RNABC: forward kinematics to reduce all-atom steric clashes in RNA backbone

Cyclic Automated Model Building (CAB) Applied to Nucleic Acids

Obtaining high-quality models for nucleic acid structures by automated model building programs (AMB) is still a challenge. The main reasons are the rather low resolution of the diffraction data and the large number of rotatable bonds in the main chains. The application of the most popular and documented AMB programs (e.g., PHENIX.AUTOBUILD, NAUTILUS and ARP/wARP) may provide a good assessment of the state of the art. Quite recently, a cyclic automated model building (CAB) package was described; it is a new AMB approach that makes the use of BUCCANEER for protein model building cyclic without modifying its basic algorithms. The applications showed that CAB improves the efficiency of BUCCANEER. The success suggested an extension of CAB to nucleic acids—in particular, to check if cyclically including NAUTILUS in CAB may improve its effectiveness. To accomplish this task, CAB algorithms designed for protein model building were modified to adapt them to the nucleic acid crystallochemistry. CAB was tested using 29 nucleic acids (DNA and RNA fragments). The phase estimates obtained via molecular replacement (MR) techniques were automatically submitted to phase refinement and then used as input for CAB. The experimental results from CAB were compared with those obtained by NAUTILUS, ARP/wARP and PHENIX.AUTOBUILD.

Using Rosetta for RNA homology modeling

The three-dimensional structures of RNA molecules provide rich and often critical information for understanding their functions, including how they recognize small molecule and protein partners. Computational modeling of RNA 3D structure is becoming increasingly accurate, particularly with the availability of growing numbers of template structures already solved experimentally and the development of sequence alignment and 3D modeling tools to take advantage of this database. For several recent "RNA puzzle" blind modeling challenges, we have successfully identified useful template structures and achieved accurate structure predictions through homology modeling tools developed in the Rosetta software suite. We describe our semi-automated methodology here and walk through two illustrative examples: an adenine riboswitch aptamer, modeled from a template guanine riboswitch structure, and a SAM I/IV riboswitch aptamer, modeled from a template SAM I riboswitch structure.

De novo computational RNA modeling into cryo-EM maps of large ribonucleoprotein complexes

Increasingly, cryo-electron microscopy (cryo-EM) is used to determine the structures of RNA-protein assemblies, but nearly all maps determined with this method have biologically important regions where the local resolution does not permit RNA coordinate tracing. To address these omissions, we present de novo ribonucleoprotein modeling in real space through assembly of fragments together with experimental density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of RNA-protein complexes including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes; rigorous blind tests include yeast U1 snRNP and spliceosomal P complex maps. Additionally, to aid in model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply DRRAFTER to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating the acceleration of accurate model building in challenging cases.

Progress and Current Challenges in Modeling Large RNAs

Recent breakthroughs in next-generation sequencing technologies have led to the discovery of several classes of non-coding RNAs (ncRNAs). It is now apparent that RNA molecules are not only just carriers of genetic information but also key players in many cellular processes. While there has been a rapid increase in the number of ncRNA sequences deposited in various databases over the past decade, the biological functions of these ncRNAs are largely not well understood. Similar to proteins, RNA molecules carry out a function by forming specific three-dimensional structures. Understanding the function of a particular RNA therefore requires a detailed knowledge of its structure. However, determining experimental structures of RNA is extremely challenging. In fact, RNA-only structures represent just 1% of the total structures deposited in the PDB. Thus, computational methods that predict three-dimensional RNA structures are in high demand. Computational models can provide valuable insights into structure-function relationships in ncRNAs and can aid in the development of functional hypotheses and experimental designs. In recent years, a set of diverse RNA structure prediction tools have become available, which differ in computational time, input data and accuracy. This review discusses the recent progress and challenges in RNA structure prediction methods.

Structures and Energetics of Four Adjacent G·U Pairs That Stabilize an RNA Helix

Consecutive G·U base pairs inside RNA helices can be destabilizing, while those at the ends of helices are thermodynamically stabilizing. To determine if this paradox could be explained by differences in base stacking, we determined the high-resolution (1.32 Å) crystal structure of (5'-GGUGGCUGUU-3')2 and studied three sequences with four consecutive terminal G·U pairs by NMR spectroscopy. In the crystal structure of (5'-GGUGGCUGUU-3')2, the helix is overwound but retains the overall features of A-form RNA. The penultimate base steps at each end of the helix have high base overlap and contribute to the unexpectedly favorable energetic contribution for the 5'-GU-3'/3'-UG-5' motif in this helix position. The balance of base stacking and helical twist contributes to the positional dependence of G·U pair stabilities. The energetic stabilities and similarity to A-form RNA helices suggest that consecutive G·U pairs would be recognized by RNA helix binding proteins, such as Dicer and Ago. Thus, these results will aid future searches for target sites of small RNAs in gene regulation.

Fusion RNAs in crystallographic studies of double-stranded RNA from trypanosome RNA editing

Head-to-head fusions of two identical double-stranded fragments of RNA can be designed to self-assemble from a single RNA species and form a double-stranded helix with a twofold rotation axis relating the two strands. These symmetrical RNA molecules are more likely to crystallize without end-on-end statistical packing disorder because the two halves of the molecule are identical. This approach can be used to study many fragments of double-stranded RNA or many isolated helical domains from large single-stranded RNAs that may not yet be amenable to high-resolution studies by crystallography or NMR. We used fusion RNAs to study one (the U-helix) of three functional domains formed when guide RNA binds to its cognate pre-edited mRNA from the trypanosome RNA editing system. The U-helix forms when the 3' oligo(U) tail of the guide RNA (gRNA) binds to the purine-rich, pre-edited mRNA upstream from the current RNA editing site. Fusion RNAs 16-and 32-base pairs in length formed crystals that gave diffraction to 1.37 and 1.05 Å respectively. We provide the composition of a fusion RNA crystallization screen and describe the X-ray data collection, structure determination, and refinement of the crystal structures of fusion RNAs.

Computational Methods for RNA Structure Validation and Improvement

With increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.

New tools provide a second look at HDV ribozyme structure, dynamics and cleavage

The hepatitis delta virus (HDV) ribozyme is a self-cleaving RNA enzyme essential for processing viral transcripts during rolling circle viral replication. The first crystal structure of the cleaved ribozyme was solved in 1998, followed by structures of uncleaved, mutant-inhibited and ion-complexed forms. Recently, methods have been developed that make the task of modeling RNA structure and dynamics significantly easier and more reliable. We have used ERRASER and PHENIX to rebuild and re-refine the cleaved and cis-acting C75U-inhibited structures of the HDV ribozyme. The results correct local conformations and identify alternates for RNA residues, many in functionally important regions, leading to improved R values and model validation statistics for both structures. We compare the rebuilt structures to a higher resolution, trans-acting deoxy-inhibited structure of the ribozyme, and conclude that although both inhibited structures are consistent with the currently accepted hammerhead-like mechanism of cleavage, they do not add direct structural evidence to the biochemical and modeling data. However, the rebuilt structures (PDBs: 4PR6, 4PRF) provide a more robust starting point for research on the dynamics and catalytic mechanism of the HDV ribozyme and demonstrate the power of new techniques to make significant improvements in RNA structures that impact biologically relevant conclusions.

New model-fitting and model-completion programs for automated iterative nucleic acid refinement

In the past decade many structures of nucleic acids have been determined, which have contributed to our understanding of their biological functions. However, crystals containing nucleic acids often diffract X-rays poorly. This makes electron-density interpretation difficult and requires a great deal of expertise in crystallography and knowledge of nucleic acid structure. Here, new programs called NAFIT and NABUILD for fitting and extending nucleic acid models are presented. These programs can be used as modules in the automated refinement system LAFIRE, as well as acting as independent programs. NAFIT performs sequential grouped fitting with empirical torsion-angle restraints and antibumping restraints including H atoms. NABUILD extends the model using a skeletonized map in a coarse-grained manner. It has been shown that NAFIT greatly improves electron-density fit and geometric quality and that iterative refinement with NABUILD significantly reduces the Rfree factor.

Doing molecular biophysics: finding, naming, and picturing signal within complexity

A macromolecular structure, as measured data or as a list of coordinates or even on-screen as a full atomic model, is an extremely complex and confusing object. The underlying rules of how it folds, moves, and interacts as a biological entity are even less evident or intuitive to the human mind. To do science on such molecules, or to relate them usefully to higher levels of biology, we need to start with a natural history that names their features in meaningful ways and with multiple representations (visual or algebraic) that show some aspect of their organizing principles. The two of us have jointly enjoyed a highly varied and engrossing career in biophysical research over nearly 50 years. Our frequent changes of emphasis are tied together by two threads: first, by finding the right names, visualizations, and methods to help both ourselves and others to better understand the 3D structures of protein and RNA molecules, and second, by redefining the boundary between signal and noise for complex data, in both directions-sometimes identifying and promoting real signal up out of what seemed just noise, and sometimes demoting apparent signal into noise or systematic error. Here we relate parts of our scientific and personal lives, including ups and downs, influences, anecdotes, and guiding principles such as the title theme.

Correcting pervasive errors in RNA crystallography through enumerative structure prediction

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average R(free) factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models.

Studying and polishing the PDB's macromolecules

Macromolecular crystal structures are among the best of scientific data, providing detailed insight into these complex and biologically important molecules with a relatively low level of error and subjectivity. However, there are two notable problems with getting the most information from them. The first is that the models are not perfect: there is still opportunity for improving them, and users need to evaluate whether the local reliability in a structure is up to answering their question of interest. The second is that protein and nucleic acid molecules are highly complex and individual, inherently handed and three-dimensional, and the cooperative and subtle interactions that govern their detailed structure and function are not intuitively evident. Thus there is a real need for graphical representations and descriptive classifications that enable molecular 3D literacy. We have spent our career working to understand these elegant molecules ourselves, and building tools to help us and others determine and understand them better. The Protein Data Bank (PDB) has of course been vital and central to this undertaking. Here we combine some history of our involvement as depositors, illustrators, evaluators, and end-users of PDB structures with commentary on how best to study and draw scientific inferences from them.

