Informative proteins are the proteins that play critical functional roles inside cells. They are the fundamental knowledge of translating bioinformatics into clinical practices. Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary, without considering the dynamics characteristics of biological processes. In this paper, we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks (PPINs). In this model, the common representation of multiple PPINs is learned using a deep feature generation model, based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins. Experiments were implemented on data of yeast cell cycles and different prostate cancer stages. We analyze the effectiveness of reconstruction by comparing different methods, and the ranking results of informative proteins were also compared with the results from the baseline methods. Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.