Journal articles: 'Protein simulation'

1

Daggett, Valerie. "Protein Folding−Simulation." Chemical Reviews 106, no. 5 (May 2006): 1898–916. http://dx.doi.org/10.1021/cr0404242.

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Velesinović, Aleksandar, and Goran Nikolić. "Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives." Acta Facultatis Medicae Naissensis 38, no. 1 (2021): 5–17. http://dx.doi.org/10.5937/afmnai38-28322.

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Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

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Elcock, Adrian H., David Sept, and J. Andrew McCammon. "Computer Simulation of Protein−Protein Interactions." Journal of Physical Chemistry B 105, no. 8 (March 2001): 1504–18. http://dx.doi.org/10.1021/jp003602d.

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4

Yun, R. H., and Jan Hermans. "Conformation equilibria of valine studies by dynamics simulation." "Protein Engineering, Design and Selection" 4, no. 7 (1991): 761–66. http://dx.doi.org/10.1093/protein/4.7.761.

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Arnold, Gregory E., and Rick L. Ornstein. "A molecular dynamics simulation of bacteriophage T4 lysozyme." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 703–14. http://dx.doi.org/10.1093/protein/5.7.703.

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van Gunsteren, W. F. "The role of computer simulation techniques in protein engineering." "Protein Engineering, Design and Selection" 2, no. 1 (1988): 5–13. http://dx.doi.org/10.1093/protein/2.1.5.

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7

Cherfils, Jacqueline, Stéphane Duquerroy, and Joël Janin. "Protein-protein recognition analyzed by docking simulation." Proteins: Structure, Function, and Genetics 11, no. 4 (December 1991): 271–80. http://dx.doi.org/10.1002/prot.340110406.

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8

Helms, Volkhard, Mazen Ahmad, Alexander Spaar, and Wei Gu. "Computer Simulation of Protein-Protein Association Processes." Biophysical Journal 96, no. 3 (February 2009): 75a. http://dx.doi.org/10.1016/j.bpj.2008.12.288.

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Komeiji, Yuto, Masami Uebayasi, Jun-ichiro Someya, and Ichiro Yamato. "Molecular dynamics simulation of trp-aporepressor in a solvent." "Protein Engineering, Design and Selection" 4, no. 8 (1991): 871–75. http://dx.doi.org/10.1093/protein/4.8.871.

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KHAIRUDIN, NURUL BAHIYAH AHMAD, and HABIBAH A. WAHAB. "PROTEIN STRUCTURE PREDICTION USING GAS PHASE MOLECULAR DYNAMICS SIMULATION: EOTAXIN-3 CYTOKINE AS A CASE STUDY." International Journal of Modern Physics: Conference Series 09 (January 2012): 193–98. http://dx.doi.org/10.1142/s2010194512005259.

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In the current work, the structure of the enzyme CC chemokine eotaxin-3 (1G2S) was chosen as a case study to investigate the effects of gas phase on the predicted protein conformation using molecular dynamics simulation. Generally, simulating proteins in the gas phase tend to suffer from various drawbacks, among which excessive numbers of protein-protein hydrogen bonds. However, current results showed that the effects of gas phase simulation on 1G2S did not amplify the protein-protein hydrogen bonds. It was also found that some of the hydrogen bonds which were crucial in maintaining the secondary structural elements were disrupted. The predicted models showed high values of RMSD, 11.5 Å and 13.5 Å for both vacuum and explicit solvent simulations, respectively, indicating that the conformers were very much different from the native conformation. Even though the RMSD value for the in vacuo model was slightly lower, it somehow suffered from lower fraction of native contacts, poor hydrogen bonding networks and fewer occurrences of secondary structural elements compared to the solvated model. This finding supports the notion that water plays a dominant role in guiding the protein to fold along the correct path.

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Tavanti, Francesco, Alfonso Pedone, and Maria Cristina Menziani. "Multiscale Molecular Dynamics Simulation of Multiple Protein Adsorption on Gold Nanoparticles." International Journal of Molecular Sciences 20, no. 14 (July 19, 2019): 3539. http://dx.doi.org/10.3390/ijms20143539.

