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Updated: Aug 21, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Counterintuitive structural and functional effects due to naturally occurring mutations targeting the active site of
Juan Luis Pacheco-García1, Ernesto Anoz-Carbonell2, Dmitry S Loginov3
1Departamento de Química Física, Universidad de Granada, Spain.
Abstract:
Our knowledge on the genetic diversity of the human genome is exponentially growing. However, our capacity to establish genotype-phenotype correlations on a large scale requires a combination of detailed experimental and computational work. This is a remarkable task in human proteins which are typically multifunctional and structurally complex. In addition, mutations often prevent the determination of mutant high-resolution structures by X-ray crystallography. We have characterized here the effects of five mutations in the active site of the disease-associated NQO1 protein, which are found either in cancer cell lines or in massive exome sequencing analysis in human population. Using a combination of H/D exchange, rapid-flow enzyme kinetics, binding energetics and conformational stability, we show that mutations in both sets may cause counterintuitive functional effects that are explained well by their effects on local stability regarding different functional features. Importantly, mutations predicted to be highly deleterious (even those affecting the same protein residue) may cause mild to catastrophic effects on protein function. These functional effects are not well explained by current predictive bioinformatic tools and evolutionary models that account for site conservation and physicochemical changes upon mutation. Our study also reinforces the notion that naturally occurring mutations not identified as disease-associated can be highly deleterious. Our approach, combining protein biophysics and structural biology tools, is readily accessible to broadly increase our understanding of genotype-phenotype correlations and to improve predictive computational tools aimed at distinguishing disease-prone against neutral missense variants in the human genome.
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