Machine learning assisted design of highly active peptides for drug discovery.
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 eve...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-04-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1004074 |
| _version_ | 1797351496334966784 |
|---|---|
| author | Sébastien Giguère François Laviolette Mario Marchand Denise Tremblay Sylvain Moineau Xinxia Liang Éric Biron Jacques Corbeil |
| author_facet | Sébastien Giguère François Laviolette Mario Marchand Denise Tremblay Sylvain Moineau Xinxia Liang Éric Biron Jacques Corbeil |
| author_sort | Sébastien Giguère |
| collection | DOAJ |
| description | 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/. |
| first_indexed | 2024-03-08T13:01:18Z |
| format | Article |
| id | doaj.art-7c5302b911904fddab24e0081a11b3a3 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T13:01:18Z |
| publishDate | 2015-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-7c5302b911904fddab24e0081a11b3a32024-01-19T05:35:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-04-01114e100407410.1371/journal.pcbi.1004074Machine learning assisted design of highly active peptides for drug discovery.Sébastien GiguèreFrançois LavioletteMario MarchandDenise TremblaySylvain MoineauXinxia LiangÉric BironJacques CorbeilThe 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/.https://doi.org/10.1371/journal.pcbi.1004074 |
| spellingShingle | Sébastien Giguère François Laviolette Mario Marchand Denise Tremblay Sylvain Moineau Xinxia Liang Éric Biron Jacques Corbeil Machine learning assisted design of highly active peptides for drug discovery. PLoS Computational Biology |
| title | Machine learning assisted design of highly active peptides for drug discovery. |
| title_full | Machine learning assisted design of highly active peptides for drug discovery. |
| title_fullStr | Machine learning assisted design of highly active peptides for drug discovery. |
| title_full_unstemmed | Machine learning assisted design of highly active peptides for drug discovery. |
| title_short | Machine learning assisted design of highly active peptides for drug discovery. |
| title_sort | machine learning assisted design of highly active peptides for drug discovery |
| url | https://doi.org/10.1371/journal.pcbi.1004074 |
| work_keys_str_mv | AT sebastiengiguere machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT francoislaviolette machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT mariomarchand machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT denisetremblay machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT sylvainmoineau machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT xinxialiang machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT ericbiron machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery AT jacquescorbeil machinelearningassisteddesignofhighlyactivepeptidesfordrugdiscovery |