SummaryA significant part of cellular proteins undergo reversible thiol-dependent redox transitions which often control or switch protein functions. Thioredoxins and glutaredoxins constitute two key players in this redox regulatory protein network. Both interact with various categories of proteins containing reversibly oxidized cysteinyl residues. The identification of thioredoxin/glutaredoxin target proteins is a critical step in constructing the redox regulatory network of cells or subcellular compartments. Due to the scarcity of thioredoxin/glutaredoxin target protein records in the public database, a tool called Reversibly Oxidized Cysteine Detector (ROCD) is implemented here to identify potential thioredoxin/glutaredoxin target proteins computationally, so that the in silico construction of redox regulatory network may become feasible. ROCD was tested on 46 thioredoxin target proteins in plant mitochondrion, and the recall rate was 66.7% when 50% sequence identity was chosen for structural model selection. ROCD will be used to predict the thioredoxin/glutaredoxin target proteins in human liver mitochondrion for our redox regulatory network construction project. The ROCD will be developed further to provide prediction with more reliability and incorporated into biological network visualization tools as a node prediction component. This work will advance the capability of traditional database-or text mining-based method in the network construction.
Today we have access to more than 1500 molecular database systems inside the internet. Based on these databases and information systems, computer scientists developed and implemented different methods for the automatic integration and prediction of biological networks. The idea is to use such methods for the automatic prediction and expansion of rudimentary molecular knowledge. However, the inherent data deficiency problem concerning the properties of specialized network hampers the database- and text-mining-based network construction. This paper presents the concept concerning the computational network expansion, namely for the specific biological network-thiol-disulfide redox regulatory network. Besides, a network-contexted document retrieval system (ncDocReSy) is also introduced to assist the network reduction by providing indirectly relevant literature for user's manual curation. NcDocReSy combines literature search with biological network and ranks the retrieved literature according to the network topology. NcDocReSy is implemented as a Cytoscape plugin.
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