Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity
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{"title"=>"Near-native protein loop sampling using nonparametric density estimation accommodating sparcity", "type"=>"journal", "authors"=>[{"first_name"=>"Hyun", "last_name"=>"Joo", "scopus_author_id"=>"36778951300"}, {"first_name"=>"Archana G.", "last_name"=>"Chavan", "scopus_author_id"=>"54400865700"}, {"first_name"=>"Ryan", "last_name"=>"Day", "scopus_author_id"=>"34879797900"}, {"first_name"=>"Kristin P.", "last_name"=>"Lennox", "scopus_author_id"=>"26642012500"}, {"first_name"=>"Paul", "last_name"=>"Sukhanov", "scopus_author_id"=>"54401616900"}, {"first_name"=>"David B.", "last_name"=>"Dahl", "scopus_author_id"=>"36684761700"}, {"first_name"=>"Marina", "last_name"=>"Vannucci", "scopus_author_id"=>"7003284457"}, {"first_name"=>"Jerry", "last_name"=>"Tsai", "scopus_author_id"=>"7403610600"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "sgr"=>"80055085877", "doi"=>"10.1371/journal.pcbi.1002234", "scopus"=>"2-s2.0-80055085877", "pui"=>"362834439", "pmid"=>"22028638"}, "id"=>"291c53d6-eac0-31e3-b0d5-1145ab46016e", "abstract"=>"Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.", "link"=>"http://www.mendeley.com/research/nearnative-protein-loop-sampling-using-nonparametric-density-estimation-accommodating-sparcity", "reader_count"=>8, "reader_count_by_academic_status"=>{"Researcher"=>4, "Student > Ph. D. Student"=>2, "Other"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Researcher"=>4, "Student > Ph. D. Student"=>2, "Other"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>4, "Chemistry"=>2, "Social Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}}, "reader_count_by_country"=>{"Germany"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/721045"], "description"=>"<p>The 97 candidate loops below 1 Å average Cα–Cα termini distance cutoff for the target loop 3bpx from dataset T0617, showing various orientations of the candidate loops (grey) in backbone Cα trace. Reference loop is shown in red stick representation. The best candidate by local superposition in blue and best candidate by global superposition is shown as green. (<b>a</b>) Local superposition of candidate loops to the reference crystal structure with average local RMSD of 1.86 Å. (<b>b</b>) Candidate loops are superposed only at the take-off region (first residue at N-terminus) of the loop. Average global RMSD of candidates to the reference crystal structure is 3.17 Å.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry", "mathematics"], "article_id"=>391401, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_versus_global_superposition_/391401", "title"=>"Local versus global superposition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:23:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/721577"], "description"=>"<p>Of the best candidates, lowest RMSD (Å) and average RMSD (Å) for five loops sampled using DPM-HMM method along with their loop length, number of targets in each group and average RMSD (Å) of all the templates used.</p>a<p>Minimum average RMSD of all the templates in a subgroup.</p>b<p>Maximum average RMSD of all the templates in a subgroup.</p>c<p>Average of mean RMSD of all the templates in the group. Standard deviations are given in parenthesis.</p>d<p>Lowest of all best candidates' RMSD that is sampled in each subgroup of loop targets.</p>e<p>Average of best candidate's RMSDs for every target in each subgroup. Standard deviations are given in parenthesis.</p>", "links"=>[], "tags"=>["modeling", "template", "datasets", "sampled"], "article_id"=>391933, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Loop_modeling_template_datasets_and_accuracy_measure_RMSD_for_the_sampled_candidates_/391933", "title"=>"Loop modeling template datasets and accuracy measure (RMSD) for the sampled candidates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-10-20 00:32:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/721113"], "description"=>"<p>RMSD of the best candidate versus RMSD of the best template. The diagonal line is unity. Points below the line indicate predictions better than the best template. The inset shows the percentage of better and worse predictions in each RMSD bin. When the RMSD of the best templates are below 1 Å, the chances our methods improve the loop are about 38%. When they are between in 1–2 Å, the chances are higher than 75%. In the 2–3 Å range, chances of improvement are higher than 93%. For higher than 3 Å, the loop structures are always improved.</p>", "links"=>[], "tags"=>["sampling"], "article_id"=>391473, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DPM_HMM_Sampling_performance_/391473", "title"=>"DPM-HMM Sampling performance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:24:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/720964"], "description"=>"<p>Examples of DPM-HMM estimated backbone dihedral angle density distributions at various positions of targets from predictions of the CDRH2 loop and anchor residues. The grey dots represent the observed φ,ψ input data at a particular alignment position. The contour lines represent the calculated density estimation calculated from the φ,ψ pair data. The red dots indicate the actual φ,ψ values of the target structure. Position refers to the place in the modeled loop and the PDB code refers to the predicted target. (<b>a</b>) position 1 of 1mfa <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002234#pcbi.1002234-Zdanov1\" target=\"_blank\">[55]</a>, (<b>b</b>) position 6 of 1w72 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002234#pcbi.1002234-Hulsmeyer1\" target=\"_blank\">[56]</a>, (<b>c</b>) position of 6 for 1gig <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002234#pcbi.1002234-Bizebard1\" target=\"_blank\">[57]</a> and (<b>d</b>) position 9 (last anchor residue) of 1rmf <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002234#pcbi.1002234-Jedrzejas1\" target=\"_blank\">[58]</a>.</p>", "links"=>[], "tags"=>["estimations"], "article_id"=>391325, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Density_estimations_of_966_968_distributions_/391325", "title"=>"Density estimations of φ,ψ distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:22:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/365191"], "description"=>"<div><p>Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at <a href=\"http://www.stat.tamu.edu/~dahl/software/cortorgles/\">http://www.stat.tamu.edu/~dahl/software/cortorgles/</a>.</p> </div>", "links"=>[], "tags"=>["near-native", "sampling", "nonparametric", "estimation", "accommodating", "sparcity"], "article_id"=>132174, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234", "stats"=>{"downloads"=>4, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Near_Native_Protein_Loop_Sampling_Using_Nonparametric_Density_Estimation_Accommodating_Sparcity/132174", "title"=>"Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-10-20 00:36:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/721350"], "description"=>"<p>(<b>a</b>) Correlation of the best candidate RMSD with loop length. The prediction shows a linear correlation to loop length. (<b>b</b>) Correlation of RMSD of the best candidate to the number of templates. The candidates decrease in RMSD as the number of templates increases to a cutoff of ∼30 templates, suggesting that more than 30 templates do not improve the sampling in the DPM-HMM method.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry", "mathematics"], "article_id"=>391708, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dependence_of_input_data_length_and_amount_/391708", "title"=>"Dependence of input data: length and amount.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:28:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/721414"], "description"=>"<p>Assessment of sampling efficiency for the 90 loops modeled in the CASP9 experiment (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002234#s4\" target=\"_blank\">Materials and Methods</a> for selection). All loops were modeled with very limited number of templates, mostly 1–5, and with templates of various lengths. For smaller loops with 3–8 residues, global RMSD is mostly below 2.5 Å. For medium sized loops (8–13 amino acids), global RMSD is between 1–3 Å. As the loop length increases, best-sampled conformations have higher RMSD from the native structure. The DPM-HMM fails after 20 residues as shown by the increase in RMSD above 5 Å.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry", "mathematics"], "article_id"=>391769, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CASP9_Loop_sampling_/391769", "title"=>"CASP9 Loop sampling.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:29:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/720853"], "description"=>"<p>Global superposition data set of 465 loops used to test sampling. All representations are in backbone cartoon. (<b>a</b>) 111 target loops from CDRH1 (12 residues), (<b>b</b>) 130 target loops from CDRH2 (7, 8 and 10 residues), (<b>c</b>) 111 target loops from CDRH3 (8, 10–17 residues), (<b>d</b>) 21 loops from CASP9 target, T0617 (12 residues), and (<b>e</b>) 92 globin EF loops (12, 13 and 15 residues).</p>", "links"=>[], "tags"=>["465"], "article_id"=>391210, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_465_loop_data_set_/391210", "title"=>"The 465 loop data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:20:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/721498"], "description"=>"<p>Boxplots display the RMSD sampling distribution of the DPM-HMM method alongside that of the LoopyMod method for loops of different difficulty: canonical (CDRH1) and non-canonical (CDRH3) loops. Comparison of sampling to the canonical CDRH1 is shown by the left 2 boxplots and the comparison to the non-canonical CDRH3 by the right 2 boxplots. In both cases, the DPM-HMM exhibits a tighter distribution and lower median RMSD.</p>", "links"=>[], "tags"=>["sampling"], "article_id"=>391859, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g008", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Loop_sampling_comparison_/391859", "title"=>"Loop sampling comparison.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:30:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/721243"], "description"=>"<p>RMSD of the best candidate versus average RMSD between all the templates. The data points are classified according to the number of templates used for input in the DPM-HMM φ,ψ density estimation. Grey filled circles represent targets with less than 10 templates, open circles are with 10 to 30 templates and black filled circles are with more than 30 templates.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry", "mathematics"], "article_id"=>391602, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry"], "users"=>["Hyun Joo", "Archana G. Chavan", "Ryan Day", "Kristin P. Lennox", "Paul Sukhanov", "David B. Dahl", "Marina Vannucci", "Jerry Tsai"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002234.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Influence_of_the_variation_of_input_data_/391602", "title"=>"Influence of the variation of input data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:26:42"}

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Relative Metric

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