CiteULike | Further Information

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Mendeley | Further Information

{"title"=>"Diffusion, crowding & protein stability in a dynamic molecular model of the bacterial cytoplasm", "type"=>"journal", "authors"=>[{"first_name"=>"Sean R.", "last_name"=>"McGuffee", "scopus_author_id"=>"14622829100"}, {"first_name"=>"Adrian H.", "last_name"=>"Elcock", "scopus_author_id"=>"7003451126"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"77950792003", "doi"=>"10.1371/journal.pcbi.1000694", "pui"=>"358620666", "pmid"=>"20221255", "scopus"=>"2-s2.0-77950792003", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\n1553-734X (Linking)"}, "id"=>"b23dc4e5-6755-3f24-93d7-3f11bb5d246c", "abstract"=>"Abstract A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed in vitro accurately reflects their behavior in vivo. A number of sophisticated experimental techniques now allow the behavior of individual types of ... \\n", "link"=>"http://www.mendeley.com/research/diffusion-crowding-protein-stability-dynamic-molecular-model-bacterial-cytoplasm-4", "reader_count"=>465, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>31, "Librarian"=>1, "Student > Doctoral Student"=>22, "Researcher"=>128, "Student > Ph. D. Student"=>139, "Student > Postgraduate"=>18, "Student > Master"=>49, "Other"=>7, "Student > Bachelor"=>33, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>32}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>31, "Librarian"=>1, "Student > Doctoral Student"=>22, "Researcher"=>128, "Student > Ph. D. Student"=>139, "Student > Postgraduate"=>18, "Student > Master"=>49, "Other"=>7, "Student > Bachelor"=>33, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>32}, "reader_count_by_subject_area"=>{"Unspecified"=>16, "Agricultural and Biological Sciences"=>212, "Chemical Engineering"=>4, "Chemistry"=>63, "Computer Science"=>16, "Engineering"=>29, "Biochemistry, Genetics and Molecular Biology"=>52, "Materials Science"=>3, "Mathematics"=>7, "Medicine and Dentistry"=>5, "Neuroscience"=>1, "Sports and Recreations"=>1, "Physics and Astronomy"=>52, "Psychology"=>1, "Social Sciences"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>2}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>52}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>7}, "Unspecified"=>{"Unspecified"=>16}, "Chemical Engineering"=>{"Chemical Engineering"=>4}, "Engineering"=>{"Engineering"=>29}, "Chemistry"=>{"Chemistry"=>63}, "Neuroscience"=>{"Neuroscience"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>212}, "Computer Science"=>{"Computer Science"=>16}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>52}}, "reader_count_by_country"=>{"United States"=>30, "Japan"=>7, "United Kingdom"=>11, "Malaysia"=>1, "Ghana"=>1, "Portugal"=>2, "Canada"=>1, "Netherlands"=>1, "Turkey"=>1, "Belgium"=>2, "Norway"=>3, "Taiwan"=>1, "Finland"=>1, "Denmark"=>1, "Brazil"=>1, "Mexico"=>2, "Italy"=>3, "Israel"=>1, "Australia"=>3, "France"=>1, "Germany"=>13}, "group_count"=>26}

ORCID

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• {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fploscompbiol%2Farticle%3Fid%3D10.1371%252Fjournal.pcbi.1000694", "share_count"=>1, "like_count"=>0, "comment_count"=>0, "click_count"=>0, "total_count"=>1}

