}}</ref> approaches the alignment problem from a different objective than almost all other methods. Rather than trying to find an alignment that maximally superimposes the largest number of residues, it seeks tothe arrivesubset atof a well defined probability that itsthe structural alignment 3Dleast overlaplikely wasto notoccur randomby chance. To do this it marks a local motif alignment with flags to indicate which residues simultaneously satisfy more stringent criteria: 1) Local structure overlap 2) regular secondary structure 3) 3D-superposition 4) same ordering in primary sequence. It converts the statistics of the number of residues with high-confidence matches and the size of the protein to compute an Expectation value for the outcome by chance. It excels at matching remote homologs, particularly structures generated by ab initio structure prediction to structure families such as SCOP, because it emphasizes extracting a statistically reliable sub alignment and not in achieving the maximal sequence alignment or maximal 3D superposition.<ref name="Malmstrom"></ref><ref name="robetta">{{cite journal
