蛋白質與蛋白質間的交互作用（protein-protein interaction，PPI）在生物所表現的功能中扮演重要的角色，找出這些PPI有助於瞭解分子層級中生物系統的反應機制。時至今日，有許多蛋白質固有的特性（包括；蛋白質序列、結構、功能等）被用來預測PPI。然而，沒有關於調控特性（舉例：調控蛋白質基因的轉錄因素）是否影響PPI的直接研究。本研究分析基因的調控特性是否會對PPI有影響，並建立一個基於調控特性的預測模組來預測PPI。
本研究進行了一系列對調控特性相關的完整測試，蒐集了8種不同的調控特性，並將其轉錄成12種特徵向量，包含：DNA 彎曲度、基因距離、基因大小、核小體佔有率、TATA 盒、轉錄因子結合證據、轉錄因子破壞證據以及轉錄因子結合位點相似度。實驗結果顯示，基因距離對預測蛋白質對之間是否有PPI有顯著效益，而且，依此方法對釀酒酵母菌（Saccharomyces cerevisiae）預測的結果較其他預測器優秀。
本實驗是第一個探討調控特性對PPI影響的研究，而且證實了調控特性應該被考慮在特徵中而不該被忽略。加入調控特性的特徵模組有助於幫研究者找到未知的分子機制。最後，本研究為往後的研究提供了一個新的往調控特性前進的研究方向。

Protein-protein interaction (PPIs) are essential to diverse biological processes. Elucidating these PPIs helps our understanding of the mechanisms of biological systems at the molecular level. Nowadays, various protein intrinsic features have been studied to predict PPIs. However, no studies have analyzed the regulatory features between two interacting proteins. This study aims to answer whether regulatory features preserve effects on PPIs after the gap from gene to protein as well as to build a regulatory feature-based prediction model to predict PPIs.
This study has conducted a comprehensive analysis of regulatory features. It collected eight kinds of transcriptional characteristics and encoded them to 12 transcriptional features: DNA bendability, gene size, 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, improved the prediction performance and indicate that these regulatory features did influence the PPI prediction after the gap from gene to protein. In Saccharomyces cerevisiae, our method’s prediction is better than other methods.
This work is the first study to discuss the regulatory features in predicting PPIs and the results suggest this category of features must be considered in the future. The pro-posed new regulatory characteristic encoding method has been shown capable to identify whether two proteins have interaction. 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.

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