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• {"id"=>"316649392209805312", "text"=>"@AkshatRathi it's from here http://t.co/9nZx5SL4Su but I have no intuitive sense for the expected number densities of proteins..", "created_at"=>"2013-03-26T20:34:32Z", "user"=>"aatishb", "user_name"=>"Aatish Bhatia", "user_profile_image"=>"http://a0.twimg.com/profile_images/3004047290/1fb4826dac2a09c3431bf19a21c0d669_normal.jpeg"}
• {"id"=>"350416937232248832", "text"=>"#ICAR2013 S.Spoel.Why do cells need post-translational redox modifications. Cytosol very crowded - see movies here http://t.co/C5LQugV48x", "created_at"=>"2013-06-28T00:54:42Z", "user"=>"PlantTeaching", "user_name"=>"mary williams", "user_profile_image"=>"http://a0.twimg.com/profile_images/1648376185/WeeLogo_normal.jpg"}
• {"id"=>"409995484191662080", "text"=>"大腸菌細胞質の分子混雑のシミュレーション。Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm http://t.co/UlV4lUN9LP", "created_at"=>"2013-12-09T10:38:35Z", "user"=>"ryotaiino", "user_name"=>"Ryota IINO", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3311038873/1a3ce6721e10a43cd470884cbf4e3b01_normal.jpeg"}
• {"id"=>"420569736041086978", "text"=>"L'intérieur ô combien encombré d'une bactérie.. (50 macromolécules majeures)#Cytoplasme #EColi http://t.co/H5SMTxetoy http://t.co/bgnjxOK0ON", "created_at"=>"2014-01-07T14:56:53Z", "user"=>"GrandDahut", "user_name"=>"GrandDahut", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2641795852/e7c1565c6cfb538a8bcaf347978210a3_normal.jpeg"}
• {"id"=>"502427648648560640", "text"=>"how crowded is the cytoplasm http://t.co/4RwnV6lY36", "created_at"=>"2014-08-21T12:11:00Z", "user"=>"nzradu", "user_name"=>"Radu Zabet", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3442034145/38a4e0e15ac9b561b44fa90ff83bef44_normal.png"}
• {"id"=>"748219226859638792", "text"=>"????????? ??????!\n??????: Adrian H Elcock (University of Iowa)\nPLoS Computational Biology, https://t.co/4jGUn9wDxM https://t.co/Sz9rmgTifZ", "created_at"=>"2016-06-29T18:18:59Z", "user"=>"PhiScience", "user_name"=>"????? ??? ??????", "user_profile_image"=>"http://pbs.twimg.com/profile_images/744615935449964544/4Zp04y4Y_normal.jpg"}
• {"id"=>"748224720500297729", "text"=>"RT @PhiScience: ????????? ??????!\n??????: Adrian H Elcock (University of Iowa)\nPLoS Computational Biology, https://t.co/4jGUn9wDxM https://…", "created_at"=>"2016-06-29T18:40:49Z", "user"=>"L_mansour96", "user_name"=>"???? ?????", "user_profile_image"=>"http://pbs.twimg.com/profile_images/735962201459195905/LOr0Wx-n_normal.jpg"}
• {"id"=>"748226733992714240", "text"=>"RT @PhiScience: ????????? ??????!\n??????: Adrian H Elcock (University of Iowa)\nPLoS Computational Biology, https://t.co/4jGUn9wDxM https://…", "created_at"=>"2016-06-29T18:48:49Z", "user"=>"Dr_Pixels", "user_name"=>"Bashar Bataineh", "user_profile_image"=>"http://pbs.twimg.com/profile_images/690702309077164032/fTfKgC7y_normal.jpg"}
• {"id"=>"758346553216102400", "text"=>"#compchemGRC Adrian Elcock's brownian dynamics movie of a cell from 2010 still going strong! https://t.co/gmqUa5Kc5k", "created_at"=>"2016-07-27T17:01:22Z", "user"=>"marcvanderkamp", "user_name"=>"Marc van der Kamp", "user_profile_image"=>"http://pbs.twimg.com/profile_images/631446538837983232/JVT5dp1p_normal.jpg"}
• {"id"=>"792841464027971584", "text"=>"Bacterial Cytoplasm Simulation: famous paper by McGuffee et al.\n@PLOSONE #diffusion #cell\nhttps://t.co/fuRtImEcMJ\nhttps://t.co/PD9J2nBvfO", "created_at"=>"2016-10-30T21:31:49Z", "user"=>"luyibov", "user_name"=>"liubov", "user_profile_image"=>"http://pbs.twimg.com/profile_images/573438094101217280/byxf3J2t_normal.jpeg"}
• {"id"=>"902200138122354688", "text"=>"Nice... There was a similar report in @PLOSONE in 2010 - https://t.co/WNxSMtI9vG https://t.co/I3yHgM4FL8", "created_at"=>"2017-08-28T16:04:09Z", "user"=>"iyudushkin", "user_name"=>"Yudushkin Lab", "user_profile_image"=>"http://pbs.twimg.com/profile_images/883008875909320704/J0xM-lhb_normal.jpg"}

