Compound-Target Prediction and Network-Target Analysis on Jamu Formula

Abstract

Abstract Jamu is the Indonesian traditional herbal medicine composed of several plants that have been practised for many centuries to maintain good health and to treat diseases. Unlike chemical drugs that have only one single target, the components of jamu have multiple channels and targets. Therefore, the identification of potential target and their actions is a major challenge in jamu research. We used a computational method called drugCIPHER-CS to predict the target profile for each herbal ingredient from jamu formula by combining the drug chemical interaction information in a heterogeneous network that correlates chemical, pharmacological, and genomic spaces. We also analysed network target to evaluate the importance protein of compound and pharmacological actions to help explain the action mechanisms of the jamu formula. In this work, we presented a case study of antidiabetic Jamu formula we previously developed. The jamu formula composed of four medicinal plants, namely Tinospora crispa, Zingiber officinale, Momordica charantia , and Blumea balsamifera . After determining 55 bioactive compounds in the jamu formula, we used DrugCIPHER-CS to predict their potential targets. The top 100 target profiles predicted for each compound were selected, and the target profiles of all collected compounds within a medicinal plant were combined to form integrative target profiles of this formula. We obtained 1250 potential targets of the jamu formula among 9500 listed targets on protein-protein interactions data. The potential targets then are involved in the protein interaction network and measured their topological networks by combining their degree, centrality, and betweenness. The network topology showed 20 unique potential targets hit by four medicines in jamu formula. DrugCHIPER-CS as a computational approach can speed the genome-wide identification of drug target on jamu formula efficiently. The potential target in each compound and the network demonstrate new insight to understand the mechanism on Jamu formula. However, experimental validation is still needed.

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