Neurocognitive derivation of protein surface property from protein aggregate parameters

Current work targeted to predicate parametric relationship between aggregate and individual property of a protein. In this approach, we considered individual property of a protein as its Surface Roughness Index (SRI) which was shown to have potential to classify SCOP protein families. The bulk property was however considered as Intensity Level based Multi-fractal Dimension (ILMFD) of ordinary microscopic images of heat denatured protein aggregates which was known to have potential to serve as protein marker. The protocol used multiple ILMFD inputs obtained for a protein to produce a set of mapped outputs as possible SRI candidates. The outputs were further clustered and largest cluster centre after normalization was found to be a close approximation of expected SRI that was calculated from known PDB structure. The outcome showed that faster derivation of individual protein’s surface property might be possible using its bulk form, heat denatured aggregates.