This means even in cases where two miniproteins are indistinguishable by magnitude changes in the ionic current, the dwell time might still be used to identify them. This work was an initial investigation that can be further developed to increase the accuracy of the results and be expanded to assess other miniproteins with the goal to aid future experimental work.
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Zhou, Guangfeng. "STATISTICAL MODELS AND THEIR APPLICATIONS IN STUDYING BIOMOLECULAR CONFORMATIONAL DYNAMICS." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/478773.
Повний текст джерелаАнотація:
ChemistryPh.D.
It remains a major challenge in biophysics to understand the conformational dynamics of biomolecules. As powerful tools, molecular dynamics (MD) simulations have become increasingly important in studying the full atomic details of conformational dynamics of biomolecules. In addition, many statistical models have been developed to give insight into the big datasets from MD simulations. In this work, I first describe three statistical models used to analyze MD simulation data: Lifson-Roig Helix-Coil theory, Bayesian inference models, and Markov state models. Then I present the applications of each model in analyzing MD simulations and revealing insight into the conformational dynamics of biomolecules. These statistical models allow us to bridge microscopic and macroscopic mechanisms of biological processes and connect simulations with experiments.
Temple University--Theses
Книги з теми "Proteins Molecular Dynamics Computational Biophysics"
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Sansom, M. S. P., and Philip Charles Biggin. Molecular simulations and biomembranes: From biophysics to function. Cambridge: Royal Society of Chemistry, 2010.
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M, Becker Oren, ed. Computational biochemistry and biophysics. New York: M. Dekker, 2001.
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Donald, Bruce R. Algorithms in structural molecular biology. Cambridge, Mass: MIT Press, 2011.
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Frishman, Dmitrij. Structural bioinformatics of membrane proteins. Wien: Springer, 2010.
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Kostyukov, Viktor. Molecular mechanics of biopolymers. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1010677.
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The monograph is devoted to molecular mechanics simulations of biologically important polymers like proteins and nucleic acids. It is shown that the algorithms based on the classical laws of motion of Newton, with high-quality parameterization and sufficient computing resources is able to correctly reproduce and predict the structure and dynamics of macromolecules in aqueous solution. Summarized the development path of biopolymers molecular mechanics, its theoretical basis, current status and prospects for further progress.
It may be useful to researchers specializing in molecular Biophysics and molecular biology, as well as students of senior courses of higher educational institutions, studying the biophysical and related areas of training.
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Fuxreiter, Monika. Computational Approaches to Protein Dynamics. Taylor & Francis Group, 2014.
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Fuxreiter, Monika. Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2014.
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Fuxreiter, Monika. Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2014.
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Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2018.
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Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2014.
Знайти повний текст джерелаЧастини книг з теми "Proteins Molecular Dynamics Computational Biophysics"
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Eichinger, Markus, Berthold Heymann, Helmut Heller, Helmut Grubmüller, and Paul Tavan. "Conformational Dynamics Simulations of Proteins." In Computational Molecular Dynamics: Challenges, Methods, Ideas, 78–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58360-5_4.
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Helms, Volkhard, and J. Andrew McCammon. "Conformational Transitions of Proteins from Atomistic Simulations." In Computational Molecular Dynamics: Challenges, Methods, Ideas, 66–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58360-5_3.
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Rodziewicz-Motowidło, Sylwia, Emilia Sikorska, and Justyna Iwaszkiewicz. "Molecular Dynamics Studies on Amyloidogenic Proteins." In Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes, 445–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-28554-7_14.
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Vogel, Alexander, and Daniel Huster. "Combining NMR Spectroscopy and Molecular Dynamics Simulation to Investigate the Structure and Dynamics of Membrane-Associated Proteins." In Springer Series in Biophysics, 311–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66601-3_14.
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Antosiewicz, Jan, Elzbieta Błachut-Okrasińska, Tomasz Grycuk, James M. Briggs, Stanisław T. Włodek, Bogdan Lesyng, and J. Andrew McCammon. "Prediction of pKas of Titratable Residues in Proteins Using a Poisson-Boltzmann Model of the Solute-Solvent System." In Computational Molecular Dynamics: Challenges, Methods, Ideas, 176–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58360-5_10.
