The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

2017

BioMed Eng OnLine

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BackgroundBiology is experiencing a gradual but fast transformation from a laboratory-centred science towards a data-centred one. As such, it requires robust data engineering and the use of quantitative data analysis methods as part of database curation. This paper focuses on G protein-coupled receptors, a large and heterogeneous super-family of cell membrane proteins of interest to biology in general. One of its families, Class C, is of particular interest to pharmacology and drug design. This family is quite heterogeneous on its own, and the discrimination of its several sub-families is a challenging problem. In the absence of known crystal structure, such discrimination must rely on their primary amino acid sequences.MethodsWe are interested not as much in achieving maximum sub-family discrimination accuracy using quantitative methods, but in exploring sequence misclassification behavior. Specifically, we are interested in isolating those sequences showing consistent misclassification, that is, sequences that are very often misclassified and almost always to the same wrong sub-family. Random forests are used for this analysis due to their ensemble nature, which makes them naturally suited to gauge the consistency of misclassification. This consistency is here defined through the voting scheme of their base tree classifiers.ResultsDetailed consistency results for the random forest ensemble classification were obtained for all receptors and for all data transformations of their unaligned primary sequences. Shortlists of the most consistently misclassified receptors for each subfamily and transformation, as well as an overall shortlist including those cases that were consistently misclassified across transformations, were obtained. The latter should be referred to experts for further investigation as a data curation task.ConclusionThe automatic discrimination of the Class C sub-families of G protein-coupled receptors from their unaligned primary sequences shows clear limits. This study has investigated in some detail the consistency of their misclassification using random forest ensemble classifiers. Different sub-families have been shown to display very different discrimination consistency behaviors. The individual identification of consistently misclassified sequences should provide a tool for quality control to GPCR database curators.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0357-4) contains supplementary material, which is available to authorized users.

Section: Introductionmentioning

confidence: 99%

Using random forests for assistance in the curation of G-protein coupled receptor databases

Shkurin

2017

BioMed Eng OnLine

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“…Recent analysis using semisupervised and supervised classification of class C GPCRs [8,9] with this type of transformation showed that overall accuracy (the ratio of correctly classified sequences) reaches an upper bound in the area of 90% that it is not significantly increased when more sophisticated physico-chemical transformations of the sequences are applied.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the same sequence dataset is visualized using a nonlinear dimensionality reduction (NLDR) technique, namely Generative Topographic Mapping (GTM [12]). This technique has been applied with success to many problems in biomedicine and bioinformatics [8,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods

Cárdenas

König

et al. 2015

Genomics Comput Biol

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G-protein-coupled receptors are cell membrane proteins of great interest in biology and pharmacology. Previous analysis of Class C of these receptors has revealed the existence of an upper boundary on the accuracy that can be achieved in the classification of their standard subtypes from the unaligned transformation of their primary sequences. To further investigate this apparent boundary, the focus of the analysis in this paper is placed on receptor sequences that were previously misclassified using supervised learning methods. In our experiments, these sequences are visualized using a nonlinear dimensionality reduction technique and phylogenetic trees. They are subsequently characterized against the rest of the data and, particularly, against the rest of cases of their own subtype. This exploratory visualization should help us to discriminate between different types of misclassification and to build hypotheses about database quality problems and the extent to which GPCR sequence transformations limit subtype discriminability. The reported experiments provide a proof of concept for the proposed method.

“…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

2015 International Joint Conference on Neural Networks (IJCNN)

et al. 2015

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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.