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Machine learning assisted design of highly active peptides for drug discovery.

Giguère S, Laviolette F, Marchand M, Tremblay D, Moineau S, Liang X, Biron É, Corbeil J - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.

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/.

No MeSH data available.


The 100,000 peptides with highest antimicrobial activity found by the K-longest path algorithm.
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pcbi.1004074.g003: The 100,000 peptides with highest antimicrobial activity found by the K-longest path algorithm.

Mentions: For the CAMPs dataset, the proposed approach predicted that peptide WWKWWKRLRRLFLLV should have an antibacterial potency of 1.09, a logarithmic improvement of 0.266 over the best peptide in the training set (GWRLIKKILRVFKGL, 0.824), and a substantial improvement over the average potency of that dataset (average of 0.39). The antimicrobial activity of the top 100,000 peptides are showed in Fig. 3. We observe a smooth power law with only a few peptides having outstanding biological activity, as expected. As we will see in the next section, peptides at the top of the curve, hence having the best bioactivities, are very unlikely to be found by chance.


Machine learning assisted design of highly active peptides for drug discovery.

Giguère S, Laviolette F, Marchand M, Tremblay D, Moineau S, Liang X, Biron É, Corbeil J - PLoS Comput. Biol. (2015)

The 100,000 peptides with highest antimicrobial activity found by the K-longest path algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388847&req=5

pcbi.1004074.g003: The 100,000 peptides with highest antimicrobial activity found by the K-longest path algorithm.
Mentions: For the CAMPs dataset, the proposed approach predicted that peptide WWKWWKRLRRLFLLV should have an antibacterial potency of 1.09, a logarithmic improvement of 0.266 over the best peptide in the training set (GWRLIKKILRVFKGL, 0.824), and a substantial improvement over the average potency of that dataset (average of 0.39). The antimicrobial activity of the top 100,000 peptides are showed in Fig. 3. We observe a smooth power law with only a few peptides having outstanding biological activity, as expected. As we will see in the next section, peptides at the top of the curve, hence having the best bioactivities, are very unlikely to be found by chance.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.

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/.

No MeSH data available.