4.6 Article

The properties of human disease mutations at protein interfaces

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 18, 期 2, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1009858

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资金

  1. Medical Research Council [MR/M02122X/1]
  2. Medical Research Council Precision Medicine Doctoral Training Programme studentship

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Different types of interfaces have varying propensity for pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. Residues that are close in space but do not directly participate in an interface also show significant disease enrichment. Mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and this is linked to substantial variability in their identification by computational variant effect predictors.
The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

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