  A variety of methods have been proposed for structure similarity calculation,
which are called structure alignment or superposition. One major shortcoming in
current structure alignment algorithms is in their inherent design, which is
based on local structure similarity. In this work, we propose a method to
incorporate global information in obtaining optimal alignments and
superpositions. Our method, when applied to optimizing the TM-score and the GDT
score, produces significantly better results than current state-of-the-art
protein structure alignment tools. Specifically, if the highest TM-score found
by TMalign is lower than (0.6) and the highest TM-score found by one of the
tested methods is higher than (0.5), there is a probability of (42%) that
TMalign failed to find TM-scores higher than (0.5), while the same probability
is reduced to (2%) if our method is used. This could significantly improve the
accuracy of fold detection if the cutoff TM-score of (0.5) is used.
  In addition, existing structure alignment algorithms focus on structure
similarity alone and simply ignore other important similarities, such as
sequence similarity. Our approach has the capacity to incorporate multiple
similarities into the scoring function. Results show that sequence similarity
aids in finding high quality protein structure alignments that are more
consistent with eye-examined alignments in HOMSTRAD. Even when structure
similarity itself fails to find alignments with any consistency with
eye-examined alignments, our method remains capable of finding alignments
highly similar to, or even identical to, eye-examined alignments.
