The continuing discoveries of non-coding RNAs and their critical roles in cellular and viral machinery are inspiring novel antibacterial, antitumor, nd antiviral therapies based on disrupting or manipulating the RNAs involved. Most of RNA's biological functions depend on the formation of intricate 3D structure and binding to ligands and proteins. Unfortunately, crystallographic models, our richest sources of RNA structural information, contain pervasive errors due to ambiguities in manually fitting RNA backbones into experimental density maps. We have recently brought Rosetta high- resolution RNA structure prediction together with PHENIX diffraction-based refinement and MolProbity validation, to create Enumerative Real-space Refinement ASsisted by Electron density under Rosetta. The ERRASER method corrects the majority of identifiable sugar pucker errors, steric clashes, suspicious backbone rotamers, and incorrect bond lengths/angles in a benchmark of RNA data sets, including a ribosomal subunit. Furthermore, the method, on average, improves Rfree factors to rigorously set- aside data. In this exploratory grant, we first aim to expand ERRASER to resolve ambiguities at RNA/ligand, RNA/protein, and RNA crystal contacts, as will be necessary for correcting RNA enzyme active sites, ligand binding sites, and ribonucleoprotein machines. Second, we aim to make ERRASER available as a fully automated server that will both refine all extant PDB-deposited RNA and ribonucleoprotein models and enable crystallographers to rapidly correct errors in their future data sets. By rapidly and systematicall disambiguating RNA model fitting, ERRASER will enable RNA crystallography with significantly fewer errors.

Public Health Relevance

RNA molecules play fundamental roles in transmitting and regulating genetic information in all living systems, including disease-causing bacteria, retroviruses like HIV, and tumor cells. New potentially life-saving therapies that target these RNAs are being hindered by our imperfect understanding of how RNAs fold into intricate 3D structures. Our work aims to develop a new tool that corrects pervasive mistakes in RNA crystallographic models, which are our richest sources of 3D information.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code
Chou, Fang-Chieh; Echols, Nathaniel; Terwilliger, Thomas C et al. (2016) RNA Structure Refinement Using the ERRASER-Phenix Pipeline. Methods Mol Biol 1320:269-82
Chou, Fang-Chieh; Kladwang, Wipapat; Kappel, Kalli et al. (2016) Blind tests of RNA nearest-neighbor energy prediction. Proc Natl Acad Sci U S A 113:8430-5
Sripakdeevong, Parin; Cevec, Mirko; Chang, Andrew T et al. (2014) Structure determination of noncanonical RNA motifs guided by ¹H NMR chemical shifts. Nat Methods 11:413-6
Chou, Fang-Chieh; Lipfert, Jan; Das, Rhiju (2014) Blind predictions of DNA and RNA tweezers experiments with force and torque. PLoS Comput Biol 10:e1003756
Das, Rhiju (2013) Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz. PLoS One 8:e74830
Adams, Paul D; Baker, David; Brunger, Axel T et al. (2013) Advances, interactions, and future developments in the CNS, Phenix, and Rosetta structural biology software systems. Annu Rev Biophys 42:265-87
Chou, Fang-Chieh; Sripakdeevong, Parin; Dibrov, Sergey M et al. (2013) Correcting pervasive errors in RNA crystallography through enumerative structure prediction. Nat Methods 10:74-6
Lyskov, Sergey; Chou, Fang-Chieh; Conchúir, Shane Ó et al. (2013) Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE). PLoS One 8:e63906