Quality assessment methods for 3D protein structure models based on a residue-residue distance matrix prediction.

ABSTRACT

In the absence of experimentally determined three dimensional (3D) structures of proteins, the prediction of protein structures using computational methods is a standard alternative approach in bioinformatics. When using the predicted protein models to compute the native structure of an unknown target protein, estimating the actual quality of the protein models is important for selecting the best or near-best model. Moreover, estimates of the differences between the protein models and the native protein structure are obviously useful to end users who can then decide on the utility of the models for their specific problems. This article describes two new single-model quality assessment (QA) programs, pure single-model QA method (psQA) and a template based QA method (tbQA), that we developed. psQA is a pure single-model QA program that uses a neural network method to predict residue-residue distance matrices of the native protein structures. tbQA is a quasi-single-model QA program that mainly uses target-template sequence alignments and template structures. The performance of these two model QA programs was analyzed in a data set of 24022 models for 94 targets from the 10th critical assessment of protein structure prediction (CASP10) experiment.