title: Machine learning assisted design of highly active peptides for drug discovery.
abstract: 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 http://graal.ift.ulaval.ca/peptide-design/.
authors: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
authors: Sébastien
authors: S
authors: Giguère
authors: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
authors: François
authors: F
authors: Laviolette
authors: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
authors: Mario
authors: M
authors: Marchand
authors: Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, Canada.
authors: Denise
authors: D
authors: Tremblay
authors: Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, Canada.
authors: Sylvain
authors: S
authors: Moineau
authors: Faculty of Pharmacy, Université Laval, Québec, Canada.
authors: Xinxia
authors: X
authors: Liang
authors: Faculty of Pharmacy, Université Laval, Québec, Canada.
authors: Éric
authors: É
authors: Biron
authors: Department of Molecular Medicine, Université Laval, Québec, Canada.
authors: Jacques
authors: J
authors: Corbeil
mesh_terms: D000595:Amino Acid Sequence; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000493:pharmacokinetics*; D018407:Bacterial Physiological Phenomena / Q000187:drug effects*; D055808:Drug Discovery / Q000379:methods*; D000069550:Machine Learning; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides; D025941:Protein Interaction Mapping / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D023181:Antimicrobial Cationic Peptides; D010455:Peptides