Impact of residue accessible surface area on the prediction of protein secondary structures

Amir Momen-Roknabadi,Sayed-Amir Marashi,Mehdi Sadeghi,Hamid Pezeshk

doi:10.1186/1471-2105-9-357

Abstract

BackgroundThe problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.ResultsWe studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.ConclusionThe success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.

Highlights

The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics
The effect of application of different relative solvent accessibility (RSA) thresholds on the prediction of secondary structures It was previously reported that when a 25% threshold for predicted RSA values is used to classify residues into {B, Ex} classes (i.e. Buried vs. Exposed; see Materials and Methods), this additional information increases the accuracy of SS prediction [31]
We first investigated the effect of adding the actual RSA values, for different RSA thresholds using GOR, Chou-Fasman and HMM (Hidden Markov Method)

Summary

Introduction

The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. The problem of accurate prediction of protein threedimensional structure continues to be one of the challenging problems in Bioinformatics. In order to obtain information about the structure of a novel protein, one may consider simpler tasks, like one dimensional prediction of protein characteristics [6]. Acquiring such information is a key step in understanding the relationship between the protein folding and protein primary structure. The goal of protein secondary structure (SS) prediction methods is to predict whether each residue is in a helical structure (H), a strand (E), or in other structures (traditionally referred to as coil, C)

Methods

Results

Conclusion