Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation
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Protein functionProtein stabilityGenotype-phenotype correlationsComputational predictionSequence conservation
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]
SponsorshipERDF/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 NNF18OC0033950
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.