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{"title"=>"Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical dirichlet process model", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel", "last_name"=>"Ting", "scopus_author_id"=>"36193376300"}, {"first_name"=>"Guoli", "last_name"=>"Wang", "scopus_author_id"=>"55738558900"}, {"first_name"=>"Maxim", "last_name"=>"Shapovalov", "scopus_author_id"=>"6602660269"}, {"first_name"=>"Rajib", "last_name"=>"Mitra", "scopus_author_id"=>"55085027100"}, {"first_name"=>"Michael I.", "last_name"=>"Jordan", "scopus_author_id"=>"7202293939"}, {"first_name"=>"Roland L.", "last_name"=>"Dunbrack", "scopus_author_id"=>"7003392559"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-77954609748", "sgr"=>"77954609748", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1000763", "pmid"=>"20442867", "isbn"=>"10.1371/journal.pcbi.1000763", "pui"=>"359319053"}, "id"=>"6ac1944b-e7ae-33e9-97c5-b502926f7d9b", "abstract"=>"Distributions of the backbone dihedral angles of proteins have been studied for over 40 years. While many statistical analyses have been presented, only a handful of probability densities are publicly available for use in structure validation and structure prediction methods. The available distributions differ in a number of important ways, which determine their usefulness for various purposes. These include: 1) input data size and criteria for structure inclusion (resolution, R-factor, etc.); 2) filtering of suspect conformations and outliers using B-factors or other features; 3) secondary structure of input data (e.g., whether helix and sheet are included; whether beta turns are included); 4) the method used for determining probability densities ranging from simple histograms to modern nonparametric density estimation; and 5) whether they include nearest neighbor effects on the distribution of conformations in different regions of the Ramachandran map. In this work, Ramachandran probability distributions are presented for residues in protein loops from a high-resolution data set with filtering based on calculated electron densities. Distributions for all 20 amino acids (with cis and trans proline treated separately) have been determined, as well as 420 left-neighbor and 420 right-neighbor dependent distributions. The neighbor-independent and neighbor-dependent probability densities have been accurately estimated using Bayesian nonparametric statistical analysis based on the Dirichlet process. In particular, we used hierarchical Dirichlet process priors, which allow sharing of information between densities for a particular residue type and different neighbor residue types. The resulting distributions are tested in a loop modeling benchmark with the program Rosetta, and are shown to improve protein loop conformation prediction significantly. The distributions are available at http://dunbrack.fccc.edu/hdp.", "link"=>"http://www.mendeley.com/research/neighbordependent-ramachandran-probability-distributions-amino-acids-developed-hierarchical-dirichle", "reader_count"=>139, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>31, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>3, "Student > Master"=>19, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>31, "Student > Doctoral Student"=>7, "Student > Ph. D. 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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/99d3c464-bc64-48ce-b976-39b19c9c6ef6", "title"=>"TorusDBN by Boomsma et al.", "body"=>"Dear colleagues,\n\nThe statistically rigorous description of biomolecular structure is an important and timely endeavor, which promises to revolutionize protein structure prediction, simulation and design. We compliment the authors for a contribution to an important emerging field.\n\nHowever, we would also like to point to a closely related article that was published by us in 2008 in Proc. Natl. Acad. Sci. USA. <http://www.pnas.org/content/105/26/8932.abstract?etoc>. This article has unfortunately escaped the attention of Ting et al., it seems.\n\nOur article also presents a probabilistic model (called TorusDBN) of the phi, psi backbone angles in continuous space. As suggested (but not implemented) by Ting et al., our work combines a graphical model with directional statistics, resulting in a probabilistic model that incorporates long-range neighbor dependencies.\n\nAs the authors rightfully point out, \"Many other such statistical analyses [of] phi,psi data have appeared previously [...], but few are publicly available for use in structure validation or structure prediction.