4.6 Article

Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences

Journal

FRONTIERS IN MOLECULAR BIOSCIENCES
Volume 7, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2020.626363

Keywords

solvent accessible surface area; relative solvent accessibility; protein variations; prediction of solvent accessible surface; pathogenic protein variations

Funding

  1. PRIN2017 grant from the Italian Ministry of University and Research [2017483NH8_002]

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Solvent accessibility is a key feature in determining protein folding and stability, with disease associated single-residue variations (SRVs) targeting buried residues more frequently than exposed ones. Certain residue types such as glycine, tryptophan, tyrosine, and cysteine are more commonly associated with disease, and the pathogenicity of variations is often linked to the buried nature of the residue.
Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.

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