Automated assignment and 3D structure calculations using combinations of 2D homonuclear and 3D heteronuclear NOESY spectra

The NOAH/DIAMOD suite uses feedback filtering and self-correcting distance geometry to generate 3D structures from unassigned NOESY spectra. In this study we determined the minimum set of experiments needed to generate a high quality structure bundle. Different combinations of 3D ¹⁵N-edited, ¹³C-edited HSQC-NOESY and 2D homonuclear ¹H-¹H NOESY spectra of the 77 amino acid protein, myeloid progenitor inhibitory factor-1 (MPIF-1) were used as input for NOAH/DIAMOD calculations. The quality of the assignments of NOESY cross peaks and the accuracy of the automatically generated 3D structures were compared to those obtained with a conventional manual procedure. Combining data from two types of experiments synergistically increased the number of peaks assigned unambiguously in both individual spectra. As a general trend for the accuracy of the structures we observed structural variations in the backbone fold of the final structures of about 2 Å for single spectral data, of 1 Å to 1.5 Å for double spectral data, and of 0.6 Å for triple spectral data sets. The quality of the assignments and 3D structures from the optimal data using all three spectra were similar to those obtained from traditional assignment methods with structural variations within the bundle of 0.6 Å and 1.3 Å for backbone and heavy atoms, respectively. Almost all constraints (97%) of the automatic NOESY cross peak assignments were cross compatible with the structures from the conventional manual assignment procedure, and an even larger proportion (99%) of the manually derived constraints were compatible with the automatically determined 3D structures. The two mean structures determined by both methods differed only by 1.3 Å rmsd for the backbone atoms in the well-defined regions of the protein. Thus NOAD/DIAMOD analysis of spectra from labeled proteins provides a reliable method for high throughput analysis of genomic targets.     

Automated discovery of 3D motifs for protein function annotation

MOTIVATION: Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3-10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function. RESULTS: We apply our method, GASPS, to the haloacid dehalogenase, enolase, amidohydrolase and crotonase superfamilies and to the serine proteases. The motifs found by GASPS are as good at function prediction as 3D motifs based on expert knowledge. The GASPS motifs with the greatest ability to predict protein function consist mainly of known functional residues. However, several residues with no known functional role are equally predictive. For four groups, we show that the predictive power of our 3D motifs is comparable with or better than approaches that use the entire fold (Combinatorial-Extension) or sequence profiles (PSI-BLAST). AVAILABILITY: Source code is freely available for academic use by contacting the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Automated 3D phenotype analysis using data mining

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases.     

Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas

The analysis of atomic-resolution RNA three-dimensional (3D) structures reveals that many internal and hairpin loops are modular, recurrent, and structured by conserved non-Watson–Crick base pairs. Structurally similar loops define RNA 3D motifs that are conserved in homologous RNA molecules, but can also occur at nonhomologous sites in diverse RNAs, and which often vary in sequence. To further our understanding of RNA motif structure and sequence variability and to provide a useful resource for structure modeling and prediction, we present a new method for automated classification of internal and hairpin loop RNA 3D motifs and a new online database called the RNA 3D Motif Atlas. To classify the motif instances, a representative set of internal and hairpin loops is automatically extracted from a nonredundant list of RNA-containing PDB files. Their structures are compared geometrically, all-against-all, using the FR3D program suite. The loops are clustered into motif groups, taking into account geometric similarity and structural annotations and making allowance for a variable number of bulged bases. The automated procedure that we have implemented identifies all hairpin and internal loop motifs previously described in the literature. All motif instances and motif groups are assigned unique and stable identifiers and are made available in the RNA 3D Motif Atlas (http://rna.bgsu.edu/motifs), which is automatically updated every four weeks. The RNA 3D Motif Atlas provides an interactive user interface for exploring motif diversity and tools for programmatic data access.     

I-TASSER server for protein 3D structure prediction

Background  

Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions.     

An evaluation of detection methods for large lariat RNAs

Ty1 elements are long terminal repeat (LTR) retrotransposons that reside within the genome of Saccharomyces cerevisiae. It has been known for many years that the 2'-5' phosphodiesterase Dbr1p, which debranches intron lariats, is required for efficient Ty1 transposition. A recent report suggested the intriguing possibility that Ty1 RNA forms a lariat as a transposition intermediate. We set out to further investigate the nature of the proposed Ty1 lariat branchpoint. However, using a wide range of techniques we were unable to find any evidence for the proposed lariat structure. Furthermore, we demonstrate that some of the techniques used in the initial study describing the lariat are capable of incorrectly reporting a lariat structure. Thus, the role of the Dbr1 protein in Ty1 retrotransposition remains elusive.     

Deep generative models for 3D molecular structure

Deep generative models have gained recent popularity for chemical design. Many of these models have historically operated in 2D space; however, more recently explicit 3D molecular generative models have become of interest, which are the topic of this article. Dozens of published models have been developed in the last few years to generate molecules directly in 3D, outputting both the atom types and coordinates, either in one-shot or adding atoms or fragments step-by-step. These 3D generative models can also be guided by structural information such as a binding pocket representation to successfully generate molecules with docking score ranges similar to known actives, but still showing lower computational efficiency and generation throughput than 1D/2D generative models and sometimes producing unrealistic conformations. We advocate for a unified benchmark of metrics to evaluate generation and propose perspectives to be addressed in next implementations.     

Small RNAs loom large during reprogramming

In two independent Cell Stem Cell reports, the Morrisey and Mori groups show that human and mouse somatic cells can be reprogrammed to produce induced pluripotent stem cells by expressing microRNAs, completely eliminating the need for ectopic protein expression (Anokye-Danso et al., 2011; Miyoshi et al., 2011).     