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Zhang, Jiapu. "A Novel Canonical Dual Global Optimization Computational Approach." In Molecular Structures and Structural Dynamics of Prion Proteins and Prions, 219–62. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7318-8_13.
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Barth, Marie, and Carla Schmidt. "Quantitative Cross-Linking of Proteins and Protein." In Methods in Molecular Biology, 385–400. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_26.
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AbstractCross-linking, in general, involves the covalent linkage of two amino acid residues of proteins or protein complexes in close proximity. Mass spectrometry and computational analysis are then applied to identify the formed linkage and deduce structural information such as distance restraints. Quantitative cross-linking coupled with mass spectrometry is well suited to study protein dynamics and conformations of protein complexes. The quantitative cross-linking workflow described here is based on the application of isotope labelled cross-linkers. Proteins or protein complexes present in different structural states are differentially cross-linked using a “light” and a “heavy” cross-linker. The intensity ratios of cross-links (i.e., light/heavy or heavy/light) indicate structural changes or interactions that are maintained in the different states. These structural insights lead to a better understanding of the function of the proteins or protein complexes investigated. The described workflow is applicable to a wide range of research questions including, for instance, protein dynamics or structural changes upon ligand binding.
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Delemotte, Lucie. "Chapter 7. Bridging the Gap Between Atomistic Molecular Dynamics Simulations and Wet-lab Experimental Techniques: Applications to Membrane Proteins." In Theoretical and Computational Chemistry Series, 247–86. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788015882-00247.
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Sljoka, Adnan. "Structural and Functional Analysis of Proteins Using Rigidity Theory." In Sublinear Computation Paradigm, 337–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4095-7_14.
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AbstractOver the past two decades, we have witnessed an unprecedented explosion in available biological data. In the age of big data, large biological datasets have created an urgent need for the development of bioinformatics methods and innovative fast algorithms. Bioinformatics tools can enable data-driven hypothesis and interpretation of complex biological data that can advance biological and medicinal knowledge discovery. Advances in structural biology and computational modelling have led to the characterization of atomistic structures of many biomolecular components of cells. Proteins in particular are the most fundamental biomolecules and the key constituent elements of all living organisms, as they are necessary for cellular functions. Proteins play crucial roles in immunity, catalysis, metabolism and the majority of biological processes, and hence there is significant interest to understand how these macromolecules carry out their complex functions. The mechanical heterogeneity of protein structures and a delicate mix of rigidity and flexibility, which dictates their dynamic nature, is linked to their highly diverse biological functions. Mathematical rigidity theory and related algorithms have opened up many exciting opportunities to accurately analyse protein dynamics and probe various biological enigmas at a molecular level. Importantly, rigidity theoretical algorithms and methods run in almost linear time complexity, which makes it suitable for high-throughput and big-data style analysis. In this chapter, we discuss the importance of protein flexibility and dynamics and review concepts in mathematical rigidity theory for analysing stability and the dynamics of protein structures. We then review some recent breakthrough studies, where we designed rigidity theory methods to understand complex biological events, such as allosteric communication, large-scale analysis of immune system antibody proteins, the highly complex dynamics of intrinsically disordered proteins and the validation of Nuclear Magnetic Resonance (NMR) solved protein structures.
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Koch, Christof. "Diffusion, Buffering, and Binding." In Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.003.0017.
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In Chap. 9 we introduced calcium ions and alluded to their crucial role in regulating the day-to-day life of neurons. The dynamics of the free intracellular calcium is controlled by a number of physical and chemical processes, foremost among them diffusion and binding to a host of different proteins, which serve as calcium buffers and as calcium sensors or triggers. Whereas buffers simply bind Ca2+ above some critical concentration, releasing it back into the cytoplasm when [Ca2+]i has been reduced below this level, certain proteins— such as calmodulin—change their conformation when they bind with Ca2+ ions, thereby activating or modulating enzymes, ionic channels, or other proteins. The calcium concentration inside the cell not only determines the degree of activation of calcium-dependent potassium currents but—much more importantly—is relevant for determining the changes in structure expressed in synaptic plasticity. As discussed in Chap. 13, it is these changes that are thought to underlie learning. Given the relevance of second messenger molecules, such as Ca2+, IP3, cyclic AMP and others, for the processes underlying growth, sensory adaptation, and the establishment and maintenance of synaptic plasticity, it is crucial that we have some understanding of the role that diffusion and chemical kinetics play in governing the behavior of these substances. Today, we have unprecedented access to the spatio-temporal dynamics of intracellular calcium in individual neurons using fluorescent calcium dyes, such as fura-2 or fluo-3, in combination with confocal or two-photon microscopy in the visible or in the infrared spectrum (Tsien, 1988; Tank et al., 1988; Hernández-Cruz, Sala, and Adams, 1990; Ghosh and Greenberg, 1995).