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A multiscale molecular dynamics simulation study has been carried out in order to provide in-depth information on the adsorption of hemoglobin, myoglobin, and trypsin over citrate-capped AuNPs of 15 nm diameter. In particular, determinants for single proteins adsorption and simultaneous adsorption of the three types of proteins considered have been studied by Coarse-Grained and Meso-Scale molecular simulations, respectively. The results, discussed in the light of the controversial experimental data reported in the current experimental literature, have provided a detailed description of the (i) recognition process, (ii) number of proteins involved in the early stages of corona formation, (iii) protein competition for AuNP adsorption, (iv) interaction modalities between AuNP and protein binding sites, and (v) protein structural preservation and alteration.

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12

Echave, Julian. "Fast computational mutation-response scanning of proteins." PeerJ 9 (April 21, 2021): e11330. http://dx.doi.org/10.7717/peerj.11330.

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Studying the effect of perturbations on protein structure is a basic approach in protein research. Important problems, such as predicting pathological mutations and understanding patterns of structural evolution, have been addressed by computational simulations that model mutations using forces and predict the resulting deformations. In single mutation-response scanning simulations, a sensitivity matrix is obtained by averaging deformations over point mutations. In double mutation-response scanning simulations, a compensation matrix is obtained by minimizing deformations over pairs of mutations. These very useful simulation-based methods may be too slow to deal with large proteins, protein complexes, or large protein databases. To address this issue, I derived analytical closed formulas to calculate the sensitivity and compensation matrices directly, without simulations. Here, I present these derivations and show that the resulting analytical methods are much faster than their simulation counterparts.

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13

Kovalenko, I. B., A. M. Abaturova, A. N. Diakonova, O. S. Knyazeva, D. M. Ustinin, S. S. Khruschev, G. Yu Riznichenko, and A. B. Rubin. "Computer Simulation of Protein-Protein Association in Photosynthesis." Mathematical Modelling of Natural Phenomena 6, no. 7 (2011): 39–54. http://dx.doi.org/10.1051/mmnp/20116704.

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14

Gabdoulline, Razif R., and Rebecca C. Wade. "Brownian Dynamics Simulation of Protein–Protein Diffusional Encounter." Methods 14, no. 3 (March 1998): 329–41. http://dx.doi.org/10.1006/meth.1998.0588.

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15

Sorenson, Jon M., and Teresa Head-Gordon. "Protein Engineering Study of Protein L by Simulation." Journal of Computational Biology 9, no. 1 (January 2002): 35–54. http://dx.doi.org/10.1089/10665270252833181.

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16

Bitran, Amir, William M. Jacobs, and Eugene Shakhnovich. "Validation of DBFOLD: An efficient algorithm for computing folding pathways of complex proteins." PLOS Computational Biology 16, no. 11 (November 16, 2020): e1008323. http://dx.doi.org/10.1371/journal.pcbi.1008323.

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Atomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions. Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a protein’s folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation. Our algorithm, which is available online, can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease.

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17

Berg, Andrej, and Christine Peter. "Simulating and analysing configurational landscapes of protein–protein contact formation." Interface Focus 9, no. 3 (April 19, 2019): 20180062. http://dx.doi.org/10.1098/rsfs.2018.0062.

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Interacting proteins can form aggregates and protein–protein interfaces with multiple patterns and different stabilities. Using molecular simulation one would like to understand the formation of these aggregates and which of the observed states are relevant for protein function and recognition. To characterize the complex configurational ensemble of protein aggregates, one needs a quantitative measure for the similarity of structures. We present well-suited descriptors that capture the essential features of non-covalent protein contact formation and domain motion. This set of collective variables is used with a nonlinear multi-dimensional scaling-based dimensionality reduction technique to obtain a low-dimensional representation of the configurational landscape of two ubiquitin proteins from coarse-grained simulations. We show that this two-dimensional representation is a powerful basis to identify meaningful states in the ensemble of aggregated structures and to calculate distributions and free energy landscapes for different sets of simulations. By using a measure to quantitatively compare free energy landscapes we can show how the introduction of a covalent bond between two ubiquitin proteins at different positions alters the configurational states of these dimers.

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18

Cheung, David, and Suman Samantray. "Molecular Dynamics Simulation of Protein Biosurfactants." Colloids and Interfaces 2, no. 3 (September 8, 2018): 39. http://dx.doi.org/10.3390/colloids2030039.

