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Protein tolerance to random amino acid change
Haiwei H Guo1, Juno Choe, Lawrence A Loeb
1Joseph Gottstein Memorial Cancer Laboratory, Department of Pathology, University of Washington School of Medicine, Seattle, 98195-7705, USA.
Summary
Protein mutagenesis is key to evolution and biotechnology. Researchers developed a method to quantify the "x factor," the probability of a mutation inactivating a protein, finding it to be 34% for human AAG, a crucial DNA repair enzyme.
Area of Science:
- Molecular Biology
- Biochemistry
- Evolutionary Biology
Background:
- Protein mutagenesis is fundamental to evolution, aging, disease, and biotechnology.
- Understanding protein tolerance to mutations is vital for predicting functional inactivation.
- The probability of a random amino acid change causing functional loss is defined as the 'x factor'.
Purpose of the Study:
- To develop a general method for calculating the 'x factor' of proteins.
- To quantify the 'x factor' for the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG).
- To characterize tolerated amino acid substitutions in AAG and their structural determinants.
Main Methods:
- Creation of gene-wide mutagenesis libraries with varying mutation rates (2.2, 4.6, 6.2 changes/mutant).
- High-stringency selection to identify functional mutants (>19,000-fold enrichment).
- Sequencing of functional mutants to identify tolerated substitutions and mapping them onto protein structure.
Main Results:
- The 'x factor' for human AAG was determined to be 34% ± 6%.
- Reanalysis of diverse protein data suggests similar inactivation probabilities across proteins.
- Tolerated substitutions in AAG were mapped, revealing lower substitutability in conserved residues, beta-strands, and DNA-binding regions.
Conclusions:
- The developed method provides a broadly applicable approach to quantify protein mutational tolerance.
- Protein structural elements like beta-strands and DNA-binding interfaces exhibit lower tolerance to mutations.
- Findings have implications for applied molecular evolution, understanding mutation rates, and organismal mutational burden.