P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Abstract Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been publi...
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| Format: | Article |
| Language: | English |
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BMC
2018-08-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s13321-018-0285-8 |
| _version_ | 1818249224275361792 |
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| author | Radoslav Krivák David Hoksza |
| author_facet | Radoslav Krivák David Hoksza |
| author_sort | Radoslav Krivák |
| collection | DOAJ |
| description | Abstract Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines. |
| first_indexed | 2024-12-12T15:33:05Z |
| format | Article |
| id | doaj.art-34715cd7cb01436e9a681b511d5677a9 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-12-12T15:33:05Z |
| publishDate | 2018-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-34715cd7cb01436e9a681b511d5677a92022-12-22T00:20:05ZengBMCJournal of Cheminformatics1758-29462018-08-0110111210.1186/s13321-018-0285-8P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structureRadoslav Krivák0David Hoksza1Department of Software Engineering, Charles UniversityDepartment of Software Engineering, Charles UniversityAbstract Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.http://link.springer.com/article/10.1186/s13321-018-0285-8Ligand binding sitesProtein pocketsBinding site predictionProtein surface descriptorsMachine learningRandom forests |
| spellingShingle | Radoslav Krivák David Hoksza P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure Journal of Cheminformatics Ligand binding sites Protein pockets Binding site prediction Protein surface descriptors Machine learning Random forests |
| title | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| title_full | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| title_fullStr | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| title_full_unstemmed | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| title_short | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| title_sort | p2rank machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
| topic | Ligand binding sites Protein pockets Binding site prediction Protein surface descriptors Machine learning Random forests |
| url | http://link.springer.com/article/10.1186/s13321-018-0285-8 |
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