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• {"title"=>"File:Diffusion-Crowding-&-Protein-Stability-in-a-Dynamic-Molecular-Model-of-the-Bacterial-Cytoplasm-pcbi.1000694.s014.ogv", "url"=>"http://commons.wikimedia.org/wiki/File:Diffusion-Crowding-&-Protein-Stability-in-a-Dynamic-Molecular-Model-of-the-Bacterial-Cytoplasm-pcbi.1000694.s014.ogv", "timestamp"=>"2018-02-12T17:08:15Z"}
• {"title"=>"File:Diffusion-Crowding-&-Protein-Stability-in-a-Dynamic-Molecular-Model-of-the-Bacterial-Cytoplasm-pcbi.1000694.s015.ogv", "url"=>"http://commons.wikimedia.org/wiki/File:Diffusion-Crowding-&-Protein-Stability-in-a-Dynamic-Molecular-Model-of-the-Bacterial-Cytoplasm-pcbi.1000694.s015.ogv", "timestamp"=>"2018-02-12T17:08:29Z"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/860238"], "description"=>"<p><b>A</b>. Computed stabilization of the folded state relative to the unfolded state for two experimentally-studied proteins; experimental data for Lrp (λ_6-85) and CRABP taken from refs <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#pcbi.1000694-Ghaemmaghami1\" target=\"_blank\">[4]</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#pcbi.1000694-Ignatova2\" target=\"_blank\">[32]</a> respectively. ‘steric sampling’ indicates that insertions were performed on snapshots taken from a BD simulation performed with the ‘steric’ energy function; ‘steric scoring’ etc. indicates that the ‘steric’ energy function was used to calculate the cytoplasm-interaction energies, E_int, of the inserted proteins. <b>B</b>. Histogram of interaction energies, E_int, obtained for all non-clashing insertions of the folded and unfolded state conformations of CRABP with snapshots sampled from the ‘full’ model BD simulations; inset shows the same for λ_6-85. <b>C</b>. Distribution of radius of gyration values for the 1000 unfolded conformations generated with the RCG software <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#pcbi.1000694-Jha1\" target=\"_blank\">[31]</a>; distributions are plotted in order of increasing molecular weight of the studied proteins. <b>D</b>. Same as A. but showing computed results for six other proteins, listed in order of increasing molecular weight. <b>E</b>. Computed stabilization of dimeric form relative to two separated monomers for eleven proteins, listed in order of increasing molecular weight. <b>F</b>. Computed stabilization of oligomeric form relative to separated monomers for three proteins.</p>", "links"=>[], "tags"=>["cytoplasm", "folding"], "article_id"=>530696, "categories"=>["Biophysics", "Computational Biology"], "users"=>["Sean R. McGuffee", "Adrian H. Elcock"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000694.g004", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thermodynamic_effects_of_the_cytoplasm_model_on_protein_folding_and_association_equilibria_/530696", "title"=>"Thermodynamic effects of the cytoplasm model on protein folding and association equilibria.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:11:36"}
• {"files"=>["https://ndownloader.figshare.com/files/860091"], "description"=>"<p><b>A</b>. D_trans values for the three most abundant proteins plotted versus observation interval δt; error bars indicate the standard deviation of values obtained from three independent simulations; solid lines represent fits to the data obtained by integrating the analytical functions shown in the next panel. <b>B</b>. Computed anomality exponents, α, obtained by numerically differentiating the D_trans values shown in <b>A</b>; solid lines represent fits to the data using an analytical function defined in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#s4\" target=\"_blank\">Methods</a>. <b>C</b>. Anomality exponent, α, computed at the shortest accessible time interval (δt_mid = 144ps) plotted for all molecule types versus molecular weight; error bars represent standard deviations from the three independent BD simulations. <b>D</b>. Long-time D_trans values expressed relative to infinite-dilution values plotted versus molecular weight of each molecule type; asterisk denotes GFP. <b>E</b>. Short-time D_rot values expressed relative to infinite-dilution values plotted versus molecular weight of each molecule type. <b>F</b>. Ratio of the effective translational and effective rotational viscosities, plotted for all molecule types versus molecule weight.</p>", "links"=>[], "tags"=>["rotational", "diffusion", "cytoplasm"], "article_id"=>530553, "categories"=>["Biophysics", "Computational Biology"], "users"=>["Sean R. McGuffee", "Adrian H. Elcock"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000694.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Translational_and_rotational_diffusion_in_the_cytoplasm_model_/530553", "title"=>"Translational and rotational diffusion in the cytoplasm model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:09:13"}
• {"files"=>["https://ndownloader.figshare.com/files/427596", "https://ndownloader.figshare.com/files/427638", "https://ndownloader.figshare.com/files/427667", "https://ndownloader.figshare.com/files/427699", "https://ndownloader.figshare.com/files/427721", "https://ndownloader.figshare.com/files/427745", "https://ndownloader.figshare.com/files/427762", "https://ndownloader.figshare.com/files/427786", "https://ndownloader.figshare.com/files/427808", "https://ndownloader.figshare.com/files/427830", "https://ndownloader.figshare.com/files/427890", "https://ndownloader.figshare.com/files/427958", "https://ndownloader.figshare.com/files/427999", "https://ndownloader.figshare.com/files/428040", "https://ndownloader.figshare.com/files/428075"], "description"=>"<div><p>A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed <em>in vitro</em> accurately reflects their behavior <em>in vivo</em>. A number of sophisticated experimental techniques now allow the behavior of