Hardware Acceleration of the STRIKE String Kernel Algorithm for Estimating Protein to Protein Interactions.

Protein-protein interaction (PPI) is an important field in bioinformatics which helps in understanding diseases and devising therapy. PPI aims at estimating the similarity of protein sequences and their common regions. STRIKE was introduced as a PPI algorithm which was able to achieve reasonable improvement over existing PPI prediction methods. Although it consumes a lower execution time than most of other state-of the-art PPI prediction methods, its compute-intensive nature and the large volume of protein sequences in protein databases necessitate further time acceleration. In this paper, we develop hardware accelerator designs for the STRIKE algorithm. Results indicate that the weighted STRIKE accelerator execution times are about 10x longer than the unweighted STRIKE accelerator execution times. To further accelerate the performance of the weighted STRIKE, a parallel module accelerator organization duplicating the weighted STRIKE modules is introduced, achieving near linear speedups for long sequences of 100 or more characters. As demonstrated by Verilog simulations and FPGA runs, the weighted STRIKE module accelerator exhibits three orders of magnitude speed improvement over multi-core and cluster computers. Much higher speedups are possible with the parallel module accelerator.