Protein structure similarity from principle component correlation analysis |
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| Authors: | Xiaobo Zhou James Chou Stephen TC Wong |
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| Affiliation: | (1) Harvard Center for Neurodegeneration and Repair – Center for Bioinformatics, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA;(2) Functional and Molecular Imaging Center, Radiology Department, Brigham and Women's Hospital, One Brigham Circle, 1620 Tremont Street, Boston, MA 02121, USA;(3) Department of Biological Chemistry and Molecular Pharmacology, Harvard Medial School, 240 Longwood Avenue, Boston, MA 02115, USA |
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| Abstract: | Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. |
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