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

We 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.The 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.The problem of accurate prediction of protein three-dimensional structure continues to be one of the challenging problems in Bioinformatics. The large-scale genome sequencing efforts have made this problem even more significant. Roughly 50% of the proteins in a genome have at least one homolog in protein structure databases and their structure can be predicted efficiently by homology modeling [1,2]. However, for the other half of the sequences no structural template is currently known. To date, the performance of ab initio three dimensional prediction methods are still far from being perfect [3-5]. Therefore, 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 relation