4.6 Article

Linking protein structural and functional change to mutation using amino acid networks

Journal

PLOS ONE
Volume 17, Issue 1, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0261829

Keywords

-

Funding

  1. CONACYT [285544/2016, 2115]
  2. National Institute of Genomic Medicine (Mexico)
  3. National Laboratory of Complexity Sciences [232647/2014 CONACYT]
  4. 2016 Marcos Moshinsky Fellowship in the Physical Sciences

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The study investigates the relationship between protein structure and function, revealing a strong correlation between structural changes and functional changes. Significant changes in protein structure are associated with functional loss, while positions that gain function through mutations tend to be structurally robust. A computational method is also proposed to predict mutation-sensitive positions using structural changes, demonstrating good predictive performance.
The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

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