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Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation.
Visibelli, Anna; Finetti, Rebecca; Niccolai, Neri; Spiga, Ottavia; Santucci, Annalisa.
Afiliación
  • Visibelli A; Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
  • Finetti R; Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
  • Niccolai N; Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
  • Spiga O; Le Ricerche del BarLume Free Association, Ville di Corsano, 53014 Monteroni d'Arbia, Italy.
  • Santucci A; Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
Int J Mol Sci ; 25(13)2024 Jun 25.
Article en En | MEDLINE | ID: mdl-39000061
ABSTRACT
The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Biología Computacional / Enfermedades Raras Idioma: En Revista: Int J Mol Sci Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Biología Computacional / Enfermedades Raras Idioma: En Revista: Int J Mol Sci Año: 2024 Tipo del documento: Article