We have used computational methods to design multiple peptide inhibitors of the DENV E glycoprotein. Importantly, out of seven peptides synthesized and tested, two peptides with high activity and one peptide with intermediate activity were identified. A high resolution crystal structure of the pre-fusion conformation of the DENV-2 E [14] was used as the starting point to generate in situ energy minimized peptides. Two distinct approaches were used for the design of these peptides. First, we built upon previous work targeting DENV fusion peptide and domain II hinge regions with naturally occurring E protein sequences from these regions [9] . No inhibitory activity was found for the optimized fusion peptide region sequence (DN80opt), indicating that this region may not be a promising target mechanistically for DENV peptide inhibitors. Since an analogous, naturally occurring WNV domain II hinge region peptide was shown to be inhibitory against WNV [9] , we reasoned that a more tightly binding analog of this region in the DENV E protein could be designed and might have improved inhibitory activity. This turned out to be correct, and we identified two distinct binding-optimized peptide sequences to this region with antiviral activity, DN57opt and DN81opt. This supports previous predictions of hinge region inhibitors and the proposed mechanism of fusion based on hinge region movements [11, 14, 15] . The second approach to designing peptide inhibitors was to identify peptides with non-native sequences derived from E protein regions that are highly stable in terms of structure and binding as evaluated by an all-atom scoring function (RAPDF). This identified four regions that were used to derive additional optimized peptides (Figure 1 ). Of the four resulting peptides tested, one, 1OAN1, was identified as having antiviral activity. This confirms the use of the sliding window RAPDF minimization approach for finding tightly binding protein ligands [23, 24] . It is perhaps not surprising that computational binding optimization increased the activity of previously inactive peptides that were based on naturally occurring E protein sequences. Naturally occurring sequences have multiple balancing selection pressures that may limit their binding stability in vivo. The combined use of primary sequence prediction tools [9] and structural optimization tools [23, 24] should be a valuable approach for finding binding partners and inhibitors for other protein targets.