Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences

Cárdenas, Martha Ivón; Vellido, Alfredo; Giraldo, Jesús

doi:10.1109/bhi.2014.6864476

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…This GPCR discrimination task has been addressed in the past using supervised [6], semi-supervised [7] and even fully unsupervised [8], [9] modelling approaches.…”

Section: Introductionmentioning

confidence: 99%

The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences

König

Alquézar

Vellido

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The investigation of protein functionality often relies on the knowledge of crystal 3-D structure. This structure is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as G Protein-Coupled Receptors (GPCRs) and specially of those of class C, which are the target of the current study. In the absence of information about tertiary or quaternary structures, functionality can be investigated from the primary structure, that is, from the amino acid sequence. In previous research, we found that the different subtypes of class C GPCRs could be discriminated with a high level of accuracy from the n-gram transformation of their complete primary sequences, using a method that combined twostage feature selection with kernel classifiers. This study aims at discovering whether subunits of the complete sequence retain such discrimination capabilities. We report experiments that show that the extracellular N-terminal domain of the receptor suffices to retain the classification accuracy of the complete sequence and that it does so using a reduced selection of n-grams whose length of up to five amino acids opens up an avenue for class C GPCR signature motif discovery.

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“…This GPCR discrimination task has been addressed in the past using supervised [6], semi-supervised [7] and even fully unsupervised [8], [9] modelling approaches.…”

Section: Introductionmentioning

confidence: 99%