Present Perspectives on the Automated Classification of the G-Protein Coupled Receptors (GPCRs) at the Protein Sequence Level

Abstract

The G-protein coupled receptors -or GPCRs -comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.3

Chemogenomic Discovery of Allosteric Antagonists at the GPRC6A Receptor

“…The pioneering proof-of-concept for inter-GPCR-family ligand inference provided in this study is fundamental as it clearly has achieved what the traditional classifications or similarity measures, i.e., evolutionary or pharmacological receptor relationships, cannot (Davies et al, 2011;Garland and Gloriam, 2011a). Specifically, the chemogenomic ligand inference covered allosteric ligands and crossed GPCR families.…”

Identification Methods of G Protein-Coupled Receptors

“…Although such approaches are undoubtedly valid, they cannot be optimal for identifying GPCRs. First, the sequence of (Davies et al, 2011). It should also be noted that classification systems of GPCRs based on the conventional biochemistry were designed using ligands to that the receptors are fixed and not the sequence similarity.…”

“…Bioinformatics approaches cited and the most commonly used are: Sequence Similarity Methods and Pattern/Profile Database Search Methods. There is also a thorough study of Davis et al, (2007a) and Davies et al (2011) where they describe the difficulties inherent in developing GPCRs classification algorithm and include techniques already used in the field. Among these techniques, there are bioinformatics tools such as BLAST and FASTA, the motif-based approach and other alignment methods.…”

Chemical Genomics Approach for GPCR–Ligand Interaction Prediction and Extraction of Ligand Binding Determinants

“…1−4 The GPCR superfamily has low sequence similarity in full length comparisons but contains seven highly conserved segments consisting of 25−35 consecutive residues within TM regions. 5−11 To date, hundreds of GPCR sequence motifs have been identified, 10,11 and individual motifs comprise structurally or functionally important sequences including the TM regions and ligand binding pockets. In particular, it is wellknown that the rhodopsin-like GPCR family (class A), the largest in the GPCR superfamily, has ligand binding pockets within the TM regions, 2,4,12 and thus the class A ligands are often classified in terms of the similarity of the TM sequences in the known ligand target's TM region.…”

“…G-protein coupled receptors (GPCRs) belong to the largest group of seven transmembrane (7TM) spanning proteins involved in signal transduction, − and are among the most important target families in drug discovery . GPCRs are known to interact with a variety of ligands as diverse as small molecular-weight ions, biogenic amines, nucleosides and nucleotides, peptide and protein hormones, and lipids and eicosanoids. − The GPCR superfamily has low sequence similarity in full length comparisons but contains seven highly conserved segments consisting of 25–35 consecutive residues within TM regions. − To date, hundreds of GPCR sequence motifs have been identified, , and individual motifs comprise structurally or functionally important sequences including the TM regions and ligand binding pockets. In particular, it is well-known that the rhodopsin-like GPCR family (class A), the largest in the GPCR superfamily, has ligand binding pockets within the TM regions, ,, and thus the class A ligands are often classified in terms of the similarity of the TM sequences in the known ligand target’s TM region. , Information about the ligand binding modes in the TM regions is valuable for drug design and discovery, and therefore, various approaches have been developed for ligand binding site analysis. − …”