Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation
Metadata
Show full item recordEditorial
Frontiers
Materia
Protein function Protein stability Genotype-phenotype correlations Computational prediction Sequence conservation
Date
2022-11-24Referencia bibliográfica
Pacheco-Garcia JL... [et al.] (2022), Effect of naturally-occurring mutations on the stability and function of cancerassociated NQO1: Comparison of experiments and computation. Front. Mol. Biosci. 9:1063620. doi: [10.3389/fmolb.2022.1063620]
Sponsorship
ERDF/Spanish Ministry of Science, Innovation and Universities-State Research Agency; Junta de Andalucia RTI 2018-096246-B-I00; ERDF/Counseling of Economic transformation, Industry, Knowledge and Universities P18-RT-2413; Comunidad Valenciana B-BIO-84-UGR20; Novo Nordisk Foundation; Novocure Limited CIAICO/2021/135 NNF18OC0033950Abstract
Recent advances in DNA sequencing technologies are revealing a large
individual variability of the human genome. Our capacity to establish
genotype-phenotype correlations in such large-scale is, however, limited.
This task is particularly challenging due to the multifunctional nature of
many proteins. Here we describe an extensive analysis of the stability and
function of naturally-occurring variants (found in the COSMIC and gnomAD
databases) of the cancer-associated human NAD(P)H:quinone oxidoreductase
1 (NQO1). First, we performed in silico saturation mutagenesis studies
(>5,000 substitutions) aimed to identify regions in NQO1 important for
stability and function. We then experimentally characterized twenty-two
naturally-occurring variants in terms of protein levels during bacterial
expression, solubility, thermal stability, and coenzyme binding. These studies
showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the
substitutions are similarly distributed in variants from the COSMIC and
gnomAD databases. Outliers in these experimental-computational
genotype-phenotype correlations remain, and we discuss these on the
grounds and limitations of our approaches. Our work represents a further
step to characterize the mutational landscape of NQO1 in the human
genome and may help to improve high-throughput in silico tools for
genotype-phenotype correlations in this multifunctional protein associated
with disease.