Carbohydrate 3D structure validation

1. Download : Download high-res image (225KB)
2. Download : Download full-size image

     

Processing of intron-encoded box C/D small nucleolar RNAs lacking a 5',3'-terminal stem structure

The C and D box-containing (box C/D) small nucleolar RNAs (snoRNAs) function in the nucleolytic processing and 2'-O-methylation of precursor rRNA. In vertebrates, most box C/D snoRNAs are processed from debranched pre-mRNA introns by exonucleolytic activities. Elements directing accurate snoRNA excision are located within the snoRNA itself; they comprise the conserved C and D boxes and an adjoining 5',3'-terminal stem. Although the terminal stem has been demonstrated to be essential for snoRNA accumulation, many snoRNAs lack a terminal helix. To identify the cis-acting elements supporting the accumulation of intron-encoded box C/D snoRNAs devoid of a terminal stem, we have investigated the in vivo processing of the human U46 snoRNA and an artificial snoRNA from the human beta-globin pre-mRNA. We demonstrate that internal and/or external stem structures located within the snoRNA or in the intronic flanking sequences support the accumulation of mammalian box C/D snoRNAs lacking a canonical terminal stem. In the intronic precursor RNA, transiently formed external and/or stable internal base-pairing interactions fold the C and D boxes together and therefore facilitate the binding of snoRNP proteins. Since the external intronic stems are degraded during snoRNA processing, we propose that the C and D boxes alone can provide metabolic stability for the mature snoRNA.     

Automated detection of tunneling nanotubes in 3D images.

BACKGROUND: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50-200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell-to-cell communication. METHODS: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. RESULTS: The TNT- and cell-detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. CONCLUSION: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell-to-cell communication where TNT quantification is essential.     

NMR structure of the Vibrio vulnificus ribosomal protein S1 domains D3 and D4 provides insights into molecular recognition of single-stranded RNAs

The ribosomal S1 protein (rS1) is indispensable for translation initiation in Gram-negative bacteria. rS1 is a multidomain protein that acts as an RNA chaperone and ensures that mRNAs can bind the ribosome in a single-stranded conformation, which could be related to fast recognition. Although many ribosome structures were solved in recent years, a high-resolution structure of a two-domain mRNA-binding competent rS1 construct is not yet available. Here, we present the NMR solution structure of the minimal mRNA-binding fragment of Vibrio Vulnificus rS1 containing the domains D3 and D4. Both domains are homologues and adapt an oligonucleotide-binding fold (OB fold) motif. NMR titration experiments reveal that recognition of miscellaneous mRNAs occurs via a continuous interaction surface to one side of these structurally linked domains. Using a novel paramagnetic relaxation enhancement (PRE) approach and exploring different spin-labeling positions within RNA, we were able to track the location and determine the orientation of the RNA in the rS1–D34 bound form. Our investigations show that paramagnetically labeled RNAs, spiked into unmodified RNA, can be used as a molecular ruler to provide structural information on protein-RNA complexes. The dynamic interaction occurs on a defined binding groove spanning both domains with identical β2-β3-β5 interfaces. Evidently, the 3′-ends of the cis-acting RNAs are positioned in the direction of the N-terminus of the rS1 protein, thus towards the 30S binding site and adopt a conformation required for translation initiation.     

Automated pollen recognition using 3D volume images from fluorescence microscopy

Identifying and counting of pollen grains in ambient air samples is still a demanding and time-consuming task even for an experienced microscopist. This article describes a technique which may be employed to establish a fully automated system for this task. Based on a 3D volume fluorescence image of a pollen grain taken with a confocal laser scanning microscope, the described system is able to recognize the pollen taxa. The system autonomously extracts all required information for the recognition from a data base with reference objects (self-learning system) and only needs to calculate very general purpose features of the volumetric data sets (so-called gray scale invariants). This allows for easy adaptation of the system to other conditions (e.g., pollen of a special area) or even other objects than pollen (e.g., spores, bacteria etc.) just by exchanging the reference data base. When using a reference data base with the 26 most important German pollen taxa, the recognition rate is 92%. With a special database for allergic purposes recognizing only Corylus, Alnus, Betula, Poaceae, Secale, Artemisia and ``allergically non-relevant' the recognition rate is 97.4%.     

Automated 3D tree-ring detection and measurement from X-ray computed tomography

Tree ring analysis is essential to reveal the environmental information encoded in the wood structure. It provides quantitative data on the anatomical structure which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, to support global vegetation models and for the dendrochronological analysis of archaeological wooden artefacts. Currently, several imaging-based methods for tree-ring detection and tree-ring feature estimation exist. However, despite advances in computer vision and edge recognition algorithms, detection of tree-rings is mostly limited to two-dimensional (2D) datasets and performed manually in some cases. This paper describes a new approach to estimate the three-dimensional (3D) structure of tree rings and their width automatically from X-ray computed tomography data. This approach relies on a modified Canny edge detection algorithm, which is capable of detecting fully connected tree-ring edges throughout the image stack. Our results show that this approach performs well on six tree species having conifer, ring-porous and diffuse-porous ring boundary structures. In our study, image denoising proved to be a critical step to achieve accurate results. A major advantage of this procedure is that it requires very little to no user interaction rendering it a reproducible procedure for tree-ring width measurements. As it also provides 3D representations of the ring edges, it also may be used in the future for the inspection of anatomical features.