Experimental Mapping Data Research Articles

Heterogeneous microstructure of yttrium hydride and its relation to mechanical properties

The goal of this study is to investigate the properties of yttrium hydride materials in relation to the microstructure, especially its homogeneity. High-throughput nanoindentation mapping was used to evaluate hardness distribution. Raman spectral imaging demonstrated its sensitivity to the presence of YH2 and impurities. Raman peak position maps were correlated with residual stress in the specimens. Electron backscatter diffraction mapping provided phase distributions with correlation to high-energy X-ray diffraction analysis. The experimental mapping data were combined and analyzed using unsupervised machine learning cluster procedures. The machine learning analysis revealed that yttrium hydride specimens contained a major δ-YH2 − x phase component and minor α-Y and δ-YH2 − x components with significant residual stress. The minor phase fraction decreased with increasing nominal H/Y ratio, which affected the nanoindentation and Vickers hardness. The multimodal mapping procedures described herein affect developing important microstructure–property relationships, as well as correlations in heterogeneity and mechanical properties.

Modeling RNA secondary structure folding ensembles using SHAPE mapping data

RNA secondary structure prediction is widely used for developing hypotheses about the structures of RNA sequences, and structure can provide insight about RNA function. The accuracy of structure prediction is known to be improved using experimental mapping data that provide information about the pairing status of single nucleotides, and these data can now be acquired for whole transcriptomes using high-throughput sequencing. Prior methods for using these experimental data focused on predicting structures for sequences assuming that they populate a single structure. Most RNAs populate multiple structures, however, where the ensemble of strands populates structures with different sets of canonical base pairs. The focus on modeling single structures has been a bottleneck for accurately modeling RNA structure. In this work, we introduce Rsample, an algorithm for using experimental data to predict more than one RNA structure for sequences that populate multiple structures at equilibrium. We demonstrate, using SHAPE mapping data, that we can accurately model RNA sequences that populate multiple structures, including the relative probabilities of those structures. This program is freely available as part of the RNAstructure software package.

Modeling RNA Secondary Structure with Sequence Comparison and Experimental Mapping Data

Secondary structure prediction is an important problem in RNA bioinformatics because knowledge of structure is critical to understanding the functions of RNA sequences. Significant improvements in prediction accuracy have recently been demonstrated though the incorporation of experimentally obtained structural information, for instance using selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) mapping. However, such mapping data is currently available only for a limited number of RNA sequences. In this article, we present a method for extending the benefit of experimental mapping data in secondary structure prediction to homologous sequences. Specifically, we propose a method for integrating experimental mapping data into a comparative sequence analysis algorithm for secondary structure prediction of multiple homologs, whereby the mapping data benefits not only the prediction for the specific sequence that was mapped but also other homologs. The proposed method is realized by modifying the TurboFold II algorithm for prediction of RNA secondary structures to utilize basepairing probabilities guided by SHAPE experimental data when such data are available. The SHAPE-mapping-guided basepairing probabilities are obtained using the RSample method. Results demonstrate that the SHAPE mapping data for a sequence improves structure prediction accuracy of other homologous sequences beyond the accuracy obtained by sequence comparison alone (TurboFold II). The updated version of TurboFold II is freely available as part of the RNAstructure software package.

A mapping model for product surface design based on ergonomics requirements

In this paper, a mapping model for product surface morphology design based on ergonomics is proposed. The method advances the traditional approaches based on anthropometric measurements to a new one based on digitised product surfaces; thus, it simplifies the complexity of non-uniform rational B-spline surface representation during product design. The method was constructed by converting the experimental data to the surface shapes based on the pressure distribution map. An adjustment algorithm is presented to eliminate the errors in mapping non-planar base shapes. The experimental mapping data are pretreated, so that the mapping process reflects the statistical characteristics of the tested groups as far as possible. Based on the mapping model, a prototype implementation system for ergonomic product design is developed. As an example, a car seat design is presented to show the performance and availability of the method.

Systematic Analysis of Proteoglycan Modification Sites in Caenorhabditis elegans by Scanning Mutagenesis

Proteoglycan modification is essential for development and early cell division in Caenorhabditis elegans. The specification of proteoglycan attachment sites is defined by the Golgi enzyme polypeptide xylosyltransferase. Here we evaluate the substrate specificity of this xylosyltransferase for its downstream targets by using reporter proteins containing proteoglycan modification sites from C. elegans syndecan/SDN-1. The N terminus of the SDN-1 contains a Ser-Gly proteoglycan site at Ser(71), flanked by potential mucin and N-glycosylation sites. However, Ser(71) was exclusively used as a proteoglycan site in vivo, based on mapping studies with a Ser(71) reporter protein, glycosyltransferase RNA interference, and co-expression of worm polypeptide xylosyltransferase. To elucidate the substrate requirements of this enzyme, a library of 42 point mutants of the Ser(71) reporter was expressed in tissue culture. The nematode proteoglycan modification site in SDN-1 required serine (not threonine), two flanking glycine residues (positions -1 and +1), and either one proximal acidic N-terminal amino acid (positions -4, -3, and -2) or a pair of distal N-terminal acidic amino acids (positions -6 and -5). C-terminal acidic amino acids, although present in many proteoglycan modification sites, had minimal impact on xylosylation at Ser(71). Proline inhibited glycosylation when present at -1, +1, or +2. The position of glycine, proline, and acidic amino acids allows the glycosylation machinery to discriminate between mucin and proteoglycan modification sites. The key residues that define proteoglycan modification sites also function with the Drosophila polypeptide xylosyltransferase, indicating that the specificity in the glycosylation process is evolutionarily conserved. Using a neural network method, a preliminary proteoglycan predictor has been developed.

SMOOTH: a statistