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[Background] The task of computing highly accurate structural alignments of proteins in very short
computation time is still challenging. This is partly due to the complexity of protein structures.
Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of
vectors between protein backbone atoms, and other reduced representations are used. These
decrease the effort of comparing large sets of coordinates, but protein structural alignment still
remains computationally expensive.
[Results] We represent the topology of a protein structure through a structural profile that
expresses the global effective connectivity of each residue. We have shown recently that this
representation allows explicitly expressing the relationship between protein structure and protein
sequence. Based on this very condensed vectorial representation, we develop a structural
alignment framework that recognizes structural similarities with accuracy comparable to
established alignment tools. Furthermore, our algorithm has favourable scaling of computation time
with chain length. Since the algorithm is independent of the details of the structural representation,
our framework can be applied to sequence-to-sequence and sequence-to-structure comparison
within the same setup, and it is therefore more general than other existing tools.
FT and MP gratefully acknowledge generous
financial support from the Deutsche Forschungsgemeinschaft via project
PO 1025/1-1 and from the Deutscher Akademischer Austauschdienst via
project D/06/12858. UB acknowledges financial support from the Spanish
Education and Science Ministry through the Ramón y Cajal program and the
grant no. FIS2004-05073.
Peer reviewed