Giulio Poli, Salvatore Galati, Marco Macchia, Tiziano Tuccinardi
{"title":"ConPath 2.0:一种评估hRPE65错义变异潜在致病性的优化共识策略","authors":"Giulio Poli, Salvatore Galati, Marco Macchia, Tiziano Tuccinardi","doi":"10.1007/s00894-025-06481-x","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p><i>h</i>RPE65 is an essential enzyme in the retinoid visual cycle. Numerous missense variants of <i>h</i>RPE65 have been linked to retinal disorders, such as retinitis pigmentosa and Leber congenital amaurosis. Moreover, many <i>h</i>RPE65 missense mutations are currently classified as variants of uncertain significance (VUS) due to insufficient evidence for a definitive pathogenicity classification. Addressing this limitation is critical for enabling accurate diagnoses and identifying suitable candidates for gene therapy. For this reason, we developed a <i>h</i>RPE65-tailored computational strategy, based on a consensus of multiple in silico pathogenicity predictions, enabling a rapid and reliable evaluation of the potential pathogenic effect of over 200 <i>h</i>RPE65 VUS. The analysis provided valuable insights to support the reclassification of these variants and assist clinicians in assessing their suitability for gene therapy.</p><h3>Methods</h3><p>In this study, we optimized our recently developed ConPath approach, which combines variant pathogenicity predictions from 19 different computational tools. In particular, we expanded the pool of predictive tools combined in the approach to 27, incorporated 3D-based methods that employ structural information for their prediction, and we refined the statistical framework for selecting the most reliable tools within an extended pool of more than 70 different methods. The tools were assessed based on their ability to discriminate pathogenic from benign <i>h</i>RPE65 missense mutations using an updated and expanded dataset of known <i>h</i>RPE65 variants. The resulting enhanced strategy, ConPath 2.0, was applied to the 210 <i>h</i>RPE65 VUS reported in the ClinVar database.</p></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 9","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ConPath 2.0: an optimized consensus strategy for assessing the potential pathogenicity of hRPE65 missense variants\",\"authors\":\"Giulio Poli, Salvatore Galati, Marco Macchia, Tiziano Tuccinardi\",\"doi\":\"10.1007/s00894-025-06481-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context</h3><p><i>h</i>RPE65 is an essential enzyme in the retinoid visual cycle. Numerous missense variants of <i>h</i>RPE65 have been linked to retinal disorders, such as retinitis pigmentosa and Leber congenital amaurosis. Moreover, many <i>h</i>RPE65 missense mutations are currently classified as variants of uncertain significance (VUS) due to insufficient evidence for a definitive pathogenicity classification. Addressing this limitation is critical for enabling accurate diagnoses and identifying suitable candidates for gene therapy. For this reason, we developed a <i>h</i>RPE65-tailored computational strategy, based on a consensus of multiple in silico pathogenicity predictions, enabling a rapid and reliable evaluation of the potential pathogenic effect of over 200 <i>h</i>RPE65 VUS. The analysis provided valuable insights to support the reclassification of these variants and assist clinicians in assessing their suitability for gene therapy.</p><h3>Methods</h3><p>In this study, we optimized our recently developed ConPath approach, which combines variant pathogenicity predictions from 19 different computational tools. In particular, we expanded the pool of predictive tools combined in the approach to 27, incorporated 3D-based methods that employ structural information for their prediction, and we refined the statistical framework for selecting the most reliable tools within an extended pool of more than 70 different methods. The tools were assessed based on their ability to discriminate pathogenic from benign <i>h</i>RPE65 missense mutations using an updated and expanded dataset of known <i>h</i>RPE65 variants. 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ConPath 2.0: an optimized consensus strategy for assessing the potential pathogenicity of hRPE65 missense variants
Context
hRPE65 is an essential enzyme in the retinoid visual cycle. Numerous missense variants of hRPE65 have been linked to retinal disorders, such as retinitis pigmentosa and Leber congenital amaurosis. Moreover, many hRPE65 missense mutations are currently classified as variants of uncertain significance (VUS) due to insufficient evidence for a definitive pathogenicity classification. Addressing this limitation is critical for enabling accurate diagnoses and identifying suitable candidates for gene therapy. For this reason, we developed a hRPE65-tailored computational strategy, based on a consensus of multiple in silico pathogenicity predictions, enabling a rapid and reliable evaluation of the potential pathogenic effect of over 200 hRPE65 VUS. The analysis provided valuable insights to support the reclassification of these variants and assist clinicians in assessing their suitability for gene therapy.
Methods
In this study, we optimized our recently developed ConPath approach, which combines variant pathogenicity predictions from 19 different computational tools. In particular, we expanded the pool of predictive tools combined in the approach to 27, incorporated 3D-based methods that employ structural information for their prediction, and we refined the statistical framework for selecting the most reliable tools within an extended pool of more than 70 different methods. The tools were assessed based on their ability to discriminate pathogenic from benign hRPE65 missense mutations using an updated and expanded dataset of known hRPE65 variants. The resulting enhanced strategy, ConPath 2.0, was applied to the 210 hRPE65 VUS reported in the ClinVar database.
期刊介绍:
The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling.
Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry.
Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.