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To predict protein-protein docking sites in a massive protein dataset, we built a cloud computing based computing pipeline. This pipeline conforms to Elastic MapReduce. The implementation of this pipeline includes three components. First, the cloud computing is based on the application of the open source hadoop platform. Second, the pipeline combines several existing protein-protein docking site methods. Third, the pipeline takes advantage of network computing resource to predict protein-protein docking sites by distributed data processing services. The results show our method can highly improve the performance of protein-protein docking site prediction. Index Terms—Cloud-computing; protein docking site; pipeline. method has either extinguished strengths or drawbacks. Effective combination of these tools would achieve a better result. Considering the distributed computing, flexible storage and scalable management, we attempt to use the cloud computing platform to predict the protein docking sites from large scale datasets not only to save energy of the method but also to improve stability and availability of the application. In order to improve the prediction of protein docking sites, we apply the MapReduce (4) which is a distributed computing architecture firstly proposed by Google to solve the large amount of data and to convert the result into the file system or database. We design a pipeline by combining diverse public prediction tools of protein docking sites to get the highly reliable protein docking site candidates.

Prediction of Protein-Protein Docking Sites Based on a Cloud-Computing Pipeline