Predikin uses a cutoff value of 1 however, using a cut-off value of 0 greatly increases the number of kinases that position weight matrices can be built for, without affecting the accuracy of those position weight matrices. By using a cut-off value of 0 Predikin is able to build position weight matrices for many more protein kinases. We also asked the question of whether using a cut-off value of 0 adversely affected the distances we obtained compared with using a value of 1. We calculated the distance from the experimentally derived position weight matrix for 12 kinases using a cut-off value of both 1 and 0. In four cases, the smallest distance was produced with a cut-off value of 1 and, in a further four cases, a cut-off value of 0 gave the smallest distance. In the remaining four cases the smallest distance was equal between cutoff values. These results show that using a substitution cut-off value of 0 does not adversely affect the majority of cases �?and in some cases it even improves the Frobenius distance obtained. Again, the advantages of extending the range of Predikin are significant, while the disadvantages in increases to distance are very slight, as in most cases the increase in distance is itself very small. Figure 4 shows the Carfilzomib effect of applying various new options of Predikin to the yeast kinases characterised by Mok et al. The leftmost distribution, showing output from the original version of Predikin, shows that while all predictions made had good p-values Predikin was only able to make predictions for 25% of the kinases. By updating PredikinDB, but still using BLOSUM62 and a cut-off value of 1, Predikin is able to more than double the number of kinases predictions can be made for. The updated database also causes the median p-value to drop quite significantly. This trend is repeated when we use BLOSUM62 with a cut-off value of 0: the median p-value drops below 1e-30 and the coverage of kinase that Predikin can make predictions for rises to 80%. When we switch to BLOSUM30 we see a similar effect, with the final distribution in Figure 4 showing results using BLOSUM30 and a cut-off value of 0. Here the median p-value drops to 1e-42 and the coverage reaches over 90%. When we use the updated version of PredikinDB, the predictions generally improve, but we also see some outliers starting to appear. These always correspond to kinases that Predikin was previously unable to make predictions for. We consider the benefits of smaller Frobenius distances for most kinases and significantly greater coverage of kinases to greatly out-weigh the disadvantages of a small number of larger distances. There remained five kinases that Predikin was unable to build specificity matrices for under any circumstances: Cak1, Kin1, Psk1, Sky1 and Ypl141c. Two of these are CMGC kinases and the others are calmodulin-dependent kinases.