Computational Methods for Understanding Riboswitches (eBook)

Donald H. Burke-Aguero, Shi-Jie Chen (Herausgeber)

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2015 | 1. Auflage
422 Seiten
Elsevier Science (Verlag)
978-0-12-801618-3 (ISBN)

Infos

This new volume of Methods in Enzymology continues the legacy of this premier serial with quality chapters authored by leaders in the field. This volume covers computational prediction RNA structure and dynamics, including such topics as computational modeling of RNA secondary and tertiary structures, riboswitch dynamics, and ion-RNA, ligand-RNA and DNA-RNA interactions. - Continues the legacy of this premier serial with quality chapters authored by leaders in the field - Covers computational methods and applications in RNA structure and dynamics - Contains chapters with emerging topics such as RNA structure prediction, riboswitch dynamics and thermodynamics, and effects of ions and ligands.

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Chapter Two

Modeling Complex RNA Tertiary Folds with Rosetta

Clarence Yu Cheng*; Fang-Chieh Chou*; Rhiju Das*,†,1    * Department of Biochemistry, Stanford University, Stanford, California, USA
† Department of Physics, Stanford University, Stanford, California, USA
1 Corresponding author: email address: [e-mail]

Abstract

Reliable modeling of RNA tertiary structures is key to both understanding these structures’ roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100–300 nucleotide size range at consistent subhelical (~ 1 nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

Keywords

Blind prediction

Chemical mapping

Structure mapping

Fragment assembly

RNA tertiary structure

1 Introduction

Computational modeling of RNA structures is advancing rapidly, with recent developments improving prediction and design of both secondary and tertiary structures of RNA. Continuing improvements to secondary structure prediction algorithms (Tinoco et al., 1973), classification of RNA structural motifs (Petrov, Zirbel, & Leontis, 2013), molecular dynamics and quantum mechanical techniques (Ditzler, Otyepka, Sponer, & Walter, 2010), conformational sampling with energy scoring (Das, Karanicolas, & Baker, 2010), atomic-scale loop and motif modeling (Sripakdeevong, Kladwang, & Das, 2011), integration with conventional crystallographic (Chou, Sripakdeevong, Dibrov, Hermann, & Das, 2013) and NMR approaches (Sripakdeevong et al., 2014), and connections with recent single-molecule (Chou, Lipfert, & Das, 2014) and internet-scale videogame (Lee et al., 2014) technologies hold promise for eventually attaining confident 3D modeling and design of RNAs with high spatial resolution. An important driver of recent innovation has been the establishment of blind prediction trials, proposed during a community-wide collation of 3D RNA modeling methods in 2010 (Sripakdeevong, Beauchamp, & Das, 2012) and begun soon thereafter. The RNA-Puzzles trials (Cruz et al., 2012), modeled after the 20-year-old CASP trials in protein structure prediction, challenge participating groups to create accurate 3D models of RNAs from sequence alone; the submitted models are compared to unreleased crystallographic structures of the targets to assess the methods’ predictive power. These trials provide a rigorous testing ground for current computational as well as hybrid experimental/computational structure prediction methods on RNA domains that are of strong biological interest.

This chapter describes methods from our laboratory of medium computational and experimental expense that achieve subhelix-resolution accuracy for 3D models of 100- to 300-nucleotide RNAs, a typical size range for many riboswitch and ribozyme domains and representative of RNA-Puzzles target sizes. Subhelical resolution, while not the ultimate achievable, has still been useful in guiding mutational experiments in vitro and in vivo, detecting partial structure in riboswitches without their ligands, and in revealing or illustrating evolutionary connections that are not obvious from sequence comparisons alone. The primary tools for this approach are constraints from chemical mapping experiments, which we discuss briefly here and will be described in more detail elsewhere, and computational modeling to integrate chemical mapping data into 3D portraits.

