Integrative Method Based on Linear Regression for the Prediction of Zinc-Binding Sites in Proteins

Zinc is an important trace element, and it can be used in combination with proteins to play an important biological function. In this paper, three types of prediction tools based on sequence were studied for the prediction of zinc-binding sites in proteins, and a novel integrated predictor termed meta-zincPrediction is presented. Multiple linear regressions were used in the proposed approach to integrate the results of the three prediction tools, and the parameters were estimated by the least square method until the optimal model was constructed. Using the Zhao_dataset data set, the area under recall-precision curve (AURPC) of our predictor reached nearly 0.9 and increased by 2%-9% compared with the other three predictors; the other performance indexes were also improved. To further demonstrate the robustness and accuracy of the integrated predictor, we tested it on a non-redundant independent test dataset (CollectedDataset). The AURPC increased by 2%-8%. The other three indexes, including the precision, specificity, and MCC, were increased by 5%-8%, 2%-8%, and 4%-12%, respectively, with a recall of 70%. The prediction ability of the meta-zincPrediction was better than the other three predictors, regardless of whether the zinc-binding sites contained four types of residues or a single residue. Our method can be applied to the recognition of zinc binding sites based on sequence information, but will also be useful for inferring protein function and more propitious for the treatment of some diseases.