Тези доповідей конференцій з теми "Proteins Molecular Dynamics Computational Biophysics"
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Stacklies, Wolfram, Frauke Gräter, Dong-Qing Wei, and Xi-Jun Wang. "Force Distribution in Proteins from Molecular Dynamics Simulations." In THEORY AND APPLICATIONS OF COMPUTATIONAL CHEMISTRY—2008. AIP, 2009. http://dx.doi.org/10.1063/1.3108379.
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Takasu, Masako, Hiromu Sugiyama, Yosuke Hirata, Hironao Yamada, Takeshi Miyakawa, and Ryota Morikawa. "Molecular dynamics simulation of coarse grained models of gel and proteins." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015 (ICCMSE 2015). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4938848.
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Kitao, Akio, Ryuhei Harada, Yasutaka Nishihara, and Duy Phuoc Tran. "Parallel cascade selection molecular dynamics for efficient conformational sampling and free energy calculation of proteins." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016 (ICCMSE 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4968639.
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Ishioka, T., H. Yamada, T. Miyakawa, R. Morikawa, S. Akanuma, A. Yamagishi, and M. Takasu. "Mutual positional preference of IPMDH proteins for binding studied by coarse-grained molecular dynamics simulation." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016 (ICCMSE 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4968649.
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Harada, Ryuhei, Yasutaka Nishihara, Nobuhiko Wakai, and Akio Kitao. "Conformational transition pathway and free energy analyses of proteins by parallel cascade selection molecular dynamics (PaCS-MD)." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2014 (ICCMSE 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4897682.
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Jewel, Yead, Prashanta Dutta, and Jin Liu. "Coarse-Grained Molecular Dynamics Simulations of Sugar Transport Across Lactose Permease." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52337.
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Sugar (one of the critical nutrition elements for all life forms) transport across the cell membranes play essential roles in a wide range of living organism. One of the most important active transport (against the sugar concentration) mechanisms is facilitated by the transmembrane transporter proteins, such as the Escherichia coli lactose permease (LacY) proteins. Active transport of sugar molecules with LacY proteins requires a proton gradient and a sequence of complicated protein conformational changes. However, the exact molecular mechanisms and the protein structural information involved in the transport process are largely unknown. All atom atomistic simulations are able to provide full details but are limited to relative small length and time scales due to the computational cost. The protein conformational changes during sugar transport across LacY are large scale structural reorganization and inaccessible to all atom simulations. In this work, we investigate the molecular mechanisms and conformational changes during sugar transport using coarse-grained molecular dynamics (CGMD) simulations. In our coarse-grained force field, we follow the procedures developed by Han et al. [1, 2], in which the protein model is united-atom based and each heavy atom together with the attached hydrogen atoms is represented by one site, then the protein force filed is coupled with the MARTINI [3] water and lipid force fields. This hybrid force field takes the advantage of the efficiency of MARTINI force field for the environment (water and lipid), while retaining the detailed conformational information for the proteins. Specifically, we develop the new force fields for interactions between sugar molecules and protein by matching the potential of mean force between all-atom and coarse-grained models. Then we validate our force field by comparing the potential of mean force for a glucose interaction with a carbohydrate binding protein from our new force field, with the results from all atom simulations. After validation, we implement the force field for sugar transport across LacY proteins. Through our simulations we are able to capture the formation/breakage of the important hydrogen bonds and salt bridges, which are crucial to the overall conformational changes of LacY.