RCrane: semi-automated RNA model building

RCrane is a new tool for the partially automated building of RNA crystallographic models into electron-density maps of low or intermediate resolution. This tool 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.

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.

The crystal structure of an oligo(U):pre-mRNA duplex from a trypanosome RNA editing substrate

Guide RNAs bind antiparallel to their target pre-mRNAs to form editing substrates in reaction cycles that insert or delete uridylates (Us) in most mitochondrial transcripts of trypanosomes. The 5' end of each guide RNA has an anchor sequence that binds to the pre-mRNA by base-pair complementarity. The template sequence in the middle of the guide RNA directs the editing reactions. The 3' ends of most guide RNAs have ∼15 contiguous Us that bind to the purine-rich unedited pre-mRNA upstream of the editing site. The resulting U-helix is rich in G·U wobble base pairs. To gain insights into the structure of the U-helix, we crystallized 8 bp of the U-helix in one editing substrate for the A6 mRNA of Trypanosoma brucei. The fragment provides three samples of the 5'-AGA-3'/5'-UUU-3' base-pair triple. The fusion of two identical U-helices head-to-head promoted crystallization. We obtained X-ray diffraction data with a resolution limit of 1.37 Å. The U-helix had low and high twist angles before and after each G·U wobble base pair; this variation was partly due to shearing of the wobble base pairs as revealed in comparisons with a crystal structure of a 16-nt RNA with all Watson-Crick base pairs. Both crystal structures had wider major grooves at the junction between the poly(U) and polypurine tracts. This junction mimics the junction between the template helix and the U-helix in RNA-editing substrates and may be a site of major groove invasion by RNA editing proteins.

Predicting the Flexibility Profile of Ribosomal RNAs

Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye-Waller factor or B-factor. Although numerous works have been addressed on theoretical and computational studies of the B-factor profiles of proteins, the methods used for predicting B-factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure-flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B-factor based on primary sequences (sequence-based) and advanced structures (structure-based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B-factors, we demonstrate that (i) rRNA B-factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure-based approach performed much better as compared to sequence-based methods in modeling of rRNA B-factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

Semiautomated model building for RNA crystallography using a directed rotameric approach

Structured RNA molecules play essential roles in a variety of cellular processes; however, crystallographic studies of such RNA molecules present a large number of challenges. One notable complication arises from the low resolutions typical of RNA crystallography, which results in electron density maps that are imprecise and difficult to interpret. This problem is exacerbated by the lack of computational tools for RNA modeling, as many of the techniques commonly used in protein crystallography have no equivalents for RNA structure. This leads to difficulty and errors in the model building process, particularly in modeling of the RNA backbone, which is highly error prone due to the large number of variable torsion angles per nucleotide. To address this, we have developed a method for accurately building the RNA backbone into maps of intermediate or low resolution. This method is semiautomated, as it requires a crystallographer to first locate phosphates and bases in the electron density map. After this initial trace of the molecule, however, an accurate backbone structure can be built without further user intervention. To accomplish this, backbone conformers are first predicted using RNA pseudotorsions and the base-phosphate perpendicular distance. Detailed backbone coordinates are then calculated to conform both to the predicted conformer and to the previously located phosphates and bases. This technique is shown to produce accurate backbone structure even when starting from imprecise phosphate and base coordinates. A program implementing this methodology is currently available, and a plugin for the Coot model building program is under development.

PHENIX: a comprehensive Python-based system for macromolecular structure solution

The PHENIX software for macromolecular structure determination is described.

Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. How­ever, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallo­graphic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.

MolProbity: all-atom structure validation for macromolecular crystallography

MolProbity is a structure-validation web service that provides broad-spectrum solidly based evaluation of model quality at both the global and local levels for both proteins and nucleic acids. It relies heavily on the power and sensitivity provided by optimized hydrogen placement and all-atom contact analysis, complemented by updated versions of covalent-geometry and torsion-angle criteria. Some of the local corrections can be performed automatically in MolProbity and all of the diagnostics are presented in chart and graphical forms that help guide manual rebuilding. X-ray crystallography provides a wealth of biologically important molecular data in the form of atomic three-dimensional structures of proteins, nucleic acids and increasingly large complexes in multiple forms and states. Advances in automation, in everything from crystallization to data collection to phasing to model building to refinement, have made solving a structure using crystallography easier than ever. However, despite these improvements, local errors that can affect biological interpretation are widespread at low resolution and even high-resolution structures nearly all contain at least a few local errors such as Ramachandran outliers, flipped branched protein side chains and incorrect sugar puckers. It is critical both for the crystallographer and for the end user that there are easy and reliable methods to diagnose and correct these sorts of errors in structures. MolProbity is the authors' contribution to helping solve this problem and this article reviews its general capabilities, reports on recent enhancements and usage, and presents evidence that the resulting improvements are now beneficially affecting the global database.