Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.

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A sink moving scheme based on local residual energy of nodes in wireless sensor networks TAN Chang-geng(谭长庚), XU Ke(许 可), WANG Jian-xin(王建新), CHEN Song-qiao(陈松乔)Abstract: In the application of periodic data-gathering in sensor networks, sensor nodes located near the sink have to forward the data received from all other nodes to the sink, which depletes their energy very quickly. A moving scheme for the sink based on local residual energy was proposed. In the scheme, the sink periodically moves to a new location with the highest stay-value defined by the average residual energy and the number of neighbors. The scheme can balance energy consumption and prevent nodes around sink from draining their energy very quickly in the networks. The simulation results show that the scheme can prolong the network lifetime by 26%−65% compared with the earlier schemes where the sink is static or moves randomly.

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