The clinical spectrum of -related neurodevelopmental disorders (GRD) results from gene- and variant-dependent primary alterations of the NMDA receptor, disturbing glutamatergic neurotransmission. Despite gene variants' functional annotations being dually critical for stratification and precision medicine design, genetically diagnosed pathogenic variants currently outnumber their relative functional annotations. Based on high-resolution crystal 3D models and topological domains conservation between GluN1, GluN2A, and GluN2B subunits of the NMDAR, we have generated GluN1-GluN2A-GluN2B subunits structural superimposition model to find equivalent positions between GluN subunits. We have developed a structural algorithm that predicts functional changes in the equivalent structural positions in other GluN subunits. GRIN structural algorithm was computationally evaluated to the full missense variants repertoire, consisting of 4,525 variants. The analysis of this structure-based model revealed an absolute predictive power for GluN1, GluN2A, and GluN2B subunits, both in terms of pathogenicity-association (benign vs. pathogenic variants) and functional impact (loss-of-function, benign, gain-of-function). Further, we validated this computational algorithm experimentally, using an library of GluN2B-equivalent GluN2A artificial variants, designed from pathogenic GluN2B variants. Thus, the implementation of the GRIN structural algorithm allows to computationally predict the pathogenicity and functional annotations of variants, resulting in the duplication of pathogenic variants assignment, reduction by 30% of variants with uncertain significance, and increase by 70% of functionally annotated variants. Finally, GRIN structural algorithm has been implemented into variants Database (http://lmc.uab.es/grindb), providing a computational tool that accelerates missense variants stratification, contributing to clinical therapeutic decisions for this neurodevelopmental disorder.
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http://dx.doi.org/10.3389/fncel.2022.998719 | DOI Listing |
JMIR Form Res
January 2025
Department of Public Health, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan, 81 562-93-2476, 81 562-93-3079.
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Due to the rapid development of urbanisation, cities frequently experience waterlogging during rainfall. Rain gardens are widely used in new urban construction because they effectively control surface runoff from rainwater, thereby reducing waterlogging. The runoff control effectiveness of rain gardens is influenced by multiple factors.
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Faculty of Fine Arts, Design and Architecture Department of Landscape Architecture, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
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January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
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Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
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