\" We point out that our method is freely available from SourceForge <http://sourceforge.net/projects/phaistos/> under the GPL license.\n\nBest regards,\n\nWouter Boomsma, University of Cambridge, UK\n\nKanti V. Mardia and Charles C. Taylor, University of Leeds, UK\n\nJesper Ferkinghoff-Borg, DTU, Denmark\n\nAnders Krogh and Thomas Hamelryck, University of Copenhagen, Denmark", "isRemoved"=>false, "created"=>"2010-05-20T11:41:24Z", "lastModified"=>"2010-05-20T11:41:24Z", "creator"=>{"userId"=>"21139"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000763", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>1, "mostRecentActivity"=>"2010-05-20T22:42:01Z", "replies"=>[{"type"=>"REPLY", "parentID"=>13923, "annotationUri"=>"info:doi/10.1371/reply/842ef61f-c79a-448f-96b4-95e545ad1fb2", "title"=>"RE: TorusDBN by Boomsma et al.", "body"=>"Thanks for pointing out your paper, Thomas. And I apologize that we neglected to cite it in our paper. \n\nI guess the two methods may be used in different ways, or at least in different contexts of how people construct their structure prediction programs. Ours is a set of files that contain probability distributions, which people may use however they wish and they may find them easy to understand and use. At least some of the neighbor interactions that produce different effects may be readily observed. \n\nI gather yours is a program that generates probabilities or samples on demand, and as with hidden Markov models it is not necessarily designed for interpretation of the transition probabilities or emission probabilities. However it may be better at generating samples and clearly has some features not included in ours (secondary structure effects, for instance; ours is based only on loop conformations, and in particular optimized for loop structure prediction).", "isRemoved"=>false, "created"=>"2010-05-20T22:42:01Z", "lastModified"=>"2010-05-20T22:42:01Z", "creator"=>{"userId"=>"35709"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000763", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2010-05-20T22:42:01Z", "replies"=>[]}]}

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• {"month"=>"11", "year"=>"2014", "pdf_views"=>"10", "xml_views"=>"3", "html_views"=>"86"}
• {"month"=>"12", "year"=>"2014", "pdf_views"=>"10", "xml_views"=>"2", "html_views"=>"74"}
• {"month"=>"1", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"84"}
• {"month"=>"2", "year"=>"2015", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"55"}
• {"month"=>"3", "year"=>"2015", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"68"}
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• {"month"=>"5", "year"=>"2015", "pdf_views"=>"17", "xml_views"=>"0", "html_views"=>"74"}
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• {"month"=>"11", "year"=>"2015", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"47"}
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• {"month"=>"2", "year"=>"2016", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"55"}
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• {"month"=>"4", "year"=>"2016", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"29"}
• {"month"=>"5", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"45"}
• {"month"=>"6", "year"=>"2016", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"51"}
• {"month"=>"7", "year"=>"2016", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"48"}
• {"month"=>"8", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"40"}
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• {"month"=>"10", "year"=>"2016", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"61"}
• {"month"=>"11", "year"=>"2016", "pdf_views"=>"1", "xml_views"=>"0", "html_views"=>"53"}
• {"month"=>"12", "year"=>"2016", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"44"}
• {"month"=>"1", "year"=>"2017", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"45"}
• {"month"=>"2", "year"=>"2017", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"64"}
• {"month"=>"3", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"51"}
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• {"month"=>"8", "year"=>"2017", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"39"}
• {"month"=>"9", "year"=>"2017", "pdf_views"=>"29", "xml_views"=>"2", "html_views"=>"60"}
• {"month"=>"10", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"119"}
• {"month"=>"11", "year"=>"2017", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"234"}
• {"month"=>"12", "year"=>"2017", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"65"}
