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Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at . [ABSTRACT FROM AUTHOR]

Background: Postnatal human cardiomyocyte proliferation declines rapidly with age, which has been suggested to be correlated with increases in oxidative DNA damage in mice and plays an important role in regulating cardiomyocyte proliferation. However, the relationship between oxidative DNA damage and age in humans is unclear. Methods: Sixty right ventricular outflow myocardial tissue specimens were obtained from ventricular septal defect infant patients during routine congenital cardiac surgery. These specimens were divided into three groups based on age: group A (age 0–6 months), group B (age, 7–12 months), and group C (>12 months). Each tissue specimen was subjected to DNA extraction, RNA extraction, and immunofluorescence. Results: Immunofluorescence and qRT-PCR analysis revealed that DNA damage markers—mitochondrial DNA copy number, oxoguanine 8, and phosphorylated ataxia telangiectasia mutated—were highest in Group B. However immunofluorescence and qRT-PCR demonstrated that two cell proliferation markers, Ki67 and cyclin D2, were decreased with age. In addition, wheat germ agglutinin-staining indicated that the average size of cardiomyocytes increased with age. Conclusions: Oxidative DNA damage of cardiomyocytes was not correlated positively with age in human beings. Oxidative DNA damage is unable to fully explain the reduced proliferation of human cardiomyocytes. [ABSTRACT FROM AUTHOR]