Background/Objectives: Somatic and genetic mutations in glutathione peroxidases (GPxs), including GPx7 and GPx8, have been linked to intellectual disability, microcephaly, and various tumors. GPx7 and GPx8 evolved the latest among the GPx enzymes and are present in the endoplasmic reticulum. Although lacking a glutathione binding domain, GPx7 and GPx8 possess peroxidase activity that helps the body respond to cellular stress. However, the protein mutations in these peroxidases remain relatively understudied. Methods: By elucidating the structural and stability consequences of missense mutations, this study aims to provide insights into the pathogenic mechanisms involved in different cancers, thereby aiding clinical diagnosis, treatment strategies, and the development of targeted therapies. We performed saturated computational mutagenesis to analyze 2926 and 3971 missense mutations of GPx7 and GPx8, respectively. Results: The results indicate that G153H and G153F in GPx7 are highly destabilizing, while E93M and W142F are stabilizing. In GPx8, N74W and G173W caused the most instability while S70I and S119P increased stability. Our analysis shows that highly destabilizing somatic and genetic mutations are more likely pathogenic compared to stabilizing mutations. Conclusions: This comprehensive analysis of missense mutations in GPx7 and GPx8 provides critical insights into their impact on protein structure and stability, contributing to a deeper understanding of the roles of somatic mutations in cancer development and progression. These findings can inform more precise clinical diagnostics and targeted treatment approaches for cancers.
more »
« less
A Computational Approach: The Functional Effects of Thyroid Peroxidase Variants in Thyroid Cancer and Genetic Disorders
PURPOSEThyroid peroxidase (TPO) is essential for the synthesis of thyroid hormones. However, specific mutations render TPO antigenic and prone to autoimmune attacks leading to thyroid cancer, TPO deficiency, and congenital hypothyroidism (CH). Despite technological advancement, most experimental procedures cannot quickly identify the genetic causes of CH nor detect thyroid cancer in the early stages. METHODSWe performed saturated computational mutagenesis to calculate the folding energy changes (∆∆G) caused by missense mutations and analyzed the mutations involved in post-translational modifications (PTMs). RESULTSOur results showed that the functional important missense mutations occurred in the heme peroxidase domain. Through computational saturation mutagenesis, we identified the TPO mutations in G393 and G348 affecting protein stability and PTMs. Our folding energy calculations revealed that seven of nine somatic thyroid cancer mutations destabilized TPO. CONCLUSIONThese findings highlight the impact of these specific mutations on TPO stability, linking them to thyroid cancer and other genetic thyroid-related disorders. Our results show that computational mutagenesis of proteins provides a quick insight into rare mutations causing Mendelian disorders and cancers in humans.
more »
« less
- Award ID(s):
- 2000296
- PAR ID:
- 10510834
- Publisher / Repository:
- ASCO Publications
- Date Published:
- Journal Name:
- JCO Clinical Cancer Informatics
- Issue:
- 8
- ISSN:
- 2473-4276
- Subject(s) / Keyword(s):
- Computational mutagenesis Post-Translational Modification Hypothyroidism Thyroid cancer Missense mutations
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Myeloperoxidase (MPO) is a heme peroxidase with microbicidal properties. MPO plays a role in the host’s innate immunity by producing reactive oxygen species inside the cell against foreign organisms. However, there is little functional evidence linking missense mutations to human diseases. We utilized in silico saturation mutagenesis to generate and analyze the effects of 10,811 potential missense mutations on MPO stability. Our results showed that ~71% of the potential missense mutations destabilize MPO, and ~8% stabilize the MPO protein. We showed that G402W, G402Y, G361W, G402F, and G655Y would have the highest destabilizing effect on MPO. Meanwhile, D264L, G501M, D264H, D264M, and G501L have the highest stabilization effect on the MPO protein. Our computational tool prediction showed the destabilizing effects in 13 out of 14 MPO missense mutations that cause diseases in humans. We also analyzed putative post-translational modification (PTM) sites on the MPO protein and mapped the PTM sites to disease-associated missense mutations for further analysis. Our analysis showed that R327H associated with frontotemporal dementia and R548W causing generalized pustular psoriasis are near these PTM sites. Our results will aid further research into MPO as a biomarker for human complex diseases and a candidate for drug target discovery.more » « less
-
Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.more » « less
-
While worldwide efforts for improving COVID-19 vaccines are currently considered a top priority, the role of the genetic variants responsible for virus receptor protein stability is less studied. Angiotensin-converting enzyme-2 is the primary target of the SARS-CoV-1/SARS-CoV-2 spike (S) glycoprotein, enabling entry into the human body. Here, we applied computational saturation mutagenesis approaches to determine the folding energy caused by all possible mutations in ACE2 proteins within ACE2 - SARS-CoV-1-S/ACE2 - SARS-CoV-2-S complexes. We observed ACE2 mutations at residue D350 causing the most stabilizing effects on the protein. In addition, we identified ACE2 genetic variations in African Americans (rs73635825, rs766996587, and rs780574871), Latino Americans (rs924799658), and both groups (rs4646116 and rs138390800) affecting stability in the ACE2 - SARS-CoV-2-S complex. The findings in this study may aid in targeting the design of stable neutralizing peptides for treating minority patients.more » « less
-
Abstract Background3′-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging. ResultsWe introduce a residual neural network model,APARENT2, that can infer 3′-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2’s performance on several variant datasets, including functional reporter data and human 3′ aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3′ untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of$${>}43$$ million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3′-end and autism spectrum disorder. To experimentally validate APARENT2’s predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells. ConclusionsA sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3′-end mutations and human health.more » « less
An official website of the United States government

