Detection of Functional Modes in Protein Dynamics
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{"title"=>"Detection of functional modes in protein dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Jochen S.", "last_name"=>"Hub", "scopus_author_id"=>"14056233000"}, {"first_name"=>"Bert L.", "last_name"=>"De Groot", "scopus_author_id"=>"7003590166"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-70049114950", "sgr"=>"70049114950", "pui"=>"355237313", "isbn"=>"10.1371/journal.pcbi.1000480", "pmid"=>"19714202", "doi"=>"10.1371/journal.pcbi.1000480"}, "id"=>"33574208-52c0-36a8-b552-b147292d9009", "abstract"=>"Proteins frequently accomplish their biological function by collective atomic motions. Yet the identification of collective motions related to a specific protein function from, e.g., a molecular dynamics trajectory is often non-trivial. Here, we propose a novel technique termed \"functional mode analysis\" that aims to detect the collective motion that is directly related to a particular protein function. Based on an ensemble of structures, together with an arbitrary \"functional quantity\" that quantifies the functional state of the protein, the technique detects the collective motion that is maximally correlated to the functional quantity. The functional quantity could, e.g., correspond to a geometric, electrostatic, or chemical observable, or any other variable that is relevant to the function of the protein. In addition, the motion that displays the largest likelihood to induce a substantial change in the functional quantity is estimated from the given protein ensemble. Two different correlation measures are applied: first, the Pearson correlation coefficient that measures linear correlation only; and second, the mutual information that can assess any kind of interdependence. Detecting the maximally correlated motion allows one to derive a model for the functional state in terms of a single collective coordinate. The new approach is illustrated using a number of biomolecules, including a polyalanine-helix, T4 lysozyme, Trp-cage, and leucine-binding protein.", "link"=>"http://www.mendeley.com/research/detection-functional-modes-protein-dynamics", "reader_count"=>209, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>66, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>75, "Student > Postgraduate"=>6, "Student > Master"=>19, "Other"=>2, "Student > Bachelor"=>10, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>66, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>75, "Student > Postgraduate"=>6, "Student > Master"=>19, "Other"=>2, "Student > Bachelor"=>10, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>85, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Chemistry"=>39, "Computer Science"=>15, "Engineering"=>11, "Biochemistry, Genetics and Molecular Biology"=>25, "Materials Science"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Physics and Astronomy"=>17, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>17}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>11}, "Chemistry"=>{"Chemistry"=>39}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>85}, "Computer Science"=>{"Computer Science"=>15}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>25}}, "reader_count_by_country"=>{"Hungary"=>1, "United States"=>10, "United Kingdom"=>6, "Portugal"=>2, "India"=>2, "Canada"=>1, "Czech Republic"=>1, "Vietnam"=>1, "Turkey"=>1, "Norway"=>4, "Poland"=>1, "Brazil"=>1, "Italy"=>4, "France"=>2, "Germany"=>6}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/885864"], "description"=>"<p>(A) HSAS (black) during unfolding events of Trp-cage simulations 1 and 2 and the prediction by the HSAS model (gray). The correlations between model and data are printed as insets. To facilitate the comparison between data and model in these plots, all HSAS curves were slightly smoothed by running averages. The were computed from the non-smoothed data (not shown). The HSAS and the prediction of all six unfolding events are shown in the supporting material. (B) Scatter plot of HSAS data versus model as collected from all six unfolding events.</p>", "links"=>[], "tags"=>["hsas", "unfolding"], "article_id"=>556330, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictive_power_of_HSAS_model_during_the_initial_unfolding_events_/556330", "title"=>"Predictive power of HSAS model during the initial unfolding events.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:45:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/439060", "https://ndownloader.figshare.com/files/439096", "https://ndownloader.figshare.com/files/439174", "https://ndownloader.figshare.com/files/439206", "https://ndownloader.figshare.com/files/439226", "https://ndownloader.figshare.com/files/439245", "https://ndownloader.figshare.com/files/439262", "https://ndownloader.figshare.com/files/439308", "https://ndownloader.figshare.com/files/439347"], "description"=>"<div><p>Proteins frequently accomplish their biological function by collective atomic motions. Yet the identification of collective motions related to a specific protein function from, e.g., a molecular dynamics trajectory is often non-trivial. Here, we propose a novel technique termed “functional mode analysis” that aims to detect the collective motion that is directly related to a particular protein function. Based on an ensemble of structures, together with an arbitrary “functional quantity” that quantifies the functional state of the protein, the technique detects the collective motion that is