Various protein functions are essential to diverse biological processes. Elucidating these protein functions and linking functional related proteins helps our understanding of the mechanisms of biological systems at the molecular level. Nowadays, various protein intrinsic features (e.g. protein sequences, structures, functions and so on) have been studied to predict functional related proteins. However, no studies have analysed the regulatory features (e.g. transcription factors that regulate the gene of a protein) between two interacting proteins. This study aims to answer whether regulatory features preserve effects on functional relation after the gap from gene to protein as well as to build a regulatory feature-based prediction model for functional related proteins.This study has conducted a comprehensive analysis of regulatory features. It collected eight kinds of transcriptional characteristics and encoded them to 16 transcriptional features: DNA bendability, gene size (with sense or with antisense), gene distance, nucleosome occupancy, TATA box information, TF binding and knockout information and eight regulatory similarities based on TFBS data. The experimental results show that gene distance, gene size, and TATA box information improved the prediction performance with 7% area under curve and indicate that these regulatory features did influence the functional relation after the gap from gene to protein. In Saccharomyces cerevisiae, our method’s prediction is better than previous methods.This work is the first study to discuss the regulatory features in predicting functional related proteins and the results suggest this category of features must be considered in the future. The proposed new regulatory characteristic encoding method has been shown capable to identify whether two proteins are functional related and. The constructed prediction model is helpful to discover the unknown molecular mechanisms of specific regulatory functions. Finally, this study leads the following works in related research topics to consider regulatory features, even the topics are in the protein level.