FILTREST3D: discrimination of structural models using restraints from experimental data

Computational modeling of RNA 3D structure based on experimental data

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

Protein-Protein Docking

Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Structural and functional insights into tRNA binding and adenosine N1-methylation by an archaeal Trm10 homologue

Purine nucleosides on position 9 of eukaryal and archaeal tRNAs are frequently modified in vivo by the post-transcriptional addition of a methyl group on their N1 atom. The methyltransferase Trm10 is responsible for this modification in both these domains of life. While certain Trm10 orthologues specifically methylate either guanosine or adenosine at position 9 of tRNA, others have a dual specificity. Until now structural information about this enzyme family was only available for the catalytic SPOUT domain of Trm10 proteins that show specificity toward guanosine. Here, we present the first crystal structure of a full length Trm10 orthologue specific for adenosine, revealing next to the catalytic SPOUT domain also N- and C-terminal domains. This structure hence provides crucial insights in the tRNA binding mechanism of this unique monomeric family of SPOUT methyltransferases. Moreover, structural comparison of this adenosine-specific Trm10 orthologue with guanosine-specific Trm10 orthologues suggests that the N1 methylation of adenosine relies on additional catalytic residues.

Modeling of Protein-RNA Complex Structures Using Computational Docking Methods

A significant part of biology involves the formation of RNA-protein complexes. X-ray crystallography has added a few solved RNA-protein complexes to the repertoire; however, it remains challenging to capture these complexes and often only the unbound structures are available. This has inspired a growing interest in finding ways to predict these RNA-protein complexes. In this study, we show ways to approach this problem by computational docking methods, either with a fully automated NPDock server or with a workflow of methods for generation of many alternative structures followed by selection of the most likely solution. We show that by introducing experimental information, the structure of the bound complex is rendered far more likely to be within reach. This study is meant to help the user of docking software understand how to grapple with a typical realistic problem in RNA-protein docking, understand what to expect in the way of difficulties, and recognize the current limitations.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Computational modeling of RNA 3D structures, with the aid of experimental restraints

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation ("Greek science" approach) or utilize information derived from known structures of other RNA molecules ("Babylonian science" approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately.

Computational modeling of protein-RNA complex structures

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.

Crystal structures of the tRNA:m2G6 methyltransferase Trm14/TrmN from two domains of life

Methyltransferases (MTases) form a major class of tRNA-modifying enzymes needed for the proper functioning of tRNA. Recently, RNA MTases from the TrmN/Trm14 family that are present in Archaea, Bacteria and Eukaryota have been shown to specifically modify tRNA^Phe at guanosine 6 in the tRNA acceptor stem. Here, we report the first X-ray crystal structures of the tRNA m²G6 (N²-methylguanosine) MTase _TTCTrmN from Thermus thermophilus and its ortholog _PfTrm14 from Pyrococcus furiosus. Structures of _PfTrm14 were solved in complex with the methyl donor S-adenosyl-l-methionine (SAM or AdoMet), as well as the reaction product S-adenosyl-homocysteine (SAH or AdoHcy) and the inhibitor sinefungin. _TTCTrmN and _PfTrm14 consist of an N-terminal THUMP domain fused to a catalytic Rossmann-fold MTase (RFM) domain. These results represent the first crystallographic structure analysis of proteins containing both THUMP and RFM domain, and hence provide further insight in the contribution of the THUMP domain in tRNA recognition and catalysis. Electrostatics and conservation calculations suggest a main tRNA binding surface in a groove between the THUMP domain and the MTase domain. This is further supported by a docking model of TrmN in complex with tRNA^Phe of T. thermophilus and via site-directed mutagenesis.

Databases and bioinformatics tools for the study of DNA repair

DNA is continuously exposed to many different damaging agents such as environmental chemicals, UV light, ionizing radiation, and reactive cellular metabolites. DNA lesions can result in different phenotypical consequences ranging from a number of diseases, including cancer, to cellular malfunction, cell death, or aging. To counteract the deleterious effects of DNA damage, cells have developed various repair systems, including biochemical pathways responsible for the removal of single-strand lesions such as base excision repair (BER) and nucleotide excision repair (NER) or specialized polymerases temporarily taking over lesion-arrested DNA polymerases during the S phase in translesion synthesis (TLS). There are also other mechanisms of DNA repair such as homologous recombination repair (HRR), nonhomologous end-joining repair (NHEJ), or DNA damage response system (DDR). This paper reviews bioinformatics resources specialized in disseminating information about DNA repair pathways, proteins involved in repair mechanisms, damaging agents, and DNA lesions.

RNA tertiary structure prediction with ModeRNA

Noncoding RNAs perform important roles in the cell. As their function is tightly connected with structure, and as experimental methods are time-consuming and expensive, the field of RNA structure prediction is developing rapidly. Here, we present a detailed study on using the ModeRNA software. The tool uses the comparative modeling approach and can be applied when a structural template is available and an alignment of reasonable quality can be performed. We guide the reader through the entire process of modeling Escherichia coli tRNA(Thr) in a conformation corresponding to the complex with an aminoacyl-tRNA synthetase (aaRS). We describe the choice of a template structure, preparation of input files, and explore three possible modeling strategies. In the end, we evaluate the resulting models using six alternative benchmarks. The ModeRNA software can be freely downloaded from http://iimcb.genesilico.pl/moderna/ under the conditions of the General Public License. It runs under LINUX, Windows and Mac OS. It is also available as a server at http://iimcb.genesilico.pl/modernaserver/. The models and the script to reproduce the study from this article are available at http://www.genesilico.pl/moderna/examples/.

DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking

Background

Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.

Results

We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.

Conclusions

In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/

Structural basis for the methylation of A1408 in 16S rRNA by a panaminoglycoside resistance methyltransferase NpmA from a clinical isolate and analysis of the NpmA interactions with the 30S ribosomal subunit

NpmA, a methyltransferase that confers resistance to aminoglycosides was identified in an Escherichia coli clinical isolate. It belongs to the kanamycin–apramycin methyltransferase (Kam) family and specifically methylates the 16S rRNA at the N1 position of A1408. We determined the structures of apo-NpmA and its complexes with S-adenosylmethionine (AdoMet) and S-adenosylhomocysteine (AdoHcy) at 2.4, 2.7 and 1.68 Å, respectively. We generated a number of NpmA variants with alanine substitutions and studied their ability to bind the cofactor, to methylate A1408 in the 30S subunit, and to confer resistance to kanamycin in vivo. Residues D30, W107 and W197 were found to be essential. We have also analyzed the interactions between NpmA and the 30S subunit by footprinting experiments and computational docking. Helices 24, 42 and 44 were found to be the main NpmA-binding site. Both experimental and theoretical analyses suggest that NpmA flips out the target nucleotide A1408 to carry out the methylation. NpmA is plasmid-encoded and can be transferred between pathogenic bacteria; therefore it poses a threat to the successful use of aminoglycosides in clinical practice. The results presented here will assist in the development of specific NpmA inhibitors that could restore the potential of aminoglycoside antibiotics.