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The comparison of protein tertiary structures is a key milestone in many structural bioinformatics activities that rely in comparing very large structure datasets. As the number of proteins in the dataset increases, the corresponding computational time taken by the protein structure comparison algorithms also increases, squarely for an all-against-all comparison and linearly for an all-against-target assessment. Thus ever larger proteomics problems call for the distribution of pairwise comparison jobs in the form of well granulated subsets/packages to be run in parallel on a pool of networked processors/workstations under the coordination of a message passing interface (MPI) environment. This paper evaluates the effect on the performance of such jobs when the MPI environment is integrated with a local resource management system (LRMS) such as sun grid engine (SGE). From our experiments with different ways of integration we draw a comparative picture of all possible approaches with the description of resource usage information for each parallel job on each processor. Understanding of different ways of integration sheds light on the most promising routes for setting up an efficient environment for very large scale protein structure comparisons.