• {"month"=>"1", "year"=>"2018", "pdf_views"=>"22", "xml_views"=>"0", "html_views"=>"49"}
• {"month"=>"2", "year"=>"2018", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"41"}
• {"month"=>"3", "year"=>"2018", "pdf_views"=>"17", "xml_views"=>"0", "html_views"=>"31"}
• {"month"=>"4", "year"=>"2018", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"36"}
• {"month"=>"5", "year"=>"2018", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"31"}
• {"month"=>"6", "year"=>"2018", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"17"}
• {"month"=>"7", "year"=>"2018", "pdf_views"=>"3", "xml_views"=>"3", "html_views"=>"14"}
• {"month"=>"8", "year"=>"2018", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"35"}
• {"month"=>"9", "year"=>"2018", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"31"}
• {"month"=>"10", "year"=>"2018", "pdf_views"=>"16", "xml_views"=>"1", "html_views"=>"36"}
• {"month"=>"11", "year"=>"2018", "pdf_views"=>"9", "xml_views"=>"1", "html_views"=>"25"}
• {"month"=>"12", "year"=>"2018", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"39"}
• {"month"=>"1", "year"=>"2019", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"24"}
• {"month"=>"2", "year"=>"2019", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"25"}
• {"month"=>"3", "year"=>"2019", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"26"}
• {"month"=>"4", "year"=>"2019", "pdf_views"=>"20", "xml_views"=>"0", "html_views"=>"22"}
• {"month"=>"5", "year"=>"2019", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"24"}
• {"month"=>"6", "year"=>"2019", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"21"}
• {"month"=>"7", "year"=>"2019", "pdf_views"=>"20", "xml_views"=>"0", "html_views"=>"23"}
• {"month"=>"8", "year"=>"2019", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"19"}
• {"month"=>"9", "year"=>"2019", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"22"}
• {"month"=>"10", "year"=>"2019", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"15"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/852937"], "description"=>"<p><b>A</b>. ILE (left) – ALA (center conformation A, coil, PDB entry 1N55, residue 69 of chain A). Ile likely prevents H-bond acceptor to N_i+1. <b>B</b>. VAL (right) – ARG (cent, A, coil, 2FWH_527A). Val likely prevents H-bond donor to O_i−1. <b>C</b>. ASN (left) – SER (cent, A, coil, 3C8W_9A). Asn forms hydrogen bond to N_i+1. <b>D</b>. PRO (right) – ARG (cent, A, coil, 2RKV_296A). Very close steric bump of Pro CD with N<i>_i</i>. <b>E</b>. PHE (left) – SER (cent, B, coil, 2GS8_248A). Favorable hydrophobic interaction with Tyr<i>_i</i>₊₁. <b>F</b>. TYR (right) – PRO (cent, P, turn, 3CA8_34A). Tyr unfavorable electro. interaction with O_i−1.</p>", "links"=>[], "tags"=>["computational biology/protein structure prediction", "mathematics/statistics"], "article_id"=>523392, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g009", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neighbor_dependent_effects_/523392", "title"=>"Neighbor-dependent effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:42:39"}
• {"files"=>["https://ndownloader.figshare.com/files/852745"], "description"=>"<p><b>A</b>: left neighbors. <b>B</b>: right neighbors. The distance between any two neighboring residue in the plots is approximate. “ALL” indicates all residues as neighbor and is boxed in each figure.</p>", "links"=>[], "tags"=>["scaling", "plots", "hellinger"], "article_id"=>523201, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g007", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multi_dimensional_scaling_plots_of_average_Hellinger_distances_/523201", "title"=>"Multi-dimensional scaling plots of average Hellinger distances.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:41:33"}
• {"files"=>["https://ndownloader.figshare.com/files/853196"], "description"=>"<p>The first row is the percent in each region for each secondary structure type (Stride C, T, or T+C+B). Top: right neighbors. Bottom: left neighbors. Each cell gives the difference as a percentage of all residues in the secondary structure type. E.g., 40.3% of non-pre-Pro Coil residues are in a P conformation, while 45.2% (40.3+4.9) of non-pre-Pro Coil residues with Ile as right neighbor are in a P conformation. Negative numbers are in bold italic type.</p>", "links"=>[], "tags"=>["ramachandran", "regions", "nearest", "neighbors"], "article_id"=>523650, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.t006", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Changes_in_Ramachandran_regions_for_right_and_left_nearest_neighbors_in_Coil_Turn_and_All_loop_residues_/523650", "title"=>"Changes in Ramachandran regions for right and left nearest neighbors in Coil, Turn, and All loop residues.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 01:44:08"}
