Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation.

ABSTRACT:

Aim and objective

The rapid increase in the amount of protein sequence data available leads to an urgent need for novel computational algorithms to analyze and compare these sequences. This study is undertaken to develop an efficient computational approach for timely encoding protein sequences and extracting the hidden information.

Methods

Based on two physicochemical properties of amino acids, a protein primary sequence was converted into a three-letter sequence, and then a graph without loops and multiple edges and its geometric line adjacency matrix were obtained. A generalized PseAAC (pseudo amino acid composition) model was thus constructed to characterize a protein sequence numerically.

Results

By using the proposed mathematical descriptor of a protein sequence,

SUBMITTER: Li C 

PROVIDER: S-EPMC5930480 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature