A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences

Alfredo Vellido; Martha Ivón Cárdenas; Iván Olier; Xavier Rovira; Jesús Giraldo

A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences

Alfredo Vellido, Martha Ivón Cárdenas, Iván Olier, Xavier Rovira, Jesús Giraldo

Department of Paediatrics, Obstetrics and Gynaecology and Preventive Medicine and Public Health

Research output: Contribution to journal › Article › Research › peer-review

Abstract

The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use a kernel-based statistical machine learning model for the visual exploration of GPCR functional groups from their sequences. This is based on the rich information provided by the model regarding the probability of each sequence belonging to a certain receptor group.

Original language	English
Pages (from-to)	233-238
Number of pages	6
Journal	ESANN 2011 - 19th European Symposium on Artificial Neural Networks
Publication status	Published - 2011

Cite this

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abstract = "The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use a kernel-based statistical machine learning model for the visual exploration of GPCR functional groups from their sequences. This is based on the rich information provided by the model regarding the probability of each sequence belonging to a certain receptor group.",

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AU - Giraldo, Jesús

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AB - The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use a kernel-based statistical machine learning model for the visual exploration of GPCR functional groups from their sequences. This is based on the rich information provided by the model regarding the probability of each sequence belonging to a certain receptor group.

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A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences

Abstract

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