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{"title"=>"Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy", "type"=>"journal", "authors"=>[{"first_name"=>"Arash", "last_name"=>"Bahrami", "scopus_author_id"=>"8937102300"}, {"first_name"=>"Amir H.", "last_name"=>"Assadi", "scopus_author_id"=>"7003910411"}, {"first_name"=>"John L.", "last_name"=>"Markley", "scopus_author_id"=>"36045082900"}, {"first_name"=>"Hamid R.", "last_name"=>"Eghbalnia", "scopus_author_id"=>"6602553214"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"63549111769", "doi"=>"10.1371/journal.pcbi.1000307", "pui"=>"354408119", "pmid"=>"19282963", "scopus"=>"2-s2.0-63549111769", "issn"=>"1553734X", "isbn"=>"1553-734x"}, "id"=>"7a468827-b5ad-3133-9e31-5186459e2fe9", "abstract"=>"The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.", "link"=>"http://www.mendeley.com/research/probabilistic-interaction-network-evidence-algorithm-application-complete-labeling-peak-lists-protei", "reader_count"=>59, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>14, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>27, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>14, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>27, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>1, "Agricultural and Biological Sciences"=>21, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Chemical Engineering"=>1, "Chemistry"=>18, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>18}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>21}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>2, "United Kingdom"=>1}, "group_count"=>0}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/904714"], "description"=>"<p>The peaks in the most sensitive experiments in the data are used initially as reference peaks. Aligning the peaks along the common dimensions and registering them with respect to reference peaks enables us to define a common putative object called the spin system. Spin systems are then assembled to derive triplet spin systems.</p>", "links"=>[], "tags"=>["biophysics/experimental biophysical methods", "biotechnology/protein chemistry and proteomics", "chemical biology/protein chemistry and proteomics", "Computational biology", "mathematics"], "article_id"=>575168, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spin_system_generation_network_in_PINE_NMR_/575168", "title"=>"Spin system generation network in PINE-NMR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:26:08"}
• {"files"=>["https://ndownloader.figshare.com/files/904829"], "description"=>"<p>Overlapping tripeptides (triplet residue) are evaluated. The weights on the edges are derived from amino acid typing, secondary structures, connectivity experiments, and possible outlier assignments. According to the statistical physics model described in the text, application of the belief propagation algorithm yields the marginal probabilities for backbone assignments.</p>", "links"=>[], "tags"=>["backbone"], "article_id"=>575291, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphical_network_for_backbone_chemical_shift_assignments_/575291", "title"=>"Graphical network for backbone chemical shift assignments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:28:11"}
• {"files"=>["https://ndownloader.figshare.com/files/904569"], "description"=>"<p>A set of probabilistic influence sub-networks are combined into a larger influence network. The iterative probabilistic inference on the complex network ensures globally consistent labeling.</p>", "links"=>[], "tags"=>["relationships"], "article_id"=>575029, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_network_of_relationships_in_PINE_NMR_/575029", "title"=>"Global network of relationships in PINE-NMR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:23:49"}
• {"files"=>["https://ndownloader.figshare.com/files/904464"], "description"=>"<p>Each input data point (S) is linked to a set of labels (L) with associated weights. Similarity measures and constraints are utilized to construct each neighborhood system or topology (as denoted by the arrows).</p>", "links"=>[], "tags"=>["neighborhoods", "built"], "article_id"=>574929, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_system_of_neighborhoods_built_around_each_data_value_in_PINE_/574929", "title"=>"Illustration of the system of neighborhoods built around each data value in PINE.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:22:09"}
• {"files"=>["https://ndownloader.figshare.com/files/904391"], "description"=>"<p>Peaks observed in multidimensional spectra are matched to search for common frequencies. Some common frequencies identify atoms within a residue; others identify atoms in neighboring residues. The common visual aid in this process is a series of paired strip plots from complementary NMR experiments. Strips from CBCA(CO)NH (<i>a</i> and <i>c</i>) and HNCACB (<i>b</i> and <i>d</i>) experiments can be used here to assign the tripeptide Thr-Tyr-His. Starting with C^α (CA) and C^β (CB) frequencies assumed to belong to Thr⁶⁶ (strip <i>a</i>), a horizontal trace (line), arising from the common frequency of NH nuclei, is used to locate C^α and C^β of Tyr⁶⁷ in (strip <i>b</i>). To continue the process, the same peaks are located in (strip <i>c</i>), and the peaks are traced to strip <i>d</i>. In strip <i>d</i>, given the accepted tolerances across spectra (shown by boxes around the selected peaks), several alternative assignments are plausible for His⁶⁸. These additional peaks may be artifacts (false peaks), or peaks from other nuclei with similar frequency. Depending on the starting point of the assignment process, the choice of experiments, the amount of conflicting information, or other factors, an exponentially expanding number of alternative assignments can arise, rendering a computational solution intractable. This difficulty has proved to be a major drawback for NMR structure determination, particularly for larger proteins.