Classification of G-protein coupled receptors at four levels

Gao, Qing-Bin; Wang, Zhengzhi

doi:10.1093/protein/gzl038

Cited by 74 publications

(58 citation statements)

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“…During the last 7 years or so, a number of methods were proposed in this regard [154][155][156][157][158][159]. Some of them were developed for identifying the main functional classes of GPCRs (see, e.g., [157]) and some for the sub-functional classes (see, e.g., [155]).…”

Section: Gpcr-camentioning

confidence: 99%

REVIEW : Recent advances in developing web-servers for predicting protein attributes

Chou¹,

Shen²

2009

277

173

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Recent advance in large-scale genome sequencing has generated a huge volume of protein sequences. In order to timely utilize the information hidden in these newly discovered sequences, it is highly desired to develop computational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a systematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

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Section: Gpcr-camentioning

confidence: 99%

REVIEW : Recent advances in developing web-servers for predicting protein attributes

Chou¹,

Shen²

2009

277

173

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“…Investigates its reason, the FD feature plays a crucial role in predicting GPCRs. According to reports, amino acid composition (AAC) are simplest but effective features in predicting GPCRs [11], however, only AAC features are insufficient with a lake of sequence order information. To compensate for this deficiency, CGR-based method is proposed in this research.…”

Section: Further Discussionmentioning

confidence: 99%

“…Among them, in 2004, a web server called "GPCRpred" was developed for prediction of families and subfamilies of G-protein coupled receptors using support vector machine (SVM) [10]. Gao and Wang [11] introduced a nearest neighbor method to discriminate GPCRs from non-GPCRs and subsequently classify GPCRs at four levels on the basis of amino acid composition (AAC) and dipeptide composition of proteins in 2006. In 2008, Xiao, Wang and Chou [12] employed "cellular automaton" to reveal the pattern features hidden in piles of long and complicated protein sequences and predicted GPCRs and subfamilies with CD classifier.…”

Section: Introductionmentioning

confidence: 99%

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Nie

Wang

et al. 2015

BME

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Abstract. G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

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“…Class A GPCRs share the same arrangement of the seven helices [59] and their 7TMD sequence similarities are relatively high [60][61][62][63]. However, even small sequence differences can lead to significant differences in overall structure and particularly in the topology of the ligand binding site [64].…”

Section: Introductionmentioning

confidence: 99%

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

Àlex¹,

Heifetz²,

Mazanetz³

et al. 2013

Med chem

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G-Protein Coupled Receptors (GPCRs) have enormous physiological and biomedical importance, being the primary target of a large number of modern drugs. The availability of structural information of the binding site of the targeted GPCR plays a key role in rationalization, efficiency and cost-effectiveness of the drug discovery process. However, obtaining structural information on GPCRs using X-ray crystallography or NMR requires a large investment of time and is technically very challenging. This situation significantly limits the ability of these methods to have an impact in drug discovery for GPCR targets in the short term and hence there is an urgent need for other effective and cost-efficient alternatives. We present here a practical approach that integrates GPCR modelling with fragment based screening to provide structural insights on the H3 and H4 histamine receptor binding sites. This approach creates a cost-efficient new avenue for structure-based drug design (SBDD) against GPCR targets. We report here a success of using this protocol for the discovery of selective and dual H3 and H4 antagonists. Our fragment screen yielded 44 H3, 21 H4 selective and 20 dual fragment hits. These fragments were used to construct high-quality H3 and H4 models followed by binding site exploration and structure based virtual screening (VS). Overall, 172 compounds were purchased for testing based on the virtual screening results. Of the 74 compounds predicted to have dual activity, 33 had activity against one or other of the two receptors (44%), of which 17 had activity against both. Of the 19 compounds predicted to be H3 selective, 13 were active against H3 (68%) and 10 of these also had selectivity over H4. Of the 79 compounds predicted to be H4 selective, 36 were active against H4 (45%) and 2 of these also had selectivity over H3.

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Classification of G-protein coupled receptors at four levels

Cited by 74 publications

References 1 publication

REVIEW : Recent advances in developing web-servers for predicting protein attributes

REVIEW : Recent advances in developing web-servers for predicting protein attributes

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

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