For all kinases that made up the challenge, Predikin produced the most accurate predictions. To assess the ability of these new features to increase the number of protein kinases Predikin can make predictions for, and to evaluate their affect on accuracy, a published data set of 61 protein kinase from yeast was used. For each of these kinases, a position weight matrix, which described the sequence specificity surrounding the phospho-residue, had been experimentally determined. To successfully build a position weight matrix, the Predikin method relies on identifying similar specificity-determining residues, and this, in turn, is reliant on the substitution matrix used. Testing has shown that the use of different substitution matrices can enable Predikin to build position weight matrices for more protein kinases. To analyse the benefits of using different substitution matrices, we attempted to build position weight matrices for each of the yeast protein kinases using various BLOSUM matrices. To assess the quality of Predikin‘s position weight matrices we used the same evaluation method as the DREAM4 challenge: similarity to a experimentally mapped position weight matrix using the distance induced by the Frobenius norm. The DREAM4 challenge also provided p-values for each Frobenius distance, this is the probability that a random position weight matrix has the same or smaller Frobenius distance, and we have applied the same method to calculate p-values for the yeast kinase predictions. From 16 BLOSUM matrices, BLOSUM30 clearly stands out as providing the most position weight matrices, but an important question is whether the position weight matrices produced by this matrix are as accurate as those built by Predikin‘s default substitution matrix: BLOSUM62? We calculated the Frobenius distance for the 12 protein kinases for which a position weight matrix can be built using all of the substitution matrices. For any given kinase, the distance produced does not vary greatly as the BLOSUM matrix changes. These results also show that there is no Pazopanib single best substitution matrix �?the best matrix to use is dependant on the kinase, and that while we may not select the best matrix for individual kinases every time, the difference in the prediction is likely to be very small. Together these results show that we are able to increase the number of kinases Predikin can build position weight matrices for by changing the substitution matrix, and that BLOSUM30 captures the most kinases. We have also shown that the distance to the experimentally derived position weight matrix is not adversely effected by the use of BLOSUM30. We have also found that altering the substitution matrix cut-off value affects the number of position weight matrices that can be built. BLOSUM62 contains numbers ranging from 24 to 11 with higher numbers indicating more likely substitutions.