Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level. / Davies, Matthew N; Gloriam, David E; Secker, Andrew; Freitas, Alex A.; Timmis, Jon; Flower, Darren R.
I: Current Topics in Medicinal Chemistry, Bind 11, Nr. 15, 2011, s. 1994-2009.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level
AU - Davies, Matthew N
AU - Gloriam, David E
AU - Secker, Andrew
AU - Freitas, Alex A.
AU - Timmis, Jon
AU - Flower, Darren R.
N1 - Keywords: GPCR; classification; bioinformatics; alignment; tools
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Former Faculty of Pharmaceutical Sciences
M3 - Journal article
C2 - 21470173
VL - 11
SP - 1994
EP - 2009
JO - Current Topics in Medicinal Chemistry
JF - Current Topics in Medicinal Chemistry
SN - 1568-0266
IS - 15
ER -
ID: 35921799