maximally correlated to the functional quantity. The functional quantity could, e.g., correspond to a geometric, electrostatic, or chemical observable, or any other variable that is relevant to the function of the protein. In addition, the motion that displays the largest likelihood to induce a substantial change in the functional quantity is estimated from the given protein ensemble. Two different correlation measures are applied: first, the Pearson correlation coefficient that measures linear correlation only; and second, the mutual information that can assess any kind of interdependence. Detecting the maximally correlated motion allows one to derive a model for the functional state in terms of a single collective coordinate. The new approach is illustrated using a number of biomolecules, including a polyalanine-helix, T4 lysozyme, Trp-cage, and leucine-binding protein.</p></div>", "links"=>[], "tags"=>["detection", "modes"], "article_id"=>146571, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000480.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000480.s009"], "stats"=>{"downloads"=>19, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Detection_of_Functional_Modes_in_Protein_Dynamics/146571", "title"=>"Detection of Functional Modes in Protein Dynamics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-08-28 01:49:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/885971"], "description"=>"<p>(A) Apo structure of LBP in cartoon and surface representation. (B) RMSD with respect to the apo structure versus simulation time. Model building and cross-validation sets are highlighted by red and green background, respectively. (C) RMSD versus the collective coordinate optimized via mutual information (MI). Model building set (red dots), cross-validation set (green dots), and spline fitted to the model building set (black curve). (D) Scatter plot of cross-validation set showing model versus data. Optimizing MI yields favorable correlation (red dots, ), optimizing the Pearson coefficient only poor correlation (black dots, ). (E) Correlations and of the model building and cross-validation sets, respectively, versus the number of PCA vectors used during the optimization. MI optimization (green/red curves) is compared to Pearson optimization (blue/black curves). (F) Convergence of FMA with simulation time in the model building set (MBS). and (from Pearson and MI optimization, compare legend) are showns as a function of simulation time in the MBS using 10 PCA vectors as basis set. All remaining frames of the 100-ns trajectory were applied as cross validation set.</p>", "links"=>[], "tags"=>["rmsd", "leucine-binding", "apo"], "article_id"=>556430, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g008", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_mode_analysis_of_the_RMSD_of_leucine_binding_protein_LBP_with_respect_to_its_apo_structure_/556430", "title"=>"Functional mode analysis of the RMSD of leucine-binding protein (LBP) with respect to its apo structure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:47:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/885794"], "description"=>"<p>(A) Cartoon representation of the ensemble-weighted MCM contributing to the increase in HSAS. Side chains are shown as sticks. (B) Eigenvalues of the principal components (PCs), (C) components of the collective vector with respect to the PCA vectors . (D) Contribution and (E) cumulative contribution of the PC to the variance of the model. The dashed line indicates the variance of the HSAS during the simulation.</p>", "links"=>[], "tags"=>["hydrophobic", "solvent-accessible"], "article_id"=>556253, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g006", "stats"=>{"downloads"=>5, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Collective_motion_related_to_the_increase_in_hydrophobic_solvent_accessible_surface_HSAS_of_Trp_cage_/556253", "title"=>"Collective motion related to the increase in hydrophobic solvent-accessible surface (HSAS) of Trp-cage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:44:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/885722"], "description"=>"<p>(A) Trp-cage protein in the folded state, shown in cartoon and surface representation. The HSAS is shown as orange surface, the hydrophilic SAS as blue surface. (B) HSAS during 8 independent simulations (sim 1-8). HSAS (black curves), and to guide the eye, the HSAS smoothed by a moving average (red curves). In simulation frames highlighted by a yellow background, Trp-cage was considered as folded. The initial unfolding events are highlighted by a blue background. (C) HSAS versus simulation time (black curve) combined from all folded states of the 8 Trp-cage simulations (B). The first 100 ns were used as model building set (red background), the remaining 83.3 ns as cross-validation set (green background). (D) Scatter plot of the data versus the model using the cross-validation set. (E) Correlations and of the model building and cross-validation sets, respectively, versus the number of PCA vectors used during the optimization.</p>", "links"=>[], "tags"=>["hydrophobic", "solvent-accessible", "trp-cage", "folded"], "article_id"=>556178, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g005", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_mode_analysis_of_the_hydrophobic_solvent_accessible_surface_HSAS_of_Trp_cage_in_the_folded_state_/556178", "title"=>"Functional mode analysis of the hydrophobic solvent-accessible surface (HSAS) of Trp-cage in the folded state.