</p>", "links"=>[], "tags"=>["resonance", "assignments", "labeled", "isotopes"], "article_id"=>574845, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conventional_process_of_resonance_assignments_for_a_protein_labeled_with_stable_isotopes_13_C_and_15_N_/574845", "title"=>"Conventional process of resonance assignments for a protein labeled with stable isotopes (¹³C and ¹⁵N).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:20:45"}
• {"files"=>["https://ndownloader.figshare.com/files/904271"], "description"=>"<p>After the data have been collected, the challenging “front-end” process leads to sequence-specific amino acid labeling. The “back-end” process then leads to the three-dimensional structure.</p>", "links"=>[], "tags"=>["stages"], "article_id"=>574734, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conventional_stages_in_protein_structure_determination_by_NMR_/574734", "title"=>"Conventional stages in protein structure determination by NMR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-13 01:18:54"}
• {"files"=>["https://ndownloader.figshare.com/files/904920"], "description"=>"a<p>The maximum number of backbone assignment achievable theoretically on the basis of the peak lists provided as input to PINE, divided by the total number of backbone assignments deposited in BMRB, multiplied by 100.</p>b<p>Number of correct PINE-NMR backbone assignments, divided by the total number of backbone assignments deposited in BMRB, multiplied by 100.</p>c<p>Number of correct PINE-NMR (backbone/side chain) assignments (i.e. in agreement with those in BMRB), divided by the maximum number of (backbone/side chain) assignments achievable theoretically on the basis the peak lists provided as input to PINE, multiplied by 100.</p>d<p>Percentage of residues correctly assigned to helix, strand, or “other” by PINE-NMR on the basis of agreement with DSSP <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000307#pcbi.1000307-Kabsch1\" target=\"_blank\">[50]</a> analysis of the deposited three-dimensional structure of the protein.</p>e<p>Total number of C′, C^α, and C^β atoms detected as possible outliers by LACS method <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000307#pcbi.1000307-Wang1\" target=\"_blank\">[16]</a> in the final assignment.</p>f<p>Defined as (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000307#pcbi.1000307.s003\" target=\"_blank\">Table S1</a>).</p>g<p>Defined as number of noise peaks divided by number of real peaks in HNCACB.</p>h<p>All input included data from an HSQC or HNCO experiment; data from additional experiments were as indicated by shaded boxes: <i>1</i> CBCA(CO)NH or HN(CO)CACB, <i>2</i> HNCACB, <i>3</i> HNCA, <i>4</i> HN(CO)CA, <i>5</i> HN(CA)CO, <i>6</i> H(CCO)NH, <i>7</i> C(CO)NH, <i>8</i> HBHA(CO)NH, <i>9</i> HCCH-TOCSY.</p>i<p>Stereo array isotope labeled (SAIL) protein; data were analyzed without corrections for isotope shifts due to deuterium labeling.</p>", "links"=>[], "tags"=>["pine-nmr", "nmr"], "article_id"=>575379, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Infectious Diseases"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000307.t001", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Backbone_and_side_chain_assignment_performance_of_PINE_NMR_with_NMR_data_from_a_representative_group_of_twelve_proteins_/575379", "title"=>"Backbone and side chain assignment performance of PINE-NMR with NMR data from a representative group of twelve proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-03-13 01:29:39"}
• {"files"=>["https://ndownloader.figshare.com/files/447599", "https://ndownloader.figshare.com/files/447631", "https://ndownloader.figshare.com/files/447688"], "description"=>"<div><p>The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system <em>M</em> with Hamiltonian <em>H</em> to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (<a href=\"http://pine.nmrfam.wisc.edu\">http://pine.nmrfam.wisc.edu</a>). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.</p></div>", "links"=>[], "tags"=>["probabilistic", "algorithm", "labeling", "lists", "nmr", "spectroscopy"], "article_id"=>148210, "categories"=>["Biological Sciences", "Mathematics", "Biophysics", "Biochemistry", "Cancer"], "users"=>["Arash Bahrami", "Amir H. Assadi", "John L. Markley", "Hamid R. Eghbalnia"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000307.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000307.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000307.s003"], "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Probabilistic_Interaction_Network_of_Evidence_Algorithm_and_its_Application_to_Complete_Labeling_of_Peak_Lists_from_Protein_NMR_Spectroscopy/148210", "title"=>"Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-03-13 02:16:50"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime