scholarly journals The Annotation of RNA Motifs

2002 ◽  
Vol 3 (6) ◽  
pp. 518-524 ◽  
Author(s):  
Neocles B. Leontis ◽  
Eric Westhof
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Three Dimensional ◽  
Global Structure ◽  
Hierarchical Organization ◽  
Dimensional Structure ◽  
Base Pairs ◽  
Rna Motifs ◽  
Rna Sequences ◽  
Ordered Array

The recent deluge of new RNA structures, including complete atomic-resolution views of both subunits of the ribosome, has on the one hand literally overwhelmed our individual abilities to comprehend the diversity of RNA structure, and on the other hand presented us with new opportunities for comprehensive use of RNA sequences for comparative genetic, evolutionary and phylogenetic studies. Two concepts are key to understanding RNA structure: hierarchical organization of global structure and isostericity of local interactions. Global structure changes extremely slowly, as it relies on conserved long-range tertiary interactions. Tertiary RNA–RNA and quaternary RNA–protein interactions are mediated by RNA motifs, defined as recurrent and ordered arrays of non-Watson–Crick base-pairs. A single RNA motif comprises a family of sequences, all of which can fold into the same three-dimensional structure and can mediate the same interaction(s). The chemistry and geometry of base pairing constrain the evolution of motifs in such a way that random mutations that occur within motifs are accepted or rejected insofar as they can mediate a similar ordered array of interactions. The steps involved in the analysis and annotation of RNA motifs in 3D structures are: (a) decomposition of each motif into non-Watson–Crick base-pairs; (b) geometric classification of each basepair; (c) identification of isosteric substitutions for each basepair by comparison to isostericity matrices; (d) alignment of homologous sequences using the isostericity matrices to identify corresponding positions in the crystal structure; (e) acceptance or rejection of the null hypothesis that the motif is conserved.

scholarly journals Aligning biological sequences by exploiting residue conservation and coevolution

2020 ◽  
Author(s):  
Anna Paola Muntoni ◽  
Andrea Pagnani ◽  
Martin Weigt ◽  
Francesco Zamponi
Keyword(s):  
Protein Interactions ◽  
Message Passing ◽  
Statistical Physics ◽  
Three Dimensional ◽  
Simulated Data ◽  
Structure And Function ◽  
Dimensional Structure ◽  
Biological Sequences ◽  
Rna Sequences ◽  
And Function

Aligning biological sequences belongs to the most important problems in computational sequence analysis; it allows for detecting evolutionary relationships between sequences and for predicting biomolecular structure and function. Typically this is addressed through profile models, which capture position-specificities like conservation in sequences, but assume an independent evolution of different positions. RNA sequences are an exception where the coevolution of paired bases in the secondary structure is taken into account. Over the last years, it has been well established that coevolution is essential also in proteins for maintaining three-dimensional structure and function; modeling approaches based on inverse statistical physics can catch the coevolution signal and are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient approach based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include general second-order interactions among positions and to be therefore universally applicable to protein- and RNA-sequence alignment. The potential of our algorithm is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.

scholarly journals Evaluating DCA-based method performances for RNA contact prediction by a well-curated dataset

2019 ◽  
Author(s):  
F. Pucci ◽  
M. Zerihun ◽  
E. Peter ◽  
A. Schug
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Data ◽  
Three Dimensional ◽  
Spatial Proximity ◽  
Dimensional Structure ◽  
Rna Sequences ◽  
Contact Prediction ◽  
Rna Molecules ◽  
Quality Of Structure

AbstractRNA molecules play many pivotal roles in the cellular functioning that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analysed by methods such as Direct Coupling Analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated dataset of about seventy RNA structures with high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor difference between the performances of the different methods. Moreover, we discuss how these predictions are robust for different contact definitions and how strongly depend on procedures used to curate and align the families of homologous RNA sequences.

Assembly states of the nucleosome assembly protein 1 (NAP-1) revealed by sedimentation velocity and non-denaturing MS

2011 ◽  
Vol 436 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Masanori Noda ◽  
Susumu Uchiyama ◽  
Adam R. McKay ◽  
Akihiro Morimoto ◽  
Shigeki Misawa ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Analytical Ultracentrifugation ◽  
Three Dimensional ◽  
Sedimentation Velocity ◽  
Dimensional Structure ◽  
Histone H2a ◽  
Nucleosome Assembly ◽  
Nucleosome Assembly Protein ◽  
Nucleosome Assembly Protein 1

Proteins often exist as ensembles of interconverting states in solution which are often difficult to quantify. In the present manuscript we show that the combination of MS under nondenaturing conditions and AUC-SV (analytical ultracentrifugation sedimentation velocity) unambiguously clarifies a distribution of states and hydrodynamic shapes of assembled oligomers for the NAP-1 (nucleosome assembly protein 1). MS established the number of associated units, which was utilized as input for the numerical analysis of AUC-SV profiles. The AUC-SV analysis revealed that less than 1% of NAP-1 monomer exists at the micromolar concentration range and that the basic assembly unit consists of dimers of yeast or human NAP-1. These dimers interact non-covalently to form even-numbered higher-assembly states, such as tetramers, hexamers, octamers and decamers. MS and AUC-SV consistently showed that the formation of the higher oligomers was suppressed with increasing ionic strength, implicating electrostatic interactions in the formation of higher oligomers. The hydrodynamic shapes of the NAP-1 tetramer estimated from AUC-SV agreed with the previously proposed assembly models built using the known three-dimensional structure of yeast NAP-1. Those of the hexamer and octamer could be represented by new models shown in the present study. Additionally, MS was used to measure the stoichiometry of the interaction between the human NAP-1 dimer and the histone H2A–H2B dimer or H3–H4 tetramer. The present study illustrates a rigorous procedure for the analysis of protein assembly and protein–protein interactions in solution.

scholarly journals Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein–protein interactions

2019 ◽  
Vol 47 (W1) ◽  
pp. W331-W337 ◽  
Author(s):  
Ankit A Roy ◽  
Abhilesh S Dhawanjewar ◽  
Parichit Sharma ◽  
Gulzar Singh ◽  
M S Madhusudhan
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Optimal Number ◽  
Dimensional Structure ◽  
Side Chain ◽  
Interface Residue ◽  
Z Score ◽  
Protein Protein Interactions ◽  
Residue Contacts

Abstract Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein–protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.

Three-dimensional structure of a regular bacterial surface layer: The HPI-layer of Deinococcus radiodurans

1983 ◽  
Vol 41 ◽  
pp. 438-439
Author(s):  
W. Baumeister ◽  
M. Hahn ◽  
W.O. Saxton
Keyword(s):  
Outer Membrane ◽  
Protein Interactions ◽  
Deinococcus Radiodurans ◽  
Bacterial Species ◽  
Hexagonal Lattice ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Protein Protein Interactions ◽  
Bacterial Surface ◽  
Freeze Dried

Regularly organized surface (RS) layers are a feature common to many bacterial species; they are clearly more abundant than was anticipated even a few years ago. The RS-layers are believed to fulfil a variety of functions in the interaction between the cell and its environment (see e.g. [1]). The so-called HPI-layer of the radiotolerant bacterium Deinococcus radiodurans is a typical example of such a layer: It is composed of a single polypeptide species (Mr 105 kDa) arranged on a hexagonal lattice to form a network that covers the entire surface of the bacterium; it is associated with the outer membrane via hydrophobic protein-protein interactions.Isolated HPI-layer sheets, released from the outer membrane by detergent treatment, have been studied in the electron microscope making extensive use of the present arsenal of preparation techniques: negative staining, (auro- thio)glucose embedding, freeze-dried/unstained, freeze-dried/metal shadowed etc.Because of the notorious problem of lattice imperfections image processing usually followed the strategy of correlation averaging as outlined in some detail elsewhere.

scholarly journals Mitochondrial Arabidopsis thaliana TRXo Isoforms Bind an Iron–Sulfur Cluster and Reduce NFU Proteins In Vitro

Antioxidants ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 142 ◽  
Author(s):  
Flavien Zannini ◽  
Thomas Roret ◽  
Jonathan Przybyla-Toscano ◽  
Tiphaine Dhalleine ◽  
Nicolas Rouhier ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Protein Interactions ◽  
Function Analysis ◽  
Active Form ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Regulation Mechanism ◽  
Iron Sulfur Cluster ◽  
Iron Sulfur

In plants, the mitochondrial thioredoxin (TRX) system generally comprises only one or two isoforms belonging to the TRX h or o classes, being less well developed compared to the numerous isoforms found in chloroplasts. Unlike most other plant species, Arabidopsis thaliana possesses two TRXo isoforms whose physiological functions remain unclear. Here, we performed a structure–function analysis to unravel the respective properties of the duplicated TRXo1 and TRXo2 isoforms. Surprisingly, when expressed in Escherichia coli, both recombinant proteins existed in an apo-monomeric form and in a homodimeric iron–sulfur (Fe-S) cluster-bridged form. In TRXo2, the [4Fe-4S] cluster is likely ligated in by the usual catalytic cysteines present in the conserved Trp-Cys-Gly-Pro-Cys signature. Solving the three-dimensional structure of both TRXo apo-forms pointed to marked differences in the surface charge distribution, notably in some area usually participating to protein–protein interactions with partners. However, we could not detect a difference in their capacity to reduce nitrogen-fixation-subunit-U (NFU)-like proteins, NFU4 or NFU5, two proteins participating in the maturation of certain mitochondrial Fe-S proteins and previously isolated as putative TRXo1 partners. Altogether, these results suggest that a novel regulation mechanism may prevail for mitochondrial TRXs o, possibly existing as a redox-inactive Fe-S cluster-bound form that could be rapidly converted in a redox-active form upon cluster degradation in specific physiological conditions.

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

scholarly journals Flexibility and binding affinity in protein–ligand, protein–protein and multi-component protein interactions: limitations of current computational approaches

2011 ◽  
Vol 9 (66) ◽  
pp. 20-33 ◽  
Author(s):  
Pierre Tuffery ◽  
Philippe Derreumaux
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Large Scale ◽  
De Novo ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Structure Based Drug Design ◽  
Body Protein ◽  
Technical Problems ◽  
Multi Body

The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.

scholarly journals 2-amino-1,3-benzothiazole-6-carboxamide Preferentially Binds the Tandem Mismatch Motif r(UY:GA)

2020 ◽  
Author(s):  
Andrew T. Chang ◽  
Lu Chen ◽  
Luo Song ◽  
Shuxing Zhang ◽  
Edward P. Nikonowicz
Keyword(s):  
Small Molecules ◽  
Dipolar Coupling ◽  
Residual Dipolar Coupling ◽  
Three Dimensional ◽  
Coupling Constants ◽  
Dimensional Structure ◽  
Flanking Sequence ◽  
Base Pairs ◽  
Virtual Screen ◽  
Rna Hairpin

AbstractRNA helices are often punctuated with non-Watson-Crick features that can be the target of chemical compounds, but progress towards identifying small molecules specific for non-canonical elements has been slow. We have used a tandem UU:GA mismatch motif (5’-UG-3’:5’-AU-3’) embedded within the helix of an RNA hairpin as a model to identify compounds that bind the motif specifically. The three-dimensional structure of the RNA hairpin and its interaction with a small molecule compound identified through a virtual screen are presented. The G-A of the mismatch forms a sheared pair upon which the U-U base pair stacks. The hydrogen bond configuration of the U-U pair involves the O2 of the U adjacent to the G and the O4 of the U adjacent to the A. The G-A and U-U pairs are flanked by A-U and G-C base pairs, respectively, and the mismatch exhibits greater stability than when the motif is within the context of other flanking base pairs or when the 5’-3’ orientation of the G-A and U-U is swapped. Residual dipolar coupling constants were used to generate an ensemble of structures against which a virtual screen of 64,480 small molecules was performed to identify candidate compounds that the motif specifically binds. The tandem mismatch was found to be specific for one compound, 2-amino-1,3-benzothiazole-6-carboxamide, which binds with moderate affinity but extends the motif to include the flanking A-U and G-C base pairs. The finding that affinity for the UU:GA mismatch is flanking sequence dependent emphasizes the importance of motif context and potentially increases the number of small non-canonical features within RNA that can be specifically targeted by small molecules.

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