• {"files"=>["https://ndownloader.figshare.com/files/852459"], "description"=>"<p>Columns (left to right): central residues ALA, LYS, TYR, VAL. Rows (top to bottom): right neighbors pro, gly, val, gln. All plots are scaled to a common maximum probability height.</p>", "links"=>[], "tags"=>["neighbor-dependent", "ramachandran", "distributions", "residues"], "article_id"=>522915, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g005", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Right_Neighbor_dependent_Ramachandran_distributions_of_four_residues_with_different_right_neighbors_/522915", "title"=>"Right Neighbor-dependent Ramachandran distributions of four residues with different right neighbors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:39:51"}
• {"files"=>["https://ndownloader.figshare.com/files/852628"], "description"=>"<p>Columns (left to right): central residues ALA, LYS, and GLU. Rows (top to bottom): left neighbors val, pro, ser, and asp. Vertical scale is the same as <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000763#pcbi-1000763-g005\" target=\"_blank\">Figure 5</a>.</p>", "links"=>[], "tags"=>["neighbor-dependent", "ramachandran", "distributions", "residue"], "article_id"=>523080, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Left_neighbor_dependent_Ramachandran_distributions_of_three_residue_types_/523080", "title"=>"Left neighbor-dependent Ramachandran distributions of three residue types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:40:53"}
• {"files"=>["https://ndownloader.figshare.com/files/852252"], "description"=>"<p>All plots are scaled to a common maximum probability height.</p>", "links"=>[], "tags"=>["neighbor-dependent", "ramachandran", "distributions", "glutamine", "20", "amino", "types"], "article_id"=>522701, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g004", "stats"=>{"downloads"=>3, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Right_neighbor_dependent_Ramachandran_distributions_of_glutamine_with_each_of_20_amino_acid_types_as_right_neighbors_/522701", "title"=>"Right neighbor-dependent Ramachandran distributions of glutamine with each of 20 amino acid types as right neighbors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:38:41"}
• {"files"=>["https://ndownloader.figshare.com/files/851833"], "description"=>"<p>For axis information, see caption to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000763#pcbi-1000763-g001\" target=\"_blank\">Figure 1</a>.</p>", "links"=>[], "tags"=>["ramachandran", "distributions", "amino", "types"], "article_id"=>522287, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neighbor_independent_Ramachandran_distributions_for_remaining_9_amino_acid_types_continued_from_Figure_1_/522287", "title"=>"Neighbor-independent Ramachandran distributions for remaining 9 amino acid types (continued from Figure 1).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:36:24"}
• {"files"=>["https://ndownloader.figshare.com/files/853014"], "description"=>"<p>TCBIG includes Turn, Coil, Bridge, π-helix, and 3₁₀-helix residues.</p><p>TCB includes Turn, Coil, and Bridge residues.</p>", "links"=>[], "tags"=>["assignments"], "article_id"=>523472, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.t001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ramachandran_and_secondary_structure_assignments_for_data_set_in_percent_/523472", "title"=>"Ramachandran and secondary structure assignments for data set (in percent).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 01:43:10"}
• {"files"=>["https://ndownloader.figshare.com/files/851654"], "description"=>"<p>The axes are as follows: x-axis (horizontal) = φ from −180° to 180° with gridlines and ticks at −180°, −90°, 0°, +90°, and +180°. y-axis = ψ from −180° to 180°. z-axis (vertical) = probability density functions. All plots are scaled to a common maximum probability height, the third highest peak among all plots. Thus, Gly and Pro extend beyond the plotted vertical axis.</p>", "links"=>[], "tags"=>["ramachandran", "distributions", "12", "amino", "types", "cis", "proline", "hdp", "simulation"], "article_id"=>522095, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g001", "stats"=>{"downloads"=>3, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neighbor_independent_Ramachandran_distributions_probability_densities_for_12_amino_acid_types_including_cis_proline_CPR_from_the_HDP_simulation_see_Figure_2_for_remaining_amino_acid_types_/522095", "title"=>"Neighbor-independent Ramachandran distributions (probability densities) for 12 amino acid types including cis proline (CPR) from the HDP simulation (see Figure 2 for remaining amino acid types).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:35:19"}
