BackgroundUnderstanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences.ResultsIn the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction.ConclusionsPredictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.

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BackgroundShort interfering RNAs (siRNAs) can be used to knockdown gene expression in functional genomics. For a target gene of interest, many siRNA molecules may be designed, whereas their efficiency of expression inhibition often varies.ResultsTo facilitate gene functional studies, we have developed a new machine learning method to predict siRNA potency based on random forests and support vector machines. Since there were many potential sequence features, random forests were used to select the most relevant features affecting gene expression inhibition. Support vector machine classifiers were then constructed using the selected sequence features for predicting siRNA potency. Interestingly, gene expression inhibition is significantly affected by nucleotide dimer and trimer compositions of siRNA sequence.ConclusionsThe findings in this study should help design potent siRNAs for functional genomics, and might also provide further insights into the molecular mechanism of RNA interference.

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BACKGROUND The abnormal ultimate pH (pHu) in postmortem muscles affect the meat quality and results in substantial economic losses. Dark, firm, and dry (DFD) meat linked with the higher postmortem pHu values and exhibited many quality issues such as dark color, tough texture and shorter shelf life. This research aimed to investigate the effect of protein phosphorylation on variations in beef pHu in order to explore the possible mechanisms underlying DFD meat formation. RESULTS Glycogen and lactate contents were higher, while L* and a* were lower in high pHu beef. Shear force was higher in intermediate pHu group. Global phosphorylation of sarcoplasmic proteins was higher in low pHu samples on day 1 and of myofibrillar proteins was higher in intermediate pHu meat on days 1 and 2 postmortem. Sarcoplasmic protein bands with different phosphorylation levels were identified as containing some glycometabolism and stress response proteins and phosphorylated myofibrillar protein bands were identified sarcomeric and metabolic proteins. CONCLUSIONS Phosphorylation of multiple proteins of glycolytic pathway and contractile machinery may play critical roles in development of DFD beef. © 2021 Society of Chemical Industry.

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Tenderness is an important characteristic of meat quality. Calpastatin and calpain play important roles in meat tenderization. However, it is not clear how phosphorylation affects the regulation of calpastatin on μ-calpain and, consequently, meat tenderness. Calpastatin with high and low phosphorylation levels were obtained in vitro corresponding to the treatments by protein kinase A (PKA) and alkaline phosphatase. Then, calpain was incubated with calpastatin with different phosphorylation levels, and the effect of calpastatin on calpain activity under different phosphorylation levels was analyzed. The results showed that PKA promoted the phosphorylation of calpastatin, and a high phosphorylation level was maintained during incubation. The degradation rate of μ-calpain in AP group was higher than that in the other groups, meaning there was lower inhibition of calpastatin on calpain activity. The degradation of calpastatin was lower and its structure was more stable after phosphorylation. One more serine 133 site of calpastatin was identified in PKA group compared with the other groups. Phosphorylation at serine 133 of calpastatin enhanced its inhibition on calpain activity by maintaining its structural stability, thus inhibiting the tenderization of meat.