{"title":"Rett综合征中意义不确定MECP2变异的综合计算机重分类:性能评估和结构分析。","authors":"Sertaç Atalay, Özlem Yalçın Çapan","doi":"10.1007/s12031-025-02421-9","DOIUrl":null,"url":null,"abstract":"<div><p>Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily caused by missense variants in the <i>MECP2</i> gene. However, the presence of variants of uncertain significance (VUS) poses major challenges for clinical diagnosis and genetic counseling. In this study, we systematically evaluated the performance of 33 in silico prediction tools using a curated ClinVar dataset of <i>MECP2</i> missense variants. Performance metrics included accuracy, sensitivity, specificity, area under the curve (AUC), and Matthews correlation coefficient (MCC), incorporating gene-specific pathogenicity thresholds to enhance predictive precision. Evolutionary conservation was assessed using ConSurf, while structural consequences were examined using UniProt, HOPE, DUET, PyMOL, and RING. Nine top-performing tools—MutPred, MetaRNN, REVEL, MutScore, SNPred, BayesDel, ClinPred, AlphaMissense, and DeepSAV—achieved accuracies exceeding 91% and correctly classified all 19 functionally validated pathogenic variants. These tools consistently predicted 15 VUS as pathogenic, with 14 located within the methyl-CpG-binding domain (MBD) and one within the NCOR2/SMRT interaction region; all occurred at highly conserved residues (ConSurf score: 9). Structural analyses revealed destabilizing effects through altered hydrophobicity, electrostatic charge, and residue interactions, implicating impaired DNA binding or disrupted co-repressor interactions. This integrative framework, combining high-performance computational prediction with structural modeling, offers a robust approach to reclassifying <i>MECP2</i> VUS and supports improved diagnostic accuracy and personalized care in RTT.</p></div>","PeriodicalId":652,"journal":{"name":"Journal of Molecular Neuroscience","volume":"75 4","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive in Silico Reclassification of MECP2 Variants of Uncertain Significance in Rett Syndrome: Performance Evaluation and Structural Analysis\",\"authors\":\"Sertaç Atalay, Özlem Yalçın Çapan\",\"doi\":\"10.1007/s12031-025-02421-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily caused by missense variants in the <i>MECP2</i> gene. However, the presence of variants of uncertain significance (VUS) poses major challenges for clinical diagnosis and genetic counseling. In this study, we systematically evaluated the performance of 33 in silico prediction tools using a curated ClinVar dataset of <i>MECP2</i> missense variants. Performance metrics included accuracy, sensitivity, specificity, area under the curve (AUC), and Matthews correlation coefficient (MCC), incorporating gene-specific pathogenicity thresholds to enhance predictive precision. Evolutionary conservation was assessed using ConSurf, while structural consequences were examined using UniProt, HOPE, DUET, PyMOL, and RING. Nine top-performing tools—MutPred, MetaRNN, REVEL, MutScore, SNPred, BayesDel, ClinPred, AlphaMissense, and DeepSAV—achieved accuracies exceeding 91% and correctly classified all 19 functionally validated pathogenic variants. These tools consistently predicted 15 VUS as pathogenic, with 14 located within the methyl-CpG-binding domain (MBD) and one within the NCOR2/SMRT interaction region; all occurred at highly conserved residues (ConSurf score: 9). Structural analyses revealed destabilizing effects through altered hydrophobicity, electrostatic charge, and residue interactions, implicating impaired DNA binding or disrupted co-repressor interactions. This integrative framework, combining high-performance computational prediction with structural modeling, offers a robust approach to reclassifying <i>MECP2</i> VUS and supports improved diagnostic accuracy and personalized care in RTT.</p></div>\",\"PeriodicalId\":652,\"journal\":{\"name\":\"Journal of Molecular Neuroscience\",\"volume\":\"75 4\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Molecular Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12031-025-02421-9\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s12031-025-02421-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Comprehensive in Silico Reclassification of MECP2 Variants of Uncertain Significance in Rett Syndrome: Performance Evaluation and Structural Analysis
Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily caused by missense variants in the MECP2 gene. However, the presence of variants of uncertain significance (VUS) poses major challenges for clinical diagnosis and genetic counseling. In this study, we systematically evaluated the performance of 33 in silico prediction tools using a curated ClinVar dataset of MECP2 missense variants. Performance metrics included accuracy, sensitivity, specificity, area under the curve (AUC), and Matthews correlation coefficient (MCC), incorporating gene-specific pathogenicity thresholds to enhance predictive precision. Evolutionary conservation was assessed using ConSurf, while structural consequences were examined using UniProt, HOPE, DUET, PyMOL, and RING. Nine top-performing tools—MutPred, MetaRNN, REVEL, MutScore, SNPred, BayesDel, ClinPred, AlphaMissense, and DeepSAV—achieved accuracies exceeding 91% and correctly classified all 19 functionally validated pathogenic variants. These tools consistently predicted 15 VUS as pathogenic, with 14 located within the methyl-CpG-binding domain (MBD) and one within the NCOR2/SMRT interaction region; all occurred at highly conserved residues (ConSurf score: 9). Structural analyses revealed destabilizing effects through altered hydrophobicity, electrostatic charge, and residue interactions, implicating impaired DNA binding or disrupted co-repressor interactions. This integrative framework, combining high-performance computational prediction with structural modeling, offers a robust approach to reclassifying MECP2 VUS and supports improved diagnostic accuracy and personalized care in RTT.
期刊介绍:
The Journal of Molecular Neuroscience is committed to the rapid publication of original findings that increase our understanding of the molecular structure, function, and development of the nervous system. The criteria for acceptance of manuscripts will be scientific excellence, originality, and relevance to the field of molecular neuroscience. Manuscripts with clinical relevance are especially encouraged since the journal seeks to provide a means for accelerating the progression of basic research findings toward clinical utilization. All experiments described in the Journal of Molecular Neuroscience that involve the use of animal or human subjects must have been approved by the appropriate institutional review committee and conform to accepted ethical standards.