Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein.
Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating b...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2011-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3186768?pdf=render |
| _version_ | 1818179402577477632 |
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| author | Zsolt Bikadi Istvan Hazai David Malik Katalin Jemnitz Zsuzsa Veres Peter Hari Zhanglin Ni Tip W Loo David M Clarke Eszter Hazai Qingcheng Mao |
| author_facet | Zsolt Bikadi Istvan Hazai David Malik Katalin Jemnitz Zsuzsa Veres Peter Hari Zhanglin Ni Tip W Loo David M Clarke Eszter Hazai Qingcheng Mao |
| author_sort | Zsolt Bikadi |
| collection | DOAJ |
| description | Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening. |
| first_indexed | 2024-12-11T21:03:18Z |
| format | Article |
| id | doaj.art-a19f57097012451dab15ef5acf742c53 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T21:03:18Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a19f57097012451dab15ef5acf742c532022-12-22T00:50:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01610e2581510.1371/journal.pone.0025815Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein.Zsolt BikadiIstvan HazaiDavid MalikKatalin JemnitzZsuzsa VeresPeter HariZhanglin NiTip W LooDavid M ClarkeEszter HazaiQingcheng MaoHuman P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening.http://europepmc.org/articles/PMC3186768?pdf=render |
| spellingShingle | Zsolt Bikadi Istvan Hazai David Malik Katalin Jemnitz Zsuzsa Veres Peter Hari Zhanglin Ni Tip W Loo David M Clarke Eszter Hazai Qingcheng Mao Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. PLoS ONE |
| title | Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. |
| title_full | Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. |
| title_fullStr | Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. |
| title_full_unstemmed | Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. |
| title_short | Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. |
| title_sort | predicting p glycoprotein mediated drug transport based on support vector machine and three dimensional crystal structure of p glycoprotein |
| url | http://europepmc.org/articles/PMC3186768?pdf=render |
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