| Summary: | Protein function is the ultimate expression of the genetic code of every organism, and determining which proteins interact helps reveal their functions. MatrixMatchMaker (MMM) is a computational method of predicting protein-protein interactions that works by detecting co-evolution between pairs of proteins. Although MMM has several advanced features compared to other co-evolution-based methods, these come at the cost of high computation, and so the goal of this research is to improve the performance of MMM. First we redefine the computational problem posed by the method, and then develop a new algorithm to solve it, achieving a total speedup of 570x over the existing MMM algorithm for a biologically meaningful data set. We also develop hardware which has not yet succeeded in further improving the performance of MMM, but could serve as a platform that could lead to further gains.
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