perhaps is the reason that SQ026 tolerated mutations in P30 and G33, while SQ035 and SQ032 did not. This would also explain why Run 1 and Run 2 were less successful in their design. Not only does the more restrictive MCE Company Danshensu (sodium salt) constraints not allow for the increase in charge constraints seen in Run 4, but since there are no asparagine residues in the initial sequences, there was no opportunity for the S28N mutation through simple rearrangement. This suggests that the constraints used for Runs 1 and 2 were perhaps too restrictive and should be loosened in a similar manner to Run 3 and Run 4 for 1687736-54-4 future designs. Overall, from this analysis it would seem that Run 4 contains the optimum set of constraints for this design, allowing for both increased charge content and the S28N mutations, while restricting changes to positions 30 and 33 that were largely unsuccessful. While the S28N mutation may be specific to EZH2 inhibitor design, the increased charge constraint may be characteristic of general histone-modifying enzyme inhibitor design and is worthy of further exploration. Analysis of the template-based constraints demonstrate how one can use the results from this study to guide future EZH2 and other histone-modifying enzyme design. In order to guide future peptide inhibitor design more generally, however, one must analyze the influence of the fold specificity and approximate binding affinity metrics on the capability of the method to correctly identify peptidic inhibitors. Hence, it is useful to focus on the peptides derived from Run 4 that stood out in the endpoint assay and IC50 results. In analyzing the quantitative results from the Fold Specificity and Approximate Binding Affinity validation stages, these three peptides are the top three ranked peptides in Fold Specificity and three of the top four in Approximate Binding Affinity. Since these values are used simply as a ranking metric, this demonstrates the usefulness that both metrics have in producing designed peptides with a high probability of success. Besides analyzing the influence of input biological constraints and the selection metrics, it is also important to analyze the results of the method from a structural perspective. This allows us to determine how well the validation stages predict consistent contacts that may play an important role in the inhi
