A geometric invariant-based framework for the analysis of protein conformational space.
BACKGROUND
Characterization of the restricted nature of the protein local conformational space has remained a challenge, thereby necessitating a computationally expensive conformational search in protein modeling. Moreover, owing to the lack of unilateral structural descriptors, conventional data mining techniques, such as clustering and classification, have not been applied in protein structure analysis.


RESULTS
We first map the local conformations in a fixed dimensional space by using a carefully selected suite of geometric invariants (GIs) and then reduce the number of dimensions via principal component analysis (PCA). Distribution of the conformations in the space spanned by the first four PCs is visualized as a set of conditional bivariate probability distribution plots, where the peaks correspond to the preferred conformations. The locations of the different canonical structures in the PC-space have been interpreted in the context of the weights of the GIs to the first four PCs. Clustering of the available conformations reveals that the number of preferred local conformations is several orders of magnitude smaller than that suggested previously.


BACKGROUND
www.it.iitb.ac.in/~ashish/bioinfo2005/.
Tendulkar
Ashish V
AV
Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
Sohoni
Milind A
MA
Ogunnaike
Babatunde
B
Wangikar
Pramod P
PP
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020407:Internet; D008962:Models, Theoretical; D008968:Molecular Conformation; D010455:Peptides / Q000737:chemistry; D025341:Principal Component Analysis; D011336:Probability; D011487:Protein Conformation; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
D010455:Peptides