Figure 7. Site-directed, randomizing mutagenesis of five FLS2 residues in a region predicted by RCM to be functional identifies functional sites. (A) RCM map of eight Brassicaceae FLS2 orthologs. (B) Response to flg22 in T1 seedlings carrying FLS2 mutagenized at sites predicted by RCM. Each library represents a pool of fls2-101 seedlings, each with one FLS2 construct carrying a random mutation at the position indicated. WT: wild-type; EV: empty vector; allele numbers as in Figure 6, and parentheses enclose the amino acid number and letter (indexed both by amino acid number and LRR position). Seedlings were scored as sensitive, insensitive, or partially sensitive to 1 uM flg22 in a seedling growth inhibition assay by comparison to 95% confidence intervals for data from fls2-101 plants transformed with a wild-type FLS2 or empty vector control, assayed on the same day. At least 65 plants per library were tested over 3–4 independent experiments, and the results are pooled. doi:10.1371/journal.pone.0021614.g007 Repeat Conservation Mapping To investigate overlap between RCM output and outputs from the other computational methods, we looked for a correlation between our scoring method and positive selection analysis, ODA, and Consurf analysis (Figure 10). No strong correlation between any of the three computational methods and RCM was observed (R2,0.80). However, all methods could identify important func- tional residues. T-tests for differences between the set of scores for residues tested by mutation in which function was disrupted (Figure 10, black symbols), in comparison to the scores for residues for which function was retained after mutation (Figure 10, white symbols), revealed significant differences (p,0.05) for RCM, ODA, Consurf and positive selection analyses. However, of all the methods, RCM had the most significant difference (p,.0001). The ability of RCM to perform at least as well as other contemporary, well-accepted computational methods, in conjunction with its need for only two homologous sequences and no homology model, highlights the utility of this method. Discussion The RCM method identifies regions on the predicted surface of LRR domains that have been conserved over the course of evolution. Conserved regions on the surface of folded proteins often correspond to key functional sites such as ligand binding sites or enzyme catalytic sites [44,45]. In many cases where solved LRR+ligand crystal structures and appropriate homologs were available, RCM correctly identified the ligand binding regions of LRR domains. RCM also enabled discovery of previously un- known functional sites on the surface of EFR and FLS2. A strong trend was observed when RCM was used to direct mutational studies: significant impacts on function were frequently observed for mutations in regions that RCM predicted as conserved, while impacts were rare for mutations in non-conserved regions. This demonstrates the utility of RCM for de novo prediction of functional sites. The RCM analyses of proteins for which there are LRR+ligand crystal structures demonstrated that conservation mapping can work for orthologs or paralogs, as long as they share similar functions. For example, creating a conservation map of RI or- thologs highlighted functionally important regions previously demonstrated to be important for common function among these proteins. Mapping TIR1 and its Arabidopsis AFB1-5 paralogs, as well as PGIP1-4, demonstrated the utility of RCM for compar- isons of functionally related paralogs. TIR1 functional sites again mapped to conserved regions of the protein. Irregularities in an LRR domain must be considered when generating and interpret- ing the results of RCM maps. Still, RCM performed well over irregular loop regions. In TIR1, for example, a loop region in the second LRR is involved in binding auxin, and RCM correctly identified this region as a functional site. In contrast to comparison of orthologs, functionally important sites of PGIP could be seen in divergent regions highlighted by the program, as might be expected since PGIPs display varied specificity and inhibition mechanisms towards different polygalac- turonases [84,85]. This reinforces the point that careful choice of input sequences is crucial to gaining meaningful output data. It will be interesting in the future to map plant NB-LRR proteins that exhibit direct recognition of changing pathogen ligands, where the most divergent LRR sites might be predicted to be the sites of ligand recognition, and to compare these results to those obtained using Ka/Ks (positive selection) analyses (e.g., [86,87]). Although it was encouraging that 45% of all interaction residues from LRR+ligand structures appeared in the top 20% of RCM scores, some interaction residues received low conservation scores PLoS ONE | www.plosone.org 9 July 2011 | Volume 6 | Issue 7 | e21614