Our laboratory is developing several tools that seek to advance 3D macromolecule modeling at multiple length scales. For small RNA motifs, we leverage algorithms based on a “stepwise ansatz,” which enable modeling of RNA loops and motifs with near-atomic accuracy (better than 2 Å RMSD), particularly if limited NMR or crystallographic data are available (Chou et al., 2013; Sripakdeevong et al., 2014, 2011). Unfortunately, the computational expense of those high-resolution tools is currently prohibitive for de novo modeling of large RNAs. Instead, our practical tools for large RNAs have largely been built on Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) in the Rosetta framework, which was first introduced to model small motifs of RNAs in 2007 (Das & Baker, 2007) and was initially based on Rosetta protein structure prediction methods that we had helped in advance. Since that time, FARFAR has been progressively developed to allow for nucleotide-resolution building of not just individual RNA motifs but also more complex RNA folds involving dozens of helices. This chapter is intended to offer a practical guide to getting started with Rosetta using an up-to-date workflow from our laboratory, laid out in Fig. 1. We will illustrate this workflow below using the ligand-binding region of a tandem glycine-binding riboswitch from F. nucleatum, which forms a complex pseudosymmetric fold stabilized by A-minor interactions between two glycine-binding subdomains. A homolog of this domain was posed as an RNA-Puzzles challenge (Cruz et al., 2012), and crystallographic and biochemical work on this system by several RNA laboratories (Butler, Xiong, Wang, & Strobel, 2011; Cordero, Kladwang, VanLang, & Das, 2012; Erion & Strobel, 2011; Kladwang, VanLang, Cordero, & Das, 2011) have made this RNA a useful model system for calibrating and illustrating experimental and computational methodologies.

Figure 1 Workflow for modeling RNA structures in the Rosetta framework guided by experimental data. One-dimensional chemical mapping and mutate-and-map methods guide confident secondary structure prediction. To save computational expense during global modeling, secondary structure elements are separately preassembled. These ensembles of preassembled helices, along with experimental proximity mapping data from MOHCA-seq, are the inputs to global modeling by Fragment Assembly of RNA (FARNA), which generates low-resolution models. A fraction of the low-resolution models with the lowest Rosetta energy scores are then minimized using the Rosetta all-atom energy function (FARNA with Full-Atom Refinement, FARFAR) to resolve chainbreaks and unreasonable local geometries that can arise from fragment insertion. Finally, the minimized models are clustered using an RMSD threshold to collect 0.5% of the total low-resolution models in the largest cluster; this step identifies representative conformations sampled by the algorithm.

2 Setting the Stage for 3D Modeling Using Experimental Data

Several pieces of information can provide powerful constraints to help construct accurate 3D models of RNA. The most fundamental of these is the RNA's secondary structure. If phylogenetic inference of secondary structure is precluded by the lack of sequence homologs, difficulties in sequence alignment, or targeting of “alternative” states of the RNA (e.g., without ligands or in misfolded conformations), chemical mapping techniques provide useful guides to computational secondary structure prediction (Cordero, Kladwang, VanLang, & Das, 2014; Hajdin et al., 2013; Kladwang et al., 2011). In traditional “one-dimensional” (1D) chemical mapping experiments, solution-state RNAs are exposed to chemical modifiers which form adducts to the backbone or nucleobases depending on backbone flexibility or base-pairing status (Fig. 2A). These modifications are traditionally detected by reverse transcription, which stops at the modified location, followed by gel or capillary electrophoresis or, more recently, deep sequencing to identify the sequence position of each modification. The reactivity of each nucleotide position to the chemical modifier can be quantified using several publically available software suites, with HiTRACE (Kim, Cordero, Das, & Yoon, 2013; Yoon et al., 2011) (https://github.com/hitrace/hitrace) and MAPseeker (Seetin et al., 2014) (https://github.com/DasLab/map_seeker) particularly optimized for high-throughput analysis of capillary electrophoresis and deep-sequencing data, respectively. Secondary structure prediction servers such as RNAstructure (Reuter & Mathews, 2010) (http://rna.urmc.rochester.edu/RNAstructureWeb) and the RNA mapping database structure server (Cordero, Lucks, & Das, 2012)...

Produkt-Details

• Erscheint lt. Verlag: 24.2.2015
• Sprache: englisch
• Themenwelt: Studium ► 2. Studienabschnitt (Klinik) ► Humangenetik
• Themenwelt: Naturwissenschaften ► Biologie ► Biochemie
• Themenwelt: Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Zellbiologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Angewandte Physik
• ISBN-10: 0-12-801618-3 / 0128016183
• ISBN-13: 978-0-12-801618-3 / 9780128016183