Estimating local backbone structural deviation in homology models.
After the atomic coordinates themselves, the most important data in a homology model are the spatial reliability estimates associated with each of the atoms (atom annotation). Recent blind homology modeling predictions have demonstrated that principally correct sequence-structure alignments are achievable to sequence identities as low as 25% [Martin, A.C., MacArthur, M.W., Thornton, J.M., 1997. Assessment of comparative modeling in CASP2. Proteins Suppl(1), 14-28]. The locations and extent of spatial deviations in the backbone between correctly aligned homologous protein structures remained very poorly estimated however, and these errors were the cause of errant loop predictions [Abagyan, R., Batalov, S., Cardozo, T., Totrov, M., Webber, J., Zhou, Y., 1997. Homology modeling with internal coordinate mechanics: deformation zone mapping and improvements of models via conformational search. Proteins Suppl(1), 29-37]. In order to derive accurate measures for local backbone deviations, we made a systematic study of static local backbone deviations between homologous pairs of protein structures. We found that 'through space' proximity to gaps and chain termini, local three-dimensional 'density', three-dimensional environment conservation, and B-factor of the template contribute to local deviations in the backbone in addition to local sequence identity. Based on these finding, we have identified the meaningful ranges of values within which each of these parameters correlates with static local backbone deviation and produced a combined scoring function to greatly improve the estimation of local backbone deviations. The optimized function has more than twice the accuracy of local sequence identity or B-factor alone and was validated in a recent blind structure prediction experiment. This method may be used to evaluate the utility of a preliminary homology model for a particular biological investigation (e.g. drug design) or to provide an improved starting point for molecular mechanics loop prediction methods.
Cardozo
T
T
Skirball Institute of Biomolecular Medicine, New York University School of Medicine, NY 10016, USA.
Batalov
S
S
Abagyan
R
R
D003198:Computer Simulation; D018360:Crystallography, X-Ray; D008958:Models, Molecular; D011487:Protein Conformation; D020794:Receptor Protein-Tyrosine Kinases / Q000737:chemistry; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software
D020794:Receptor Protein-Tyrosine Kinases