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Honarmandi, Peyman, Philip Bransford та Roger D. Kamm. "Mechanical Properties of α-Helices Estimated Using Molecular Dynamics and Finite Element Simulations". У ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-69058.
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Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.
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Cortés, Juan, and Ibrahim Al-Bluwi. "A Robotics Approach to Enhance Conformational Sampling of Proteins." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70105.
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Proteins are biological macromolecules that play essential roles in living organisms. Furthermore, the study of proteins and their function is of interest in other fields in addition to biology, such as pharmacology and biotechnology. Understanding the relationship between protein structure, dynamics and function is indispensable for advances in all these areas. This requires a combination of experimental and computational methods, whose development is the object of very active interdisciplinary research. In such a context, this paper presents a technique to enhance conformational sampling of proteins carried out with computational methods such as molecular dynamics simulations or Monte Carlo methods. Our approach is based on a mechanistic representation of proteins that enables the application of efficient methods originating from robotics. The paper explains the generalities of the approach, and gives details on its application to devise Monte Carlo move classes. Results show the good performance of the method for sampling the conformational space of different types of proteins.
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EzzEldin, Hussein M., and Santiago D. Solares. "Calculation of Isothermal Intrinsic Compressibility and Compression of GvpA Protein in Halobacterium sp. NRC-1 Using Molecular Modeling and Dynamics." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86265.
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Gas vesicles are low-density, gas-filled protein organelles found inside various microorganisms. They have a lipid-free membrane with an average thickness of 2 nm and provide their hosts with buoyancy. In this study we characterized gas vesicle proteins synthesized by the Halobacterium sp. NRC-1 strain making use of molecular modeling methods and molecular dynamics (MD) simulations. The tertiary structure of GvpA protein, the major constituent of the gas vesicle membrane, was predicted using the De Novo computational design method available in the Rosetta Suite 2.3.1 software and was found to be in agreement with experimental data available from previous studies conducted by others and the consensus of different secondary structure prediction web servers. Optimization of the predicted structure was first carried out by energy minimization and simulated annealing. Subsequently, the mechanical properties of GvpA were investigated via constant pressure and temperature (NPT) aqueous MD simulations, in which two approaches were used to study the isothermal compressibility: quantification of the fluctuations in protein volume at constant pressure and temperature, and quantification of the volume changes induced through changes in the simulation pressure. Long term we plan to incorporate this information into multi-scale models of whole gas vesicles.
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Khan, Imad M., and Kurt S. Anderson. "A Robust Framework for Adaptive Multiscale Modeling of Biopolymers Using Highly Parallelizable Methods." In ASME 2013 2nd Global Congress on NanoEngineering for Medicine and Biology. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/nemb2013-93099.
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For many biopolymers (RNA, DNA, enzymes and proteins) the nature of the molecules interaction within the cell has been determined to be highly a function of its conformational structure. Understanding how to influence and control this structure thus is of critical importance if one wishes to manipulate the intercellular processes of which these biopolymers play such a central role. In molecular dynamics (MD) simulations, a fully atomistic model represents the system at the finest scale and as such captures all the dynamics of the system. If the simulation is permitted to run sufficiently long important emergent behaviors can develop and show themselves. Such MD simulations represent a direct applications of Newton’s Laws of Motion to the individual atoms in the system, and are conceptually the easiest to implement. An advantage of this procedure is that the simulation yields important information not only about the intermediate states and the mechanisms which produced them, but also provides the rates at which these processes occur. These intermediate conformational states have repeatedly been implicated in many known biological function [1], [2]. Unfortunately, this albeit correct, but naive approach quickly leads to intractable models and prohibitive computational expense when applied to systems involving many atoms. As a result, researcher often grossly over simplify the system move to non-deterministic methods such as Monte Carlo, which effectively remove dynamics from the system, or use undesirably gross model simplification. Because of these forward dynamics performance difficulties, potentially important mechanisms governing biopolymer structure have not been adequately explored and/or identified. The methods and algorithms described in this paper are intended to extend the capabilities of the simulation techniques for such systems so that the forward dynamics can better predict the non-equilibrium behavior of these systems, thus complementing Monte Carlo, while retaining much useful intermediate process and temporal information.