Abstract
AbstractRecent research works on potential of different protein surface describing parameters to predict protein surface properties gained significance for its possible implication in extracting clues on protein's functional site. In this direction, Surface Roughness Index, a surface topological parameter, showed its potential to predict SCOP-family of protein. The present work stands on the foundation of these works where a semi-empirical method for evaluation of Surface Roughness Index directly from its heat denatured protein aggregates (HDPA) was designed and demonstrated successfully. The steps followed consist, the extraction of a feature, Intensity Level Multifractal Dimension (ILMFD) from the microscopic images of HDPA, followed by the mapping of ILMFD into Surface Roughness Index (SRI) through recurrent backpropagation network (RBPN). Finally SRI for a particular protein was predicted by clustering of decisions obtained through feeding of multiple data into RBPN, to obtain general tendency of decision, as well as to discard the noisy dataset. The cluster centre of the largest cluster was found to be the best match for mapping of Surface Roughness Index of each protein in our study. The semi-empirical approach adopted in this paper, shows a way to evaluate protein's surface property without depending on its already evaluated structure.
Highlights
Structural component of a protein that is responsible for its function is basically localized on its surface
In spite of the great contribution especially of X-ray crystallography and NMR to contribute to the development of other molecular structure exploring methods including ours, where every such methods utilize the molecular structural knowledge gained from them to build the methodology, they have their own pitfalls as constraints like size limit in NMR method[2], requirement of crystal in X-ray crystallography method[3], which pose strong limitation in their applicability for all proteins
The target of the study described in this pilot work is mainly to find out a fast and simple protocol to obtain broadly the structural component of protein and its surface property, surface roughness index (SRI), by systematic incorporation of information generated from simple experiment or experiments
Summary
Structural component of a protein that is responsible for its function is basically localized on its surface. We may expect that knowledge on protein surface may give us the clue on its functional site. First let us discuss the difficulty in obtaining a protein structure both by experimental as well as predictive methods. The folding of a protein results in arrangement of the amino acid residues in specific positions in 3D space which form the functional site of that protein. 3D structure of protein can be derived through X-ray crystallography, NMR and in some cases homology modeling and other prediction methods. Structure prediction methods like homology modeling depend upon the repository of already evaluated structures which are close to target protein with at least 25% sequence similarity through position specific scoring matrix (PSSM). Accuracy of prediction methods including homology model is questionable under further optimization through energy minimization process (for example, the method adopted in Insight-II) which quite often yields minimum energy structure with very low Ramachandran score[5]
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