Classification of Protein Structure Classes on Flexible Neutral Tree.

Accurate classification on protein structural playing an important role in Bioinformatics. An increasingly evidences demonstrate that a variety of classification methods have been employed in such field. In this research, the features of amino acids composition, secondary structure's feature and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189 and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate the performance of such novel classification model in the low-homology protein tertiary structures.