Protein succinylation is a biochemical reaction in which a succinyl group (-CO-CH2-CH2-CO-) is attached to the lysine residue of a protein molecule. Lysine succinylation plays important regulatory roles in living cells. However, studies in this field are limited by the difficulty in experi-mentally identifying the substrate site specificity of lysine succinylation. To facilitate this process, several tools have been proposed for the computational identification of succinylated lysine sites. In this study, we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition. Using experimentally verified lysine succinylated sites col-lected from public resources, the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo pro-gram. These findings enabled the adoption of an effective machine learning method, support vector machine, to train a predictive model with not only the amino acid composition, but also the com-position of k-spaced amino acid pairs. After the selection of the best model using a ten-fold cross-validation approach, the selected model significantly outperformed existing tools based on an inde-pendent dataset manually extracted from published research articles. Finally, the selected model was used to develop a web-based tool, SuccSite, to aid the study of protein succinylation. Two pro-teins were used as case studies on the website to demonstrate the effective prediction of succinyla-tion sites. We will regularly update SuccSite by integrating more experimental datasets. SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.

OBJECTIVES: The effects of serum uric acid (UA) level on a variety of diseases were found from experimental and observational studies via oxidative stress and anti-oxidants. However, research on the association of UA and hearing thresholds is relatively sparse. We investigated this issue in the U.S. general population to evaluate the relationship of serum UA levels and pure tone threshold of hearing. METHODS: Forty four thousand eighty four eligible participants aged 20 to 69 years who have serum UA data and received Audiometry Examination Component were enrolled from the National Health and Nutrition Examination Survey 1999–2004. Hearing thresholds (dB) as a pure tone average at low frequencies (0.5, 1, 2 kHz) and at high frequencies (3, 4, 6, and 8 kHz) were computed. Multivariate linear regression models and tertile-based analysis with an extended-model approach for covariates adjustment were used to assess the correlation between serum UA level and hearing thresholds. RESULTS: In the adjusted mode of tertile-based analysis, the regression coefficients elucidated as the change of log-transformed mean hearing thresholds upon comparing participants in the highest tertile of serum UA to those in the lowest tertile were –0.067 (P=0.023) in high frequency and –0.058 (P=0.054) in low frequency. After adjusting for multiple pertinent covariates, inverse association between tertiles of serum UA and hearing thresholds remained essentially unchanged. The negative trends between serum UA and hearing thresholds were statistically significant (P for trends <0.05) in tertile-based multiple linear regressions. CONCLUSION: Individuals with elevated UA levels independently were found to be inversely associated with hearing thresholds for pure tone audiometry in a nationally representative sample of U.S. adults.
Adult ; Antioxidants ; Audiometry ; Hearing* ; Humans ; Linear Models ; Neuroprotection ; Nutrition Surveys ; Oxidative Stress ; Uric Acid*