Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.Peer ReviewedPostprint (author's final draft

Misclassification of class C G-protein-coupled receptors as a label noise problem

G-Protein-Coupled Receptors (GPCRs) are cell membrane proteins of relevance to biology and pharmacology. Their supervised classification in subtypes is hampered by label noise, which stems from a combination of expert knowledge limitations and lack of clear correspondence between labels and different representations of the protein primary sequences. In this brief study, we describe a systematic approach to the analysis of GPCR misclassifications using Support Vector Machines and use it to assist the discovery of database labeling quality problems and investigate the extent to which GPCR sequence physicochemical transformations reflect GPCR subtype labeling. The proposed approach could enable a filtering approach to the label noise problem.Peer ReviewedPostprint (published version

Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns

G protein-coupled receptors (GPCRs) are a large and heterogeneous superfamily of receptors that are key cell players for their role as extracellular signal transmitters. Class C GPCRs, in particular, are of great interest in pharmacology. The lack of knowledge about their full 3-D structure prompts the use of their primary amino acid sequences for the construction of robust classifiers, capable of discriminating their different subtypes. In this paper, we investigate the use of feature selection techniques to build Support Vector Machine (SVM)-based classification models from selected receptor subsequences described as n-grams. We show that this approach to classification is useful for finding class C GPCR subtype-specific motifs.Peer ReviewedPostprint (published version

Exploratory visualization of misclassified GPCRs from their transformed unaligned sequences using manifold learning techniques

Class C G-protein-coupled receptors (GPCRs) are cell membrane proteins of great relevance to biology and pharmacology. Previous research has revealed an upper boundary on the accuracy that can be achieved in their classification into subtypes from the unaligned transformation of their sequences. To investigate this, we focus on sequences that have been misclassified using supervised methods. These are visualized, using a nonlinear dimensionality reduction technique and phylogenetic trees, and then characterized against the rest of the data and, particularly, against the rest of cases of their own subtype. This should help 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.Postprint (published version

Using machine learning tools for protein database biocuration assistance

Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration.Peer ReviewedPostprint (published version

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

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 two-stage 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.Peer ReviewedPostprint (author's final draft

Allosteric binding cooperativity in a kinetic context

Allosteric modulators are of prime interest in drug discovery. These drugs regulate the binding and function of endogenous ligands, with some advantages over orthosteric ligands. A typical pharmacological parameter in allosteric modulation is binding cooperativity. This property can yield unexpected but illuminating results when decomposed into its kinetic parameters. Using two reference models (the allosteric ternary complex receptor model and a heterodimer receptor model), a relationship has been derived for the cooperativity rate constant parameters. This relationship allows many combinations of the cooperativity kinetic parameters for a single binding cooperativity value obtained under equilibrium conditions. This assessment may help understand striking experimental results involving allosteric modulation and suggest further investigations in the field" A.G. has been funded by grant PID-2021-122954NB-100 funded by MCIN/AEI/10.13039/501100011033 and by ERDF ‘A way of making Europe’."Peer ReviewedPostprint (published version

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

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Manifold learning visualization of metabotropic glutamate receptors

G-Protein-Coupled Receptors (GPCRs) are cell membrane proteins with a key role in biological processes. GPCRs of class C, in particular, are of great interest in pharmacology. The lack of knowledge about their 3-D structures means they must be investigated through their primary amino acid sequences. Sequence visualization can help to explore the existing receptor sub-groupings at different partition levels. In this paper, we focus on Metabotropic Glutamate Receptors (mGluR), a subtype of class C GPCRs. Different versions of a probabilistic manifold learning model are employed to comparatively sub-group and visualize them through different transformations of their sequences.Peer Reviewe