The search has thus been expanded to include sequences that match the variations found in the motif at each sequence position. The results are again displayed, indicating any newly discovered sequences that are significantly related to the motif sequences in addition to those found in the previous iteration. Again, an opportunity is given to go through another iteration of the program, but this time including any newly recruited sequences to refine the motif. In this fashion, a new family of sequences that are significantly similar to the original query sequence can be found. The main difficulty with searching for subtle sequence relationships based on similarity is determining the significance of the motifs that are found.
Protein structures are in general comprised of a tightly packed core and outside loops.
Amino acid subsitutions within the core are common but only certain subsitutions will work at a given amino acid position in a given structure. Another difficulty with the Psi-Blast approach is that the procedure follows a type of algorithm called a greedy algorithm. Put simply, once additional sequences which match the query are found, then these newly found sequences influence the finding of more sequences like themselves, and so on. If a different query sequence were initially used, a different group with the possible overlaps with the first may be found. Thus, there is no guarantee that the group finally discovered authentically represents a functional group.