Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.

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We observed previously that huperzine A (HupA), a selective acetylcholinesterase inhibitor, can counteract neuronal apoptosis and cell damage induced by several neurotoxic substances, and that this neuroprotective action somehow involves the mitochondria. We investigated the ability of HupA to reduce mitochondrial dysfunction in neuron-like rat pheochromocytoma (PC12) cells exposed in culture to the amyloid beta-peptide fragment 25-35 (Abeta(25-35)). After exposure to 1 microM Abeta(25-35) for various periods, cells exhibited a rapid decline of ATP levels and obvious disruption of mitochondrial membrane homeostasis and integrity as determined by characteristic morphologic alterations, reduced membrane potential, and decreased activity of ion transport proteins. In addition, Abeta(25-35) treatment also led to inhibition of key enzyme activities in the electron transport chain and the tricarboxylic acid cycle, as well as an increase of intracellular reactive oxygen species (ROS). Pre-incubation with HupA for 2 hr not only attenuated these signs of cellular stress caused by Abeta, but also enhanced ATP concentration and decreased ROS accumulation in unharmed normal cells. Those results indicate that HupA protects mitochondria against Abeta-induced damages, at least in part by inhibiting oxidative stress and improving energy metabolism, and that these protective effects reduce the apoptosis of neuronal cells exposed to this toxic peptide.

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