HDInsight4PSi: Boosting performance of 3D protein structure similarity searching with HDInsight clusters in Microsoft Azure cloud

• HDInsight4Psi boosts performance of 3D protein structure similarity searching.
• Flexible scalability of the system is enabled by locating computations in the cloud.
• Grouping protein structures in sequence files accelerates similarity searches.
• Omission of the Reduce phase (in the MapReduce) increases efficiency of computations.
• Universal Structural Alignment Speed measure is proposed to make results comparable.

3D protein structure similarity searching is one of the important processes performed in structural bioinformatics, since it allows for protein function identification and reconstruction of phylogeny for weakly related organisms. Due to the complexity of 3D protein structures and exponential growth of protein structures in public repositories, like the Protein Data Bank, the process is time-consuming and requires increased computational resources. This causes the necessity to prepare computer systems to be able to deal with such huge volumes of macromolecular data.In this paper, we show how 3D protein structure similarity searching can be performed in parallel by distributing MapReduce jobs on the HDInsight cluster in Microsoft Azure commercial cloud. Our solution combines the use of two important computing paradigms that gain popularity in recent years—Hadoop/MapReduce and Cloud computing. Our experiments performed with the use of the whole repository of protein structures from Protein Data Bank confirm that such a technological fusion is very beneficial and can be successfully applied when performing time-consuming computations over biological data. Moreover, appropriate preparation of data allows to reduce the time needed for computations and significantly accelerates the similarity searching.