ss-TEA : entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs Citation for published version (APA): Sanders, M. P. A., Fleuren, W. W. M., Verhoeven, S., Beld, van den, S., Alkema, W., Vlieg, de, J., & Klomp, J. P. G. (2011). ss-TEA : entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs. BMC Bioinformatics, 12, 332-1/12. https://doi.org/10.1186/1471-2105- 12-332 DOI: 10.1186/1471-2105-12-332 Document status and date: Published: 01/01/2011 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs. Results: Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method. Conclusions: The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/. Background 50% of the drugs currently on the market interact with G-protein coupled receptors(GPCRs),alsoknownas7 a GPCR [1,2]. transmembrane receptors, represent a large superfamily In humans, the family of 7 transmembrane receptors of proteins in the human genome and are responsible for is represented by approximately 900 members which the transduction of an endogenous signal into an intra- can be divided in several classes based upon standard cellular message, which triggers a response in many dif- similarity searches [3-5]. ferent physiological pathways. The structural architecture Recently there has been a reclassification of receptors and chemo-mechanical concept of G-protein coupled according to the GRAFS system which has the following receptors can be seen as an evolutionarily success as wit- groups: glutamate, rhodopsin, adhesion, frizzled/taste2, nessed by the large amount of family members and diver- and secretin [6]. From the structural and functional view- sity of applications in biological processes [1]. point the rhodopsin-like family, also known as the class Not surprisingly, an increasing number of these A receptors, is the largest and best studied family [6]. GPCRs is the subject of investigation as targets in drug Receptors from different families are very diverse discovery. Historical drug discovery approaches have [1,5,7], but can all be characterized by the presence of identified GPCRs as a successful drug target, since 25- seven structurally conserved alpha helices, which span the cell membrane. Most GPCRs couple to a G-protein * Correspondence: [email protected] complex upon ligand binding, resulting in the dissocia- 2 Department of Molecular Design and Informatics, MSD, Molenweg, Oss, The tion of the alpha subunit from the beta and gamma sub- Netherlands Full list of author information is available at the end of the article unit. The final signal depends on the alpha subunit of © 2011 Sanders et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Sanders et al. BMC Bioinformatics 2011, 12:332 Page 2 of 12 http://www.biomedcentral.com/1471-2105/12/332 the G-protein (Gai,Gas,Gaq/11,Ga12/13) which is acti- Oliveira et al. introduced the entropy variability plot and vated and is presumed to be receptor and ligand depen- showed that the location of the aligned residue positions dent [8-12]. The non olfactory Class A receptors in these plots correlate to structural characteristics [33]. recognize a large variety of ligands including photons Based on a similar concept as the entropy variability [13], biogenic amines [14], nucleotides [15], peptides plot Ye et al. introduced the two entropy analysis (TEA) [16], proteins [17] and lipid-like substances [18-21]. in 2006 to identify structural and functional positions in Most ligands are believed to bind fully or partly within the transmembrane region of class A GPCRs [34]. the transmembrane bundle and to trigger signaling Here we present subfamily specific two entropy analysis through a conserved canonical switch [9]. The assump- (ss-TEA), the first method to identify the ligand binding tion that similar molecules bind to similar receptors residues on subfamily level. In contrast to the previously [22] and that small molecules bind within the upper published methods ss-TEA is able to discriminate part of the transmembrane helices, similar to 11-cis ret- between subfamilies and able to identify the approxi- inal in bovine rhodopsin, carazolol in the human beta mately five residues that are involved in ligand binding adrenergic receptor 2, timolol in the turkey beta adre- for each individual subfamily of the class A GPCRs. ss- nergic receptor 1 and ZM-241385 in the human adeno- TEA is predicated on high quality sequence information sine A2 receptor, gives rise to the application of pattern deducedfromamultiplesequencealignment(MSA) recognition analysis on multiple sequence alignments of which was generated by extracting species homologues of those helices or parts thereof to identify ligand binding the class A non olfactory GPCR sequences with a method residues. It has also been shown that for some receptors reported here. This new MSA is characterized by a more which bind large proteins, like the luteinizing hormone complete set of species orthologs which improves the receptor (LHR), low molecular weight (LMW) com- subfamily definition and results of ss-TEA. Receptor spe- pounds can be designed which bind in between the cific sets of ligand binding residues, generated by ss-TEA, TM-bundle and modify signaling [23,24], suggesting improve the understanding of receptor ligand interac- that the same pattern detection techniques could be tions and the design of mutagenesis experiments, and used for those receptors as well. guide the process of homology modelling. Structure based drug design strategies often rely on high resolution information derived from protein crystal struc- Results & Discussion tures. Elucidating GPCR structures at atomic resolution Sequence retrieval & Alignment remains difficult and has only been successful for a small Using a template set of 286 human GPCR sequences, a set of receptors so far (bovine rhodopsin [25], squid rho- BLAST search was performed to retrieve non olfactory dopsin [26], human beta-2-adrenergic receptor [27], tur- class A GPCR sequences. This resulted in 20111 key beta-1-adrenergic receptor [28] and the human A2A sequences originating from 1941 species. An alignment adenosine receptor [29]). These structures have been of the transmembrane helices was obtained by gap free extremely helpful for understanding the function and alignment of all retrieved sequences using HMM models ligand binding properties of class A receptors and are a of the TM domains.