AMPLE: a cluster-and-truncate approach to solve the crystal structures of small proteins using rapidly computedab initiomodels

Abstract

Proteinab initiomodels predicted from sequence data alone can enable the elucidation of crystal structures by molecular replacement. However, the calculation of suchab initiomodels is typically computationally expensive. Here, a computational pipeline based on the clustering and truncation of cheaply obtainedab initiomodels for the preparation of structure ensembles is described. Clustering is used to select models and to quantitatively predict their local accuracy, allowing rational truncation of predicted inaccurate regions. The resulting ensembles, with or without rapidly added side chains, solved 43% of all test cases, with an 80% success rate for all-α proteins. A program implementing this approach,AMPLE, is included in theCCP4 suite of programs. It only requires the input of aFASTAsequence file and a diffraction data file. It carries out the modelling using locally installedRosetta, creates search ensembles and automatically performs molecular replacement and model rebuilding.