Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction
TL;DR: This work adds four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate of MetaPocket, and constructs a non-redundant dataset of drug-target complexes with known structure from DrugBank, DrugPort and PDB database to predict drug binding sites.
Abstract: Motivation: Protein–ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITEcs/c, PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result.
Results: Here, we continue our previous work by adding four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate. The new method MetaPocket 2.0 and the individual approaches are all tested on two datasets of 48 unbound/bound and 210 bound structures as used before. The results show that the average success rate has been raised 5% at the top 1 prediction compared with previous work. Moreover, we construct a non-redundant dataset of drug–target complexes with known structure from DrugBank, DrugPort and PDB database and apply MetaPocket 2.0 to this dataset to predict drug binding sites. As a result, >74% drug binding sites on protein target are correctly identified at the top 3 prediction, and it is 12% better than the best individual approach.
Availability: The web service of MetaPocket 2.0 and all the test datasets are freely available at http://projects.biotec.tu-dresden.de/metapocket/ and http://sysbio.zju.edu.cn/metapocket.
Contact: bhuang@biotec.tu-dresden.de
Supplementary Information:Supplementary data are available at Bioinformatics online.
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01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
TL;DR: An overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research is given.
Abstract: Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.
400 citations
TL;DR: P2Rank is a new open source software package for ligand binding site prediction from protein structure that belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.
Abstract: 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. 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. 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.
182 citations
TL;DR: The FTSite algorithm does not rely on any evolutionary or statistical information, but achieves near experimental accuracy: it is capable of identifying the binding sites in over 94% of apo proteins from established test sets that have been used to evaluate many other binding site prediction methods.
Abstract: Motivation: Binding site identification is a classical problem that is important for a range of applications, including the structure-based prediction of function, the elucidation of functional relationships among proteins, protein engineering and drug design. We describe an accurate method of binding site identification, namely FTSite. This method is based on experimental evidence that ligand binding sites also bind small organic molecules of various shapes and polarity. The FTSite algorithm does not rely on any evolutionary or statistical information, but achieves near experimental accuracy: it is capable of identifying the binding sites in over 94% of apo proteins from established test sets that have been used to evaluate many other binding site prediction methods. Availability: FTSite is freely available as a web-based server at
178 citations
TL;DR: This study enhanced its depth-based predictions of binding sites by including evolutionary information, and demonstrated that on a database of ∼200 proteins, the method performs on par with ConCavity and better than MetaPocket 2.0.
Abstract: Residue depth accurately measures burial and parameterizes local protein environment. Depth is the distance of any atom/residue to the closest bulk water. We consider the non-bulk waters to occupy cavities, whose volumes are determined using a Voronoi procedure. Our estimation of cavity sizes is statistically superior to estimates made by CASTp and VOIDOO, and on par with McVol over a data set of 40 cavities. Our calculated cavity volumes correlated best with the experimentally determined destabilization of 34 mutants from five proteins. Some of the cavities identified are capable of binding small molecule ligands. In this study, we have enhanced our depth-based predictions of binding sites by including evolutionary information. We have demonstrated that on a database (LigASite) of ∼200 proteins, we perform on par with ConCavity and better than MetaPocket 2.0. Our predictions, while less sensitive, are more specific and precise. Finally, we use depth (and other features) to predict pKas of GLU, ASP, LYS and HIS residues. Our results produce an average error of just <1 pH unit over 60 predictions. Our simple empirical method is statistically on par with two and superior to three other methods while inferior to only one. The DEPTH server (http://mspc.bii.a-star.edu.sg/depth/) is an ideal tool for rapid yet accurate structural analyses of protein structures.
169 citations
References
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TL;DR: Cd-hit-2d compares two protein datasets and reports similar matches between them; cd- Hit-est clusters a DNA/RNA sequence database and cd- hit-est-2D compares two nucleotide datasets.
Abstract: Motivation: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282--283, Bioinformatics, 18, 77--82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.
Availability: http://cd-hit.org
Contact: [email protected]
8,306 citations
"Identification of cavities on prote..." refers methods in this paper
...In the next step, we used CD-HIT (Huang et al., 2010; Li and Godzik, 2006) program to remove the redundancy of protein targets using 40% similarity threshold....
[...]
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
TL;DR: DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug data with comprehensive drug target information and is fully searchable supporting extensive text, sequence, chemical structure and relational query searches.
Abstract: DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains .4100 drug entries including .800 FDA approved small molecule and biotech drugs as well as .3200 experimental drugs. Additionally, .14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains .80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at http:// redpoll.pharmacy.ualberta.ca/drugbank/.
3,087 citations
TL;DR: The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release and contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs.
Abstract: DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.
2,380 citations
[...]
TL;DR: A new web server, CD-HIT Suite, is developed for clustering a user-uploaded sequence dataset or comparing it to another dataset at different identity levels and users can now interactively explore the clusters within web browsers.
Abstract: Summary: CD-HIT is a widely used program for clustering and comparing large biological sequence datasets. In order to further assist the CD-HIT users, we significantly improved this program with more functions and better accuracy, scalability and flexibility. Most importantly, we developed a new web server, CD-HIT Suite, for clustering a user-uploaded sequence dataset or comparing it to another dataset at different identity levels. Users can now interactively explore the clusters within web browsers. We also provide downloadable clusters for several public databases (NCBI NR, Swissprot and PDB) at different identity levels.
Availability: Free access at http://cd-hit.org
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
2,084 citations