individual types of macromolecule to be studied directly <em>in vivo</em>; none, however, allow a wide range of molecule types to be observed simultaneously. In order to tackle this issue we have adopted a computational perspective, and, having selected the model prokaryote <em>Escherichia coli</em> as a test system, have assembled an atomically detailed model of its cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations. Brownian dynamics (BD) simulations of the cytoplasm model have been calibrated to reproduce the translational diffusion coefficients of Green Fluorescent Protein (GFP) observed <em>in vivo</em>, and “snapshots” of the simulation trajectories have been used to compute the cytoplasm's effects on the thermodynamics of protein folding, association and aggregation events. The simulation model successfully describes the relative thermodynamic stabilities of proteins measured in <em>E. coli</em>, and shows that effects additional to the commonly cited “crowding” effect must be included in attempts to understand macromolecular behavior <em>in vivo</em>.</p></div>", "links"=>[], "tags"=>["crowding", "molecular", "bacterial", "cytoplasm"], "article_id"=>144427, "categories"=>["Biophysics", "Biological Sciences"], "users"=>["Sean R. McGuffee", "Adrian H. Elcock"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000694.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s010", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s011", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s012", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s013", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s014", "https://dx.doi.org/10.1371/journal.pcbi.1000694.s015"], "stats"=>{"downloads"=>12, "page_views"=>78, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Diffusion_Crowding_Protein_Stability_in_a_Dynamic_Molecular_Model_of_the_Bacterial_Cytoplasm/144427", "title"=>"Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-03-05 01:13:47"}
• {"files"=>["https://ndownloader.figshare.com/files/859945"], "description"=>"<p><b>A</b>. Extrapolated long-time D_trans values for GFP from BD simulations performed with different energy models; ‘ε’ refers to the well-depth (in kcal/mol) of the Lennard-Jones potential used to describe hydrophobic interactions (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#s4\" target=\"_blank\">Methods</a>). Solid, long-dash, short-dash and dotted lines are the experimental D_trans values from refs. 14, 15, 16 and 17 respectively. The vertical arrow indicates the energy model selected for further BD simulation. <b>B</b>. Average of the maximum distance moved during the 15µs of production for all molecule types plotted versus their molecular weights. Upper error bars indicate the largest value of the maximum distance moved found for any molecule of that type; lower error bars indicate the smallest value of the maximum distance moved. All distances expressed in terms of the molecular diameters (obtained by doubling the hydrodynamic radius calculated by HydroPro <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#pcbi.1000694-delaTorre1\" target=\"_blank\">[88]</a>. <b>C</b>. Average number of unique neighbors encountered by each molecule type as a function of simulation time; each line refers to a different molecule type. <b>D</b>. Average number of neighbors possessed by each molecule type at any instant, plotted versus molecular weight. <b>E</b>. Time constant for the slower of the two exponentials describing the rate at which neighbors are lost, plotted for each molecule type versus molecular weight. <b>F</b>. Average number of times that each molecule type's immediate neighbors exchange during 15µs simulation plotted versus molecular weight of each molecule type.</p>", "links"=>[], "tags"=>["sampling", "cytoplasm"], "article_id"=>530404, "categories"=>["Biophysics", "Computational Biology"], "users"=>["Sean R. McGuffee", "Adrian H. Elcock"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000694.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameterization_and_sampling_in_the_cytoplasm_model_/530404", "title"=>"Parameterization and sampling in the cytoplasm model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:06:44"}
• {"files"=>["https://ndownloader.figshare.com/files/859811"], "description"=>"<p><b>A</b>. Schematic inventory of the contents of the cytoplasm model. <b>B</b>. Rendering of the cytoplasm model at the end of a Brownian dynamics simulation performed with the ‘full’ energy model (see text). RNA is shown as green and yellow. This figure was prepared with VMD <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000694#pcbi.1000694-Humphrey1\" target=\"_blank\">[110]</a>.</p>", "links"=>[], "tags"=>["cytoplasm"], "article_id"=>530275, "categories"=>["Biophysics", "Computational Biology"], "users"=>["Sean R. McGuffee", "Adrian H. Elcock"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000694.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_cytoplasm_model_/530275", "title"=>"The cytoplasm model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:04:35"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime | Further Information

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• {"year"=>2015, "month"=>1, "doi"=>"10.1371/journal.pcbi.1000694", "f1000_id"=>"2680956", "url"=>"http://f1000.com/prime/2680956", "score"=>6, "classifications"=>["controversial", "new_finding", "technical_advance"], "updated_at"=>"2015-01-26T09:40:33Z"}
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• {"year"=>2015, "month"=>5, "doi"=>"10.1371/journal.pcbi.1000694", "f1000_id"=>"2680956", "url"=>"http://f1000.com/prime/2680956", "score"=>6, "classifications"=>["controversial", "new_finding", "technical_advance"], "updated_at"=>"2015-05-25T08:40:32Z"}
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