Protein structure prediction using a variety of profile libraries and 3D verification.

Abstract

This study is intended to construct a useful method for fold recognition, regardless of whether the proteins to be compared are evolutionarily related. We developed several descendants of our profile-profile comparison method to make use of known structural information for protein structure prediction. Our prediction strategy in CASP6 is simple. For every CASP6 target, we derived target-template alignments from several different versions of profile-profile comparisons. We then constructed and exhaustively evaluated 3D models based on those alignments. Subsequently, we selected proper model(s) among them. We specifically addressed the validation of our simple approach for protein structure prediction through CASP6 because the fold recognition results of CASP5 revealed areas of improvement in the selection of good models. Consequently, we applied a more stringent method for 3D model evaluation this time. All generated models were evaluated based on a structural quality score calculated by both Verify3D and Prosa2003 programs. It turns out that the prediction results of our human group were supported by the results of three servers. The pipeline that we constructed for our human group prediction and human intervention were also greatly effective in improving prediction models, but the efficacy of our scheme for 3D model evaluation was obscure.