RCrane: semi-automated RNA model building

Kevin S Keating,Anna Marie Pyle

doi:10.1107/s0907444912018549

Abstract

RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems.

Highlights

In recent years, RNA crystal structures have contributed greatly to the understanding of numerous cellular processes (Ban et al, 2000; Batey et al, 2004; Selmer et al, 2006)
RCrane works within Coot, a common program for macromolecular model building (Emsley et al, 2010), and is designed to be intuitively usable by crystallographers familiar with the Coot interface
To describe how RCrane fits into this procedure, we present here a general workflow for crystallographic RNA model building

Summary

Introduction

RNA crystal structures have contributed greatly to the understanding of numerous cellular processes (Ban et al, 2000; Batey et al, 2004; Selmer et al, 2006) These structural studies are complicated by the fact that RNA crystals normally diffract to lower resolutions than protein crystals (Keating & Pyle, 2010). Numerous tools exist for automated and partially automated model building (Cowtan, 2006; Langer et al, 2008; Terwilliger, 2003); computational tools for RNA crystallography are only beginning to emerge These tools aid in detecting (Chen et al, 2010) and correcting (Wang et al, 2008) errors in crystallographic models, interpreting electron density (Gruene & Sheldrick, 2011) and classifying specific substructures (Sarver et al, 2008; Wadley et al, 2007), but few tools are available to aid in constructing the initial crystallographic model (Hattne & Lamzin, 2008). The prediction and building protocols are based on techniques that have previously been shown to produce highly accurate structures (Keating & Pyle, 2010), but doi:10.1107/S0907444912018549 985 research papers the computer-assisted phosphate- and base-placement algorithms are newly developed and thoroughly tested below

Methods

Results

Conclusion