Improving protein secondary structure prediction based on short subsequences with local structure similarity.

ABSTRACT:

Background

When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.

Results

In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that refle

SUBMITTER: Lin HN 

PROVIDER: S-EPMC3005913 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature