Figure 9. Analysis of EFR and orthologs by other computa- tional methods. Results displayed on a homology model for (A) PAML positive/purifying selection analysis, (B) ODA analysis, (C) Consurf, and Repeat Conservation Mapping (D) RCM. For (A), residues undergoing positive selection (PP..95 for at least one model) are highlighted in yellow. In (B), ODA exclusively utilized the homology model to make its functional predictions. For Consurf (C), derived amino acid sequence data for ten orthologous LRR domains from diverse Brassicaceae species were used (see text). To allow comparison, (D) presents the RCM data of Figure 4A on a homology model. Color scale ranges from ‘‘not predicted to be a binding site/not conserved’’ (blue) to ‘‘predicted to be a binding site/ highly conserved’’ (red). All maps are shown in two views with 180u rotation between right and left columns; concave and convex faces of the LRR are indicated in (A). See Figure S7 for analysis of FLS2 using the same methods, and [55] for ODA and positive selection analyses of PGIP. doi:10.1371/journal.pone.0021614.g009 is equally possible that these divergent regions carry many residues that each make only modest contributions to elf18 recognition, with tolerance for alternative amino acid side chains. Alternatively, as was discussed for the double-alanine mutations, insignificant residues within functional regions may have been chosen for testing in our non-exhaustive mutational screen, or it may be unselected chance that the LRRs of EFR and its paralogs have diverged in this area. If these regions are under no substantial selective pressure one might expect that they would show intermediate coloration in the RCM image rather than deep blue. The deep blue sites on the map of Figure 4B are in fact quite diverse; the set of five EFR paralogs carried at least three different amino acids at most of the mutated positions, and in many cases five. As a separate matter, in the future it will be interesting to test the large conserved region that occurs on the convex face of the LRR region, especially since this region is conserved among both orthologs and paralogs of EFR. Repeat conservation mapping offers advantages over other methods that could be used to identify functional regions. RCM only requires two homologous sequences to compare, whereas positive selection analysis and Consurf are best done with at least seven sequences [46,49]. Most methods that utilize information about the spatial proximity of residues in a folded protein, such as ODA, CFG and Consurf, require a solved crystal structure or a homology model. However, LRR domains can be very challeng- ing to crystallize, and generation of a valid homology model is also highly challenging. The validation experiments conducted with RCM suggest that use of a generic LRR structural model with removal of consensus residues is sufficient to successfully predict areas of conservation or divergence. There were significant instances of overlap in the functional regions predicted by ODA and RCM. However, the two methods rely on very different concepts and they also identified non- overlapping regions in response to the same input data. This included functionally confirmed regions such as the LRR11 region of EFR and the LRR 23 region of FLS2 that ODA did not identify. Consurf was not highly informative when used with our homology-modeled FLS2 or EFR (Figures 9 and S7), possibly because of the proximity of many LRR consensus sequence residues near the LRR surface. The CFG program, while previously shown by us to be useful for some parts of LRRs [35], is not optimal because it removes hydrophobic residues from consideration even if they are on the protein surface. CFG also takes into account residues that are in the consensus of many LRRs, such as the N and P residues that are largely buried and highly conserved for structural reasons rather than driving functional differences between LRRs. By assessing variable-posi- tion rather than consensus-position residues of the LRR, RCM may benefit from a focus on the residues that are most likely to PLoS ONE | www.plosone.org 11 July 2011 | Volume 6 | Issue 7 | e21614