Method for predicting homology modeling accuracy from amino acid sequence alignment: the power function.

ABSTRACT

We have devised a power function (PF) that can predict the accuracy of a three-dimensional (3D) structure model of a protein using only amino acid sequence alignments. This Power Function (PF) consists of three parts; (1) the length of a model, (2) a homology identity percent value and (3) the agreement rate between PSI-PRED secondary structure prediction and the secondary structure judgment of a reference protein. The PF value is mathematically computed from the execution process of homology search tools, such as FASTA or various BLAST programs, to obtain the amino acid sequence alignments. There is a high correlation between the global distance test-total score (GDT_TS) value of the protein model based upon the PF score and the GDT_TS(MAX) value used as an index of protein modeling accuracy in the international contest Critical Assessment of Techniques for Protein Structure Prediction (CASP). Accordingly, the PF method is valuable for constructing a highly accurate model without wasteful calculations of homology modeling that is normally performed by an iterative method to move the main chain and side chains in the modeling process. Moreover, a model with higher accuracy can be obtained by combining the models ordered by the PF score with models sorted by the size of the CIRCLE score. The CIRCLE software is a 3D-1D program, in which energetic stabilization is estimated based upon the experimental environment of each amino acid residue in the protein solution or protein crystals.