Calpain Cleavage Prediction Using Multiple Kernel Learning
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{"title"=>"Calpain cleavage prediction using multiple kernel learning", "type"=>"journal", "authors"=>[{"first_name"=>"David A.", "last_name"=>"duVerle", "scopus_author_id"=>"36172369000"}, {"first_name"=>"Yasuko", "last_name"=>"Ono", "scopus_author_id"=>"7402959986"}, {"first_name"=>"Hiroyuki", "last_name"=>"Sorimachi", "scopus_author_id"=>"7006347216"}, {"first_name"=>"Hiroshi", "last_name"=>"Mamitsuka", "scopus_author_id"=>"6602748450"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"21559271", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0019035", "pui"=>"361732189", "scopus"=>"2-s2.0-79955780941", "sgr"=>"79955780941"}, "id"=>"bc0704f9-3f25-3f05-b015-5c9921db9902", "abstract"=>"Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.", "link"=>"http://www.mendeley.com/research/calpain-cleavage-prediction-using-multiple-kernel-learning", "reader_count"=>31, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>9, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>9, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Agricultural and Biological Sciences"=>21, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>21}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "Ireland"=>1, "Chile"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/777655"], "description"=>"<p>“Conventional” calpains (- and m-calpain) are composed of larger catalytic subunits (calpain-1 and -2) and a smaller regulatory subunit. Some homologues, such as skeletal muscle-specific calpain (calpain-3/p94) have slightly diverged properties, including unique insertion sequences (NS, IS1 and IS2) and no requirement for a small subunit. Symbols used are: <b>I</b>: N-terminal domain with little homology; <b>IIa</b> and <b>IIb</b>: protease sub-domains containing the active sites Cys and His/Asn, respectively; <b>III</b>: C2-like -binding domain; <b>IV</b> and <b>VI</b>: 5-EF-hand -binding domain; <b>V</b>: Gly-rich hydrophobic domain; <b>NS</b>, <b>IS1</b> and <b>IS2</b>: p94-specific sequences.</p>", "links"=>[], "tags"=>["structures", "calpain"], "article_id"=>448027, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_structures_of_major_calpain_homologues_/448027", "title"=>"Schematic structures of major calpain homologues.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:39:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/778311"], "description"=>"<p>Values from each calpain gene types do not add up to the figures for ‘All Types’, due to some substrates being cleaved by more than one type, while other sequences are missing calpain type labeling.</p>", "links"=>[], "tags"=>["labeling", "calpain"], "article_id"=>448681, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.t005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Substrate_labeling_by_calpain_sub_type_/448681", "title"=>"Substrate labeling by calpain sub-type.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 18:42:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/778181"], "description"=>"<p>*: using same encoding and window length as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019035#pone.0019035-Barkan1\" target=\"_blank\">[38]</a>.</p><p>**: using same encoding as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019035#pone.0019035-Barkan1\" target=\"_blank\">[38]</a> but with optimal window parameters obtained through grid search.</p><p>Results are shown as: % AUC (% SEM).</p><p><b>Position</b>: Residue position information, with a Gaussian RBF kernel ( = 2.1,  = 1.67) and canonical binary encoding. <b>SS</b>: Secondary structure, with a Spectrum kernel (<i>k</i> between 2 and 5, allowing up to 1 gap).</p>", "links"=>[], "tags"=>["gaussian", "kernel"], "article_id"=>448552, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.t002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AUC_Results_with_single_Gaussian_kernel_methods_/448552", "title"=>"AUC Results with single Gaussian kernel methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 18:41:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/777839"], "description"=>"<p>Left column uses sequence only, while right column uses secondary structure information (SS) as well.</p>", "links"=>[], "tags"=>["linear-kernel", "cleavage"], "article_id"=>448213, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AUC_with_a_linear_kernel_SVM_as_function_of_cleavage_extension_length_left_and_right_side_of_cleavage_site_in_number_of_nucleotides_/448213", "title"=>"AUC (with a linear-kernel SVM) as function of cleavage extension length (left and right side of cleavage site) in number of nucleotides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:40:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/778121"], "description"=>"<p>Normalized MKL prediction scores using Position, String and SS feature sets. <b>A</b>: on wild type <i>Rattus norvegicus</i> calpastatin (gi 13540322). <b>B</b>: on a mutant of calpastatin, obtained by deletion of Lys176 and Glu177 (highlighted in red in sequence <b>A</b> and marked by a red star in sequence <b>B</b>). Results were cropped to residues [101–200] in the sequence. Thin blue line marks 5% top scores threshold. Thick green lines highlight “loop-out” area of calpastatin sequences (shortened in the mutant by deletion of Lys176 and Glu177) where cleavage would likely occur.</p>", "links"=>[], "tags"=>["calpastatin"], "article_id"=>448495, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cleavage_prediction_on_Calpastatin_sequences_/448495", "title"=>"Cleavage prediction on Calpastatin sequences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:41:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/777958"], "description"=>"<p>Domain II is the protease domain of calpain, while domain III binds . Amino acid sequences of domain III are less conserved than those of domain II, which are highly conserved not only between - and m-calpains but also among all calpain family members.</p>", "links"=>[], "tags"=>["calpain", "substrate"], "article_id"=>448328, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_contact_region_between_calpain_and_substrate_sequence_/448328", "title"=>"Schematic representation of contact region between calpain and substrate sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:40:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/778233"], "description"=>"<p>Tested range and optimal values for SVM kernel function parameters. Integer values were tested for the entire range. Non-integer parameters were set using values within their ranges in two successive grid search of decreasing step value.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry"], "article_id"=>448604, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.t001", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SVM_Parameters_/448604", "title"=>"SVM Parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 18:42:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/777721"], "description"=>"<p>AUC score as function of symetrical extension length (number of nucleotides) on each side of putative cleavage site. <b>A</b>: using only position information. <b>B</b>: using position and secondary structure (SS) information.</p>", "links"=>[], "tags"=>["svm", "trained", "substrates", "calpain-1", "calpain-2"], "article_id"=>448092, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_kernel_SVM_performance_trained_on_full_set_of_substrates_All_vs_calpain_1_Cal_1_and_calpain_2_Cal_2_/448092", "title"=>"Linear-kernel SVM performance trained on full set of substrates (All) vs. calpain-1 (Cal 1) and calpain-2 (Cal 2).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:39:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/778257"], "description"=>"<p>Optimal training weights obtained for each combination of kernels (on full calpain set) using MKL training algorithm described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019035#pone.0019035-Sonnenburg1\" target=\"_blank\">[39]</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Biochemistry"], "article_id"=>448630, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.t006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MKL_weights_/448630", "title"=>"MKL weights.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 18:42:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/778286"], "description"=>"<p>P-values for pairwise T-test comparisons between results from different combination of kernels, using sets of 10×10 AUC results, under assumption of equal variance.</p>", "links"=>[], "tags"=>["t-test"], "article_id"=>448659, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.t004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pairwise_T_test_Comparison_/448659", "title"=>"Pairwise T-test Comparison.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 18:42:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/778031"], "description"=>"<p>AUC values produced by MKL prediction method, when varying extension length for one feature set at a time (all other parameters at their optimal value). See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019035#pone-0019035-t002\" target=\"_blank\">table 2</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019035#pone-0019035-t003\" target=\"_blank\">3</a> for notations.</p>", "links"=>[], "tags"=>["cleavage"], "article_id"=>448415, "categories"=>["Biological Sciences", "Biochemistry"], "users"=>["David A. duVerle", "Yasuko Ono", "Hiroyuki Sorimachi", "Hiroshi Mamitsuka"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019035.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AUC_as_function_of_cleavage_extension_length_/448415", "title"=>"AUC as function of cleavage extension length.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 18:41:11"}

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