Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery
Publication Date
April 07, 2015
Journal
PLOS Computational Biology
Authors
Sébastien Giguère, François Laviolette, Mario Marchand, Denise Tremblay, et al
Volume
11
Issue
4
Pages
e1004074
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004074
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004074
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25849257
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388847
Europe PMC
http://europepmc.org/abstract/MED/25849257
Web of Science
000354517600004
Scopus
84929486037
Mendeley
http://www.mendeley.com/research/machine-learning-assisted-design-highly-active-peptides-drug-discovery-4
Events
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2007845"], "description"=>"<div><p>The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at <a href=\"http://graal.ift.ulaval.ca/peptide-design/\" target=\"_blank\">http://graal.ift.ulaval.ca/peptide-design/</a>.</p></div>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369645, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074", "stats"=>{"downloads"=>3, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Machine_Learning_Assisted_Design_of_Highly_Active_Peptides_for_Drug_Discovery_/1369645", "title"=>"Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007841"], "description"=>"<p>Minimal inhibitory concentration (MIC) from <i><b>in vitro</b></i> CAMPs assay. We predicted peptides 1 to 4, peptides 5 to 7 are controls from the training set. The ordering of the peptides do not reflect their predicted bioactivities.</p><p><i>In-vitro</i> minimal inhibitory concentration assay.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369641, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.t002", "stats"=>{"downloads"=>7, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_vitro_minimal_inhibitory_concentration_assay_/1369641", "title"=>"<i>In-vitro</i> minimal inhibitory concentration assay.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007839"], "description"=>"<p>Top motif: the best 1,000 peptides obtained from the oracle. Middle motif: the best 1,000 peptides obtained from <i>h</i><sub><i>random</i></sub>. Bottom motif: the best 1,000 out of 1,000,000 random peptides.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369639, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.g005", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CAMP_bioactivity_motifs_/1369639", "title"=>"CAMP bioactivity motifs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007840"], "description"=>"<p>Bioactivity comparison between the standard combinatorial screening (<i>R</i> random picked peptides) and the proposed approach (<i>K</i> best predicted peptides), initiated with the same <i>R</i> random peptides. Values are logarithm of bactericidal potencies. The correlation coefficients of <i>h</i><sub><i>random</i></sub> were computed using the oracle.</p><p>Results from the drug discovery simulation.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369640, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.t001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_from_the_drug_discovery_simulation_/1369640", "title"=>"Results from the drug discovery simulation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007838"], "description"=>"<p>Correlation coefficient of <i>h</i><sub><i>random</i></sub> predictions on the CAMPs data while varying <i>R</i>, the number of random peptides used as training set.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369638, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_coefficient_of_h_random_predictions_on_the_CAMPs_data_while_varying_R_the_number_of_random_peptides_used_as_training_set_/1369638", "title"=>"Correlation coefficient of <i>h</i><sub><i>random</i></sub> predictions on the CAMPs data while varying <i>R</i>, the number of random peptides used as training set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007835"], "description"=>"<p>Iterative process for the design of peptide ligands.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369635, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.g002", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Iterative_process_for_the_design_of_peptide_ligands_/1369635", "title"=>"Iterative process for the design of peptide ligands.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007834"], "description"=>"<p>In this graph, every source-sink path represent a peptide of size 5 (<i>l</i> = <i>n</i> + <i>k</i> − 1) based on the alphabet {<i>A, B</i>}.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369634, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.g001", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_3_partite_graph_G_h_y_with_k_3_and_a_two_letters_alphabet_/1369634", "title"=>"Illustration of the 3-partite graph <i>G</i><sup><i>h</i><sub><b>y</b></sub></sup> with <i>k</i> = 3 and a two letters alphabet .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 03:58:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2007837"], "description"=>"<p>The 100,000 peptides with highest antimicrobial activity found by the <i>K</i>-longest path algorithm.</p>", "links"=>[], "tags"=>["optimization methods", "data generation", "Extensive analyses", "training data", "iterative combinatorial chemistry procedure", "Active Peptides", "machine Learning Assisted Design", "target protein", "drug discovery", "approach", "graph theory", "Source code", "bioactivity", "kernel methods", "algorithm", "combinatorial problem", "cationic antimicrobial peptides", "model", "laboratory experiments"], "article_id"=>1369637, "categories"=>["Uncategorised"], "users"=>["Sébastien Giguère", "François Laviolette", "Mario Marchand", "Denise Tremblay", "Sylvain Moineau", "Xinxia Liang", "Éric Biron", "Jacques Corbeil"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004074.g003", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_100_000_peptides_with_highest_antimicrobial_activity_found_by_the_K_longest_path_algorithm_/1369637", "title"=>"The 100,000 peptides with highest antimicrobial activity found by the <i>K</i>-longest path algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 03:58:40"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"2", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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