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Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations. The data set of binding sites by five ligands (ATP, ADP, GTP, GDP, and NAD) are collated from Biolip database. Then, five features, containing increment of diversity value, matrix scoring value, auto-covariance, secondary structure information, and surface accessibility information are used in binding site predictions. The support vector machine (SVM) model is used with the five features to predict ligand-binding sites. Finally, prediction results are tested by fivefold cross validation. Accuracy (Acc) of five ligands (ATP, ADP, GTP, GDP, and NAD) achieves 77.4%, 71.2%, 82.1%, 82.9%, and 85.3%, respectively; and Matthew correlation coefficient (MCC) of the above five ligands achieves 0.549, 0.424, 0.643, 0.659, and 0.702, respectively. The research result shows that for ligands with similar chemical structures, microenvironment of their binding sites and their sensitivities to features are similar, while, differences of their ligand-binding properties exist at the same time. © 2019 Wiley Periodicals, Inc.

China&rsquo, s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors, Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating &ldquo, bad points&rdquo, and then judged a &ldquo, bad spot&rdquo, sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization, Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China&rsquo, s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China&rsquo, s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.