Compared to the available protein sequences of different organisms, the number of revealed protein–protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

A facile, economical and effective method to produce hierarchically porous nitrogen-rich carbon (HPNC) from wheat straw has been reported. Acid pretreatment is introduced before KOH activation, and plays the role of promoting the formation of thinner pore walls. Without any N-doping, the N content is as high as 5.13%. The HPNC when used as an anode for Li-ion batteries exhibits a superior specific capacity of 1470 mA h g⁻¹ at 0.037 A g⁻¹, and possesses an ultrahigh rate capability of 344 mA h g⁻¹ at 18.5 A g⁻¹. Even at an extremely high current density of 37 A g⁻¹, the reversible capacity is still as high as 198 mA h g⁻¹.

Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.