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In Silico Biology 6, 0044 (2006); ©2006, Bioinformation Systems e.V.  



Sequence analysis of the Receptor Activity-Modifying Proteins family, new putative peptides and structural conformation inference

Alfonso Benítez-Páez1, 2

1 Centro de Investigación y Desarrollo en Biotecnología-CIDEB. Bogotá, Colombia
2 Current Address: Laboratorio de Genética Molecular. Centro de Investigación Príncipe Felipe. E-46013 Valencia, Spain

* Corresponding author
   Email: abenitez@cipf.es


Edited by H. Michael; received April 16, 2006; revised August 21, 2006; accepted August 28, 2006; published October 02, 2006


Abstract

The Receptor Activity-Modifying Proteins (RAMP) is a family constituted by a single N-terminal extracellular domain and a transmembrane region ending in a short cytoplasmic region. Due to their specific role in modulating the specificity of ligand binding in many class II G-Protein Coupled Receptors, these proteins are awaiting further characterization and elucidation of their structure. This was the aim of this study. We were able to find 13 new RAMP sequences including new protein sequences and predicted peptides from Expressed Sequence Tags and genomic DNA, all of them annotated in databases such as GeneBank, EMBL, Swissprot and ENSEMBL. The predicted peptides came from an array of different organisms including Teleostei and Elasmobranchii species, of which the latter was the most ancient RAMP sequence found. It was also possible to efficiently predict the 1D structure of the extracellular RAMP domain and its 3D conformation was inferred through a combination of bioinformatic approaches such as threading. The 1D structure of the extracellular RAMP domain was predicted as three α-helix domain. The most highly conserved residues in the RAMP family were found to be involved in critical functions. Bioinformatic data mining and multiple sequence alignment analysis were crucial for improving the characterization of RAMP proteins and prediction of their 1D and 3D configurations.


Keywords: RAMP proteins, structure prediction, sequence alignments