Error-estimation-guided rebuilding ofde novomodels increases the success rate ofab initiophasing

Abstract

Recent advancements in computational methods for protein-structure prediction have made it possible to generate the high-quality de novo models required for ab initio phasing of crystallographic diffraction data using molecular replacement. Despite those encouraging achievements in ab initio phasing using de novo models, its success is limited only to those targets for which high-quality de novo models can be generated. In order to increase the scope of targets to which ab initio phasing with de novo models can be successfully applied, it is necessary to reduce the errors in the de novo models that are used as templates for molecular replacement. Here, an approach is introduced that can identify and rebuild the residues with larger errors, which subsequently reduces the overall C(α) root-mean-square deviation (CA-RMSD) from the native protein structure. The error in a predicted model is estimated from the average pairwise geometric distance per residue computed among selected lowest energy coarse-grained models. This score is subsequently employed to guide a rebuilding process that focuses on more error-prone residues in the coarse-grained models. This rebuilding methodology has been tested on ten protein targets that were unsuccessful using previous methods. The average CA-RMSD of the coarse-grained models was improved from 4.93 to 4.06 Å. For those models with CA-RMSD less than 3.0 Å, the average CA-RMSD was improved from 3.38 to 2.60 Å. These rebuilt coarse-grained models were then converted into all-atom models and refined to produce improved de novo models for molecular replacement. Seven diffraction data sets were successfully phased using rebuilt de novo models, indicating the improved quality of these rebuilt de novo models and the effectiveness of the rebuilding process. Software implementing this method, called MORPHEUS, can be downloaded from http://www.riken.jp/zhangiru/software.html.