Predicting the three-dimensional structures of proteins: Combined alignment approach

Protein structure prediction is a great challenge in molecular biophysics and bioinformatics. Most approaches to structure prediction use known structure information from the Protein Data Bank (PDB). In these approaches, it is most crucial to find a homologous protein (template) from the PDB to a query sequence and to align the query sequence to the template sequence. We propose a profile-profile alignment method based on the cosine similarity criterion, and combine this with a sequence-profile alignment, the secondary structure prediction of the query protein, and the experimental secondary structure of the template protein. Our method, which we call combined alignment, provides good results for the 1107 query-template pairs of the SCOP database and the CASP5 target proteins. They show that combined alignment significantly improves the recognition of distant homology.