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:42:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/885437"], "description"=>"<p>(A/B) T4L in cartoon and surface representation. The catalytic cleft is shown as red surface (A), and Glu11 and Asp20 are depicted in ball-and-stick representation (B). (C) The motions along the first three PCA vectors. (D) , and (F), versus the simulation time (black and blue curves, respectively). The first 180 ns (250 ns for ) were used as model building sets (red background), the remaining simulation frames as cross-validation sets (green background). (E/G) Scatter plots of the data versus the model using the cross-validation sets only. (H) Correlations and for (black curves) and (blue curves), presented as a function of the number of principal components used during the optimization.</p>", "links"=>[], "tags"=>["catalytic", "cleft", "glu11-asp20", "t4", "lysozyme"], "article_id"=>555892, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_mode_analysis_of_catalytic_cleft_volume_and_Glu11_Asp20_distance_of_T4_lysozyme_T4L_/555892", "title"=>"Functional mode analysis of catalytic cleft volume and Glu11-Asp20 distance of T4 lysozyme (T4L).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:38:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/886066"], "description"=>"<p>(A) Backbone representation of the ensemble-weighted MCM motion contributing to the increase in RMSD. (B) components of the collective vector with respect to the PCA vectors . (C) Eigenvalues of the principal components (PCs), (D) Contribution of the PC to the variance of the collective coordinate .</p>", "links"=>[], "tags"=>["rmsd", "leucine-binding"], "article_id"=>556526, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g009", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Collective_motion_related_to_the_increase_in_RMSD_of_leucine_binding_protein_LBP_/556526", "title"=>"Collective motion related to the increase in RMSD of leucine-binding protein (LBP).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:48:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/885342"], "description"=>"<p>(A–C) Irrespective of the energy landscape (thin lines), the MCM (along ) is parallel to the direction with increasing (here, to the right). In contrast, the ewMCM is highly dependent on the energy landscape. (A) If no direction is favored by the energy landscape, the ewMCM is parallel to the MCM. (B) If one direction (PCA vector 1) is highly favored over another direction (PCA vector 2), a displacement along the MCM is mainly accomplished through a motion along the PCA vector 1. Therefore, the ewMCM is nearly parallel to PCA vector 1 in this case. (C) An intermediate situation between the extreme cases (A) and (B). In that case, both PCA vectors 1 and 2 contribute to the ewMCM.</p>", "links"=>[], "tags"=>["maximally", "correlated", "ensemble-weighted", "mcm", "contributing"], "article_id"=>555796, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g001", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_On_the_difference_between_the_maximally_correlated_motion_MCM_and_the_ensemble_weighted_MCM_ewMCM_contributing_to_the_functional_quantity_f_/555796", "title"=>"On the difference between the maximally correlated motion (MCM) and the ensemble-weighted MCM (ewMCM) contributing to the functional quantity <i>f</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:36:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/885639"], "description"=>"<p>(A) components of with respect to the PCA vectors . (B) Variances of the principal components (PCs), (C) contribution of the PC to the variance of the model, and (D) the cumulative contribution of principal component to the variance of the model. The dashed lines indicates the variances of and during the simulation.</p>", "links"=>[], "tags"=>["vector", "lysozyme", "cleft", "glu11", "asp20"], "article_id"=>556093, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Collective_vector__related_to_the_lysozyme_cleft_volume_black_and_to_the_distance_between_Glu11_and_Asp20_blue_/556093", "title"=>"Collective vector ‘’ related to the lysozyme cleft volume (black) and to the distance between Glu11 and Asp20 (blue).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:41:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/885539"], "description"=>"<p> (gray) and (black curve) as a function of simulation time in the model building set (MBS). For each data point in the figure, all remaining frames from the 460-ns trajectory were used as cross validation set, and the first 20 PCA vectors were applied as the basis set to construct . The inset displays the simulation time in the MBS is in a detailed scale. Applying approx. 10 ns of simulation as MBS is sufficient to yield a reasonable model () for the remining frames. Applying less than 0.5 ns as MBS yields an highly overfitted model.</p>", "links"=>[], "tags"=>["fma", "lysozyme", "cleft"], "article_id"=>555999, "categories"=>["Biophysics"], "users"=>["Jochen S. Hub", "Bert L. de Groot"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000480.g003", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Convergence_of_FMA_of_the_lysozyme_cleft_volume_/555999", "title"=>"Convergence of FMA of the lysozyme cleft volume .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-28 01:39:59"}

PMC Usage Stats | Further Information

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Relative Metric

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