A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL

Abstract

BackgroundThe process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel ‘targets’, usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases.DescriptionWe have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the ‘ChEMBL protein target slim’ allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms.We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The ‘ChEMBL protein target slim’ provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL.The ‘ChEMBL protein target slim’ is available as a browsable ‘Gene Ontology Tree’ on the ChEMBL site under the browse targets tab (https://www.ebi.ac.uk/chembl/target/browser). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website (http://geneontology.org/page/go-slim-and-subset-guide).ConclusionsWe have created a protein target navigation tool based on the ‘ChEMBL protein target slim’. The ‘ChEMBL protein target slim’ provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug target...

The ChEMBL database in 2017

“…Improvements have been made to the existing protein family classification for ion channels and transporters to more closely align the ChEMBL system with other resources (the IUPHAR/BPS Guide To PHARMACOLOGY (28) and TCDB (29)) and new classes have been introduced for epigenetic regulators (following the ChromoHub database (30)). In order to allow browsing of ChEMBL targets by Gene Ontology terms, a new GO Slim has been created (31). This is a subset of GO terms that are enriched in ChEMBL targets and allows the implementation of a simplified Gene Ontology Tree for browsing (on the ‘Browse Targets’ tab).…”

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

“…However, only [33] and [39] reported auPR scores in their paper. A comparison in terms of auPR score are given in Dataset Enzyme GPCR ion channels nuclear receptor [51] 0.904 0.8510 0.8990 0.8430 [52] 0.8920 0.8120 0.8270 0.8350 [10] 0.8075 0.8029 0.8022 0.7578 [18] 0.8320 0.7990 0.8570 0.8240 [7] 0.8251 .8034 0.8235 0.8394 [47] 0.8860 0.8930 0.8730 0.8240 [35] 0.9480 0.8990 0.8720 0.8690 [39] 0.9689 0.9369 0.9222 0.9285 Our Method 0.9754 0.9478 0.9512 0.9241 Table 6: Comparison of the performance of FRnet-2 on the four benchmark gold datasets from [39] in terms of auPR with other the state-of-the-art methods. Table 6.…”

Drug target ontology to classify and integrate drug discovery data

“…Several drug target-related resources have been developed, such as the ChEMBL Drug Target Slim [ 40 ], where GO annotations are available for drug targets in ChEMBL. Protein Ontology recently enhanced the protein annotation with pathway information and phosphorylation sites information [ 41 ].…”