• {"files"=>["https://ndownloader.figshare.com/files/852856"], "description"=>"<p>Each figure plots the numerically sorted Cα RMSDs for predictions from one form of Ramachandran distributions against the numerically sorted Cα RMSDs from another. A: neighbor-independent TCB distributions vs. original distributions in Rosetta; B: neighbor-dependent TCB distributions vs Rosetta original distributions; C: neighbor-dependent TCB distributions vs neighbor-independent TCB distributions; D: neighbor-dependent TCB distributions vs neighbor-dependent TCBIG distributions.</p>", "links"=>[], "tags"=>["plots", "230", "predictions", "rosetta", "ramchandran"], "article_id"=>523309, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g008", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantile_quantile_QQ_plots_for_230_loop_predictions_by_Rosetta_using_different_Ramchandran_distributions_/523309", "title"=>"Quantile-quantile (QQ) plots for 230 loop predictions by Rosetta using different Ramchandran distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:42:09"}
• {"files"=>["https://ndownloader.figshare.com/files/853123"], "description"=>"<p>Δφ<40° and Δψ<40° indicate predicted φ,ψ position with maximum probability is less than 40 degrees away in φ and ψ respectively from native position calculated with the neighbor-dependent distributions. %imp is the increase in the percent of all residues of each type that are predicted correctly with the neighbor-dependent distributions compared to the neighbor independent distributions. Ave. |Δφ| and |Δψ| are the mean absolute deviations of the most probable φ,ψ values from the native values of φ and ψ calculated with the neighbor-dependent distributions. Deg. Imp indicates how much better predictions are (that is lower) with the neighbor-dependent distributions than with the neighbor-independent distributions.</p>", "links"=>[], "tags"=>["predictions", "probability", "neighbor-dependent", "distributions", "residues", "2074"], "article_id"=>523578, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.t005", "stats"=>{"downloads"=>7, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ramachandran_predictions_for_maximum_probability_of_neighbor_dependent_vs_neighbor_independent_distributions_for_loop_residues_in_2074_proteins_/523578", "title"=>"Ramachandran predictions for maximum probability of neighbor-dependent vs. neighbor independent distributions for loop residues in 2074 proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 01:43:43"}
• {"files"=>["https://ndownloader.figshare.com/files/853054"], "description"=>"<p>Values are 100× Hellinger distance and rounded to the nearest integer.</p>", "links"=>[], "tags"=>["distances", "neighbor-independent", "distributions", "tcbig"], "article_id"=>523515, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.t003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hellinger_distances_for_neighbor_independent_distributions_in_the_TCBIG_set_/523515", "title"=>"Hellinger distances for neighbor-independent distributions in the TCBIG set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 01:43:20"}
• {"files"=>["https://ndownloader.figshare.com/files/852008"], "description"=>"<p>Rows (from top to bottom): Lys, Met, Leu, Glu. Columns (from left to right): TCBIG, TCB, Tonly, Conly. All plots are scaled to a common maximum probability height.</p>", "links"=>[], "tags"=>["sets", "neighbor-independent", "ramachandran", "distributions", "amino"], "article_id"=>522460, "categories"=>["Mathematics", "Medicine"], "users"=>["Daniel Ting", "Guoli Wang", "Maxim Shapovalov", "Rajib Mitra", "Michael I. Jordan", "Roland L. Dunbrack Jr"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000763.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_different_data_sets_for_neighbor_independent_Ramachandran_distributions_for_4_amino_acid_types_/522460", "title"=>"Comparison of different data sets for neighbor-independent Ramachandran distributions for 4 amino acid types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 01:37:21"}
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PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime