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Evolutionary Divergent Suppressor Mutations in Conformational Diseases.

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Evolutionary divergence from consensus amino acids may increase human protein vulnerability to disease-causing mutations by reducing conformational stability. Mammalian proteins with consensus residues may offer disease suppression, impacting therapeutic strategies.

Keywords:
compensatory mutationsconformational diseasesdisease-mechanismsgenotype-phenotype correlationsmolecular therapiesprotein stability

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Area of Science:

  • Protein evolution
  • Molecular biology
  • Human genetics

Background:

  • Destabilizing mutations are rarely tolerated in evolution but cause human conformational diseases.
  • Human proteins may be more susceptible to disease mutations due to evolutionary divergence from consensus states, impacting conformational stability.

Purpose of the Study:

  • To extend and refine the hypothesis that evolutionary divergence impacts protein disease vulnerability.
  • To investigate mechanisms of loss-of-function in diseases involving NQO1 and AGT proteins.
  • To explore the role of consensus amino acids in disease suppression and phenotypic variability.

Main Methods:

  • Phylogenetic and structural analyses.
  • Structure-based energy calculations.
  • Structure-function studies at molecular and cellular levels.
  • Focus on mammalian orthologues of NQO1 and AGT proteins.

Main Results:

  • Divergence from consensus amino acids can decrease protein conformational stability, increasing disease vulnerability.
  • Loss-of-function mechanisms include enzyme inactivation, accelerated degradation, mistargeting, and aggregation.
  • Consensus amino acids in mammalian orthologues may act as suppressors of human disease.

Conclusions:

  • Evolutionary changes in protein sequences influence disease susceptibility.
  • Understanding these evolutionary dynamics can inform therapeutic approaches for conformational diseases.
  • Phenotypic variability arises from post-translational modifications and protein quality control, affecting disease models.