Multidimensional Scaling Reveals the Main Evolutionary Pathways of Class A G-Protein-Coupled Receptors

Class A G-protein-coupled receptors (GPCRs) constitute the largest family of transmembrane receptors in the human genome. Understanding the mechanisms which drove the evolution of such a large family would help understand the specificity of each GPCR sub-family with applications to drug design. To gain evolutionary information on class A GPCRs, we explored their sequence space by metric multidimensional scaling analysis (MDS). Three-dimensional mapping of human sequences shows a non-uniform distribution of GPCRs, organized in clusters that lay along four privileged directions. To interpret these directions, we projected supplementary sequences from different species onto the human space used as a reference. With this technique, we can easily monitor the evolutionary drift of several GPCR sub-families from cnidarians to humans. Results support a model of radiative evolution of class A GPCRs from a central node formed by peptide receptors. The privileged directions obtained from the MDS analysis are interpretable in terms of three main evolutionary pathways related to specific sequence determinants. The first pathway was initiated by a deletion in transmembrane helix 2 (TM2) and led to three sub-families by divergent evolution. The second pathway corresponds to the differentiation of the amine receptors. The third pathway corresponds to parallel evolution of several sub-families in relation with a covarion process involving proline residues in TM2 and TM5. As exemplified with GPCRs, the MDS projection technique is an important tool to compare orthologous sequence sets and to help decipher the mutational events that drove the evolution of protein families

ANALYSE EVOLUTIVE DES RECEPTEURS COUPLES AUX PROTEINES G (RCPG)

Class A G-protein-coupled receptors (GPCRs) constitute the largest family of transmembrane receptors in the human genome and are involved in the regulation of many physiological functions. Understanding the mechanisms that drove the evolution of this receptor family should allow a better knowledge of sequence-structure-function relationships of the different sub-families. To gain evolutionary information on GPCRs, we explored their sequence space by metric multidimensional scaling (MDS). We applied a new MDS technique which projects supplementary sequences onto a sequence space of reference and allows comparison of sequences from different species. Results show that receptors cluster in four groups and suggest that modern receptors evolved from ancestors of the peptide receptors along three main evolutionary pathways. Proline residues of transmembrane helices 2 and/or 5 are involved in two of these pathways. To further detail the mechanisms that led to the different sub-families, we analyzed covariations of residues at different hierarchical levels (class/group/sub-family). We tested different methods of covariation analysis in order to select a method robust at the different hierarchical levels. This method highlights sequence determinants that are crucial for the evolution of specific sub-families.Les récepteurs couplés aux protéines G de classe A (RCPG) constituent la plus grande famille de récepteurs transmembranaires du génome humain et sont impliqués dans la régulation de nombreux mécanismes physiologiques. Comprendre les mécanismes évolutifs qui ont conduit à la diversité de cette famille de récepteurs pourrait permettre une meilleure connaissance des relations séquence-structure-fonction des différentes sous-familles. Pour obtenir des informations sur l'évolution des RCPG, nous avons exploré leur espace de séquences par multidimensional scaling métrique (MDS). Nous avons appliqué une nouvelle technique MDS qui projette des séquences supplémentaires sur un espace de référence et permet ainsi la comparaison des séquences de différentes espèces. Les résultats montrent que les récepteurs se répartissent en quatre groupes et suggèrent que les récepteurs actuels ont évolué à partir d'ancêtres des récepteurs de peptides suivant trois directions évolutives principales. Les prolines des hélices transmembranaires 2 et/ou 5 sont impliquées dans deux de ces directions. Pour comprendre le mécanisme fin ayant abouti à la formation des différentes sous-familles, nous avons analysé les covariations des résidus à différents niveaux hiérarchiques (classe/groupe/sous-famille). Nous avons testé différentes méthodes pour analyser les mutations corrélées afin de sélectionner une méthode robuste pour les différents jeux de séquences. L'application de cette méthode met en évidence des résidus spécifiques qui sont cruciaux pour l'évolution de sous-familles particulières

Deep learning for studying drawing behavior: A review

International audienceIn recent years, computer science has made major advances in understanding drawing behavior. Artificial intelligence, and more precisely deep learning, has displayed unprecedented performance in the automatic recognition and classification of large databases of sketches and drawings collected through touchpad devices. Although deep learning can perform these tasks with high accuracy, the way they are performed by the algorithms remains largely unexplored. Improving the interpretability of deep neural networks is a very active research area, with promising recent advances in understanding human cognition. Deep learning thus offers a powerful framework to study drawing behavior and the underlying cognitive processes, particularly in children and non-human animals, on whom knowledge is incomplete. In this literature review, we first explore the history of deep learning as applied to the study of drawing along with the main discoveries in this area, while proposing open challenges. Second, multiple ideas are discussed to understand the inherent structure of deep learning models. A non-exhaustive list of drawing datasets relevant to deep learning approaches is further provided. Finally, the potential benefits of coupling deep learning with comparative cultural analyses are discussed

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

SIMPLE SUMMARY: Understanding drawing features is a complex task, particularly concerning non-human primates, where the relevant features may not be the same as those for humans. Here, we propose a methodology for objectively analyzing drawings. To do so, we used deep learning, which allows for automated feature selection and extraction, to classify a female orangutan’s drawings according to the seasons they were produced. We found evidence of seasonal variation in her drawing behavior according to the extracted features, and our results support previous findings that features linked to colors can partly explain seasonal variation. Using grayscale images, we demonstrate that not only do colors contain relevant information but also the shape of the drawings. In addition, this study demonstrates that both the style and content of drawings partly explain seasonal variations. ABSTRACT: Drawings have been widely used as a window to the mind; as such, they can reveal some aspects of the cognitive and emotional worlds of other animals that can produce them. The study of non-human drawings, however, is limited by human perception, which can bias the methodology and interpretation of the results. Artificial intelligence can circumvent this issue by allowing automated, objective selection of features used to analyze drawings. In this study, we use artificial intelligence to investigate seasonal variations in drawings made by Molly, a female orangutan who produced more than 1299 drawings between 2006 and 2011 at the Tama Zoological Park in Japan. We train the VGG19 model to first classify the drawings according to the season in which they are produced. The results show that deep learning is able to identify subtle but significant seasonal variations in Molly’s drawings, with a classification accuracy of 41.6%. We use VGG19 to investigate the features that influence this seasonal variation. We analyze separate features, both simple and complex, related to color and patterning, and to drawing content and style. Content and style classification show maximum performance for moderately complex, highly complex, and holistic features, respectively. We also show that both color and patterning drive seasonal variation, with the latter being more important than the former. This study demonstrates how deep learning can be used to objectively analyze non-figurative drawings and calls for applications to non-primate species and scribbles made by human toddlers

Comparative analysis of sequence covariation methods to mine evolutionary hubs: Examples from selected GPCR families

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Co-Variation Approaches to the Evolution of Protein Families

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