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Surfaces and interfaces are ubiquitous in nature and are involved in many biological processes. Due to this, natural organisms have evolved a number of methods to control interfacial and surface properties. Many of these methods involve the use of specialised protein biosurfactants, which due to the competing demands of high surface activity, biocompatibility, and low solution aggregation may take structures that differ from the traditional head–tail structure of small molecule surfactants. As well as their biological functions, these proteins have also attracted interest for industrial applications, in areas including food technology, surface modification, and drug delivery. To understand the biological functions and technological applications of protein biosurfactants, it is necessary to have a molecular level description of their behaviour, in particular at surfaces and interfaces, for which molecular simulation is well suited to investigate. In this review, we will give an overview of simulation studies of a number of examples of protein biosurfactants (hydrophobins, surfactin, and ranaspumin). We will also outline some of the key challenges and future directions for molecular simulation in the investigation of protein biosurfactants and how this can help guide future developments.

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19

Tung, Hsin-Ju, and Jim Pfaendtner. "Kinetics and mechanism of ionic-liquid induced protein unfolding: application to the model protein HP35." Molecular Systems Design & Engineering 1, no. 4 (2016): 382–90. http://dx.doi.org/10.1039/c6me00047a.

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20

Khokhlov, Alexei R., and Pavel G. Khalatur. "Protein-like copolymers: computer simulation." Physica A: Statistical Mechanics and its Applications 249, no. 1-4 (January 1998): 253–61. http://dx.doi.org/10.1016/s0378-4371(97)00473-1.

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21

Bratko, Dusan, Troy Cellmer, John M. Prausnitz, and Harvey W. Blanch. "Molecular simulation of protein aggregation." Biotechnology and Bioengineering 96, no. 1 (2006): 1–8. http://dx.doi.org/10.1002/bit.21232.

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22

Pellegrini, Matteo, Stephanie W. Wukovitz, and Todd O. Yeates. "Simulation of protein crystal nucleation." Proteins: Structure, Function, and Genetics 28, no. 4 (August 1997): 515–21. http://dx.doi.org/10.1002/(sici)1097-0134(199708)28:4<515::aid-prot5>3.0.co;2-8.

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23

Khalatur, P. G., V. A. Ivanov, N. P. Shusharina, and A. R. Khokhlov. "Protein-like copolymers: Computer simulation." Russian Chemical Bulletin 47, no. 5 (May 1998): 855–60. http://dx.doi.org/10.1007/bf02498152.

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24

Parish, J. H. "Protein Purification A Computer Simulation." Biochemical Education 16, no. 4 (October 1988): 228. http://dx.doi.org/10.1016/0307-4412(88)90132-x.

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25

van Gunsteren, W. F., P. H. Hünenberger, A. E. Mark, P. E. Smith, and I. G. Tironi. "Computer simulation of protein motion." Computer Physics Communications 91, no. 1-3 (September 1995): 305–19. http://dx.doi.org/10.1016/0010-4655(95)00055-k.

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26

Iyer, Lakshmanan K., and Pradman K. Qasba. "Molecular dynamics simulation of α-lactalbumin and calcium binding c-type lysozyme." Protein Engineering, Design and Selection 12, no. 2 (February 1999): 129–39. http://dx.doi.org/10.1093/protein/12.2.129.

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27

Ophir, Ron, and Jonathan M. Gershoni. "Biased random mutagenesis of peptides: determination of mutation frequency by computer simulation." "Protein Engineering, Design and Selection" 8, no. 2 (1995): 143–46. http://dx.doi.org/10.1093/protein/8.2.143.

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28

Shesham, R. D., L. J. Bartolotti, and Y. Li. "Molecular dynamics simulation studies on Ca2+-induced conformational changes of annexin I." Protein Engineering Design and Selection 21, no. 2 (January 24, 2008): 115–20. http://dx.doi.org/10.1093/protein/gzm094.

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29

Johnson, Lucas B., Lucas P. Gintner, Sehoo Park, and Christopher D. Snow. "Discriminating between stabilizing and destabilizing protein design mutations via recombination and simulation." Protein Engineering Design and Selection 28, no. 8 (June 15, 2015): 259–67. http://dx.doi.org/10.1093/protein/gzv030.

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30

Siebers, Joerg, and A. Sarai. "1M1430 Two Maximum Step-Size Monte-Carlo Simulation of DNA-Protein Interactions." Seibutsu Butsuri 42, supplement2 (2002): S77. http://dx.doi.org/10.2142/biophys.42.s77_2.

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31

Kang, Yeona, and Charles M. Fortmann. "An Alternative Approach to Protein Folding." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/583045.

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A diffusion theory-based, all-physicalab initioprotein folding simulation is described and applied. The model is based upon the drift and diffusion of protein substructures relative to one another in the multiple energy fields present. Without templates or statistical inputs, the simulations were run at physiologic and ambient temperatures (including pH). Around 100 protein secondary structures were surveyed, and twenty tertiary structures were determined. Greater than 70% of the secondary core structures with over 80% alpha helices were correctly identified on protein ranging from 30 to 200 amino-acid sequence. The drift-diffusion model predicted tertiary structures with RMSD values in the 3–5 Angstroms range for proteins ranging 30 to 150 amino acids. These predictions are among the best for an allab initioprotein simulation. Simulations could be run entirely on a desktop computer in minutes; however, more accurate tertiary structures were obtained using molecular dynamic energy relaxation. The drift-diffusion model generated realistic energy versus time traces. Rapid secondary structures followed by a slow compacting towards lower energy tertiary structures occurred after an initial incubation period in agreement with observations.

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32

Ciemny, Maciej, Aleksandra Badaczewska-Dawid, Monika Pikuzinska, Andrzej Kolinski, and Sebastian Kmiecik. "Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields." International Journal of Molecular Sciences 20, no. 3 (January 31, 2019): 606. http://dx.doi.org/10.3390/ijms20030606.

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The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein–peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein–peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

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33

Heimstad, Eldbjørg S., Lars K. Hansen, and Arne O. Smalås. "Comparative molecular dynamics simulation studies of salmon and bovine trypsins in aqueous solution." "Protein Engineering, Design and Selection" 8, no. 4 (1995): 379–88. http://dx.doi.org/10.1093/protein/8.4.379.

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34

Baker, Charles, Sheelagh Carpendale, Przemyslaw Prusinkiewicz, and Michael Surette. "GeneVis: Simulation and Visualization of Genetic Networks." Information Visualization 2, no. 4 (December 2003): 201–17. http://dx.doi.org/10.1057/palgrave.ivs.9500055.

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GeneVis simulates genetic networks and visualizes the process of this simulation interactively, providing a visual environment for exploring the dynamics of genetic regulatory networks. The visualization environment supports several representational modes, which include: an individual protein representation, a protein concentration representation, and a network structure representation. The individual protein representation shows the activities of the individual proteins. The protein concentration representation illustrates the relative spread and concentrations of the different proteins in the simulation. The network structure representation depicts the genetic network dependencies that are present in the simulation. GeneVis includes several interactive viewing tools. These include animated transitions from the individual protein representation to the protein concentration representation and from the individual protein representation to the network structure representation. Three types of lenses are used to provide different views within a representation: fuzzy lenses, base pair lenses, and the network structure ring lens. With a fuzzy lens an alternate representation can be viewed in a selected region. The base pair lenses allow users to reposition genes for better viewing or to minimize interference during the simulation. The ring lens provides detail-in-context viewing of individual levels in the genetic network structure representation.

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35

Elcock, Adrian H., Razif R. Gabdoulline, Rebecca C. Wade, and J. Andrew McCammon. "Computer simulation of protein-protein association kinetics: acetylcholinesterase-fasciculin." Journal of Molecular Biology 291, no. 1 (September 1999): 149–62. http://dx.doi.org/10.1006/jmbi.1999.2919.

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36

Sansom, M. S. P., P. J. Bond, S. S. Deol, A. Grottesi, S. Haider, and Z. A. Sands. "Molecular simulations and lipid–protein interactions: potassium channels and other membrane proteins." Biochemical Society Transactions 33, no. 5 (October 26, 2005): 916–20. http://dx.doi.org/10.1042/bst0330916.

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Molecular dynamics simulations may be used to probe the interactions of membrane proteins with lipids and with detergents at atomic resolution. Examples of such simulations for ion channels and for bacterial outer membrane proteins are described. Comparison of simulations of KcsA (an α-helical bundle) and OmpA (a β-barrel) reveals the importance of two classes of side chains in stabilizing interactions with the head groups of lipid molecules: (i) tryptophan and tyrosine; and (ii) arginine and lysine. Arginine residues interacting with lipid phosphate groups play an important role in stabilizing the voltage-sensor domain of the KvAP channel within a bilayer. Simulations of the bacterial potassium channel KcsA reveal specific interactions of phosphatidylglycerol with an acidic lipid-binding site at the interface between adjacent protein monomers. A combination of molecular modelling and simulation reveals a potential phosphatidylinositol 4,5-bisphosphate-binding site on the surface of Kir6.2.

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37

Yao, Y. Y., K. L. Shrestha, Y. J. Wu, H. J. Tasi, C. C. Chen, J. M. Yang, A. Ando, C. Y. Cheng, and Y. K. Li. "Structural simulation and protein engineering to convert an endo-chitosanase to an exo-chitosanase." Protein Engineering Design and Selection 21, no. 9 (May 23, 2008): 561–66. http://dx.doi.org/10.1093/protein/gzn033.

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38

Tagami, U., N. Shimba, M. Nakamura, K. i. Yokoyama, E. i. Suzuki, and T. Hirokawa. "Substrate specificity of microbial transglutaminase as revealed by three-dimensional docking simulation and mutagenesis." Protein Engineering Design and Selection 22, no. 12 (October 22, 2009): 747–52. http://dx.doi.org/10.1093/protein/gzp061.

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39

Schwaigerlehner, L., M. Pechlaner, P. Mayrhofer, C. Oostenbrink, and R. Kunert. "Lessons learned from merging wet lab experiments with molecular simulation to improve mAb humanization." Protein Engineering, Design and Selection 31, no. 7-8 (May 11, 2018): 257–65. http://dx.doi.org/10.1093/protein/gzy009.

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40

Rode, Geralynne A. "Teaching Protein Synthesis Using a Simulation." American Biology Teacher 57, no. 1 (January 1, 1995): 50–52. http://dx.doi.org/10.2307/4449915.

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41

Gruebele, Martin. "Protein Dynamics in Simulation and Experiment." Journal of the American Chemical Society 136, no. 48 (December 3, 2014): 16695–97. http://dx.doi.org/10.1021/ja510614s.

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42

Bhattacharya, D. K., E. Clementi, and W. Xue. "Stochastic dynamic simulation of a protein." International Journal of Quantum Chemistry 42, no. 5 (June 5, 1992): 1397–408. http://dx.doi.org/10.1002/qua.560420516.

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43

Imamoglu, Esra. "Simulation design for microalgal protein optimization." Bioengineered 6, no. 6 (September 29, 2015): 342–46. http://dx.doi.org/10.1080/21655979.2015.1098792.

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Silvestre-Ryan, Jordi, and Jhih-Wei Chu. "Multiscale Simulation of Intra-Protein Communication." Biophysical Journal 100, no. 3 (February 2011): 527a. http://dx.doi.org/10.1016/j.bpj.2010.12.3079.

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45

Takada, S. "Protein folding simulation for genome sequence." Seibutsu Butsuri 40, supplement (2000): S106. http://dx.doi.org/10.2142/biophys.40.s106_3.

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Lin, Ping, and Coray M. Colina. "Molecular simulation of protein–polymer conjugates." Current Opinion in Chemical Engineering 23 (March 2019): 44–50. http://dx.doi.org/10.1016/j.coche.2019.02.006.

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47

Northrup, Scott H., J. Alan Luton, Jeffrey O. Boles, and John C. L. Reynolds. "Brownian dynamics simulation of protein association." Journal of Computer-Aided Molecular Design 1, no. 4 (January 1988): 291–311. http://dx.doi.org/10.1007/bf01677278.

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48

Eom, Kilho. "Computer Simulation of Protein Materials at Multiple Length Scales: From Single Proteins to Protein Assemblies." Multiscale Science and Engineering 1, no. 1 (January 2019): 1–25. http://dx.doi.org/10.1007/s42493-018-00009-7.

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49

Kurcinski, Mateusz, Sebastian Kmiecik, Mateusz Zalewski, and Andrzej Kolinski. "Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations." International Journal of Molecular Sciences 22, no. 14 (July 8, 2021): 7341. http://dx.doi.org/10.3390/ijms22147341.

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Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

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50

Lahey, Shae-Lynn J., and Christopher N. Rowley. "Simulating protein–ligand binding with neural network potentials." Chemical Science 11, no. 9 (2020): 2362–68. http://dx.doi.org/10.1039/c9sc06017k.

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Neural network potentials provide accurate predictions of the structures and stabilities of drug molecules. We present a method to use these new potentials in simulations of drugs binding to proteins using existing molecular simulation codes.

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Journal articles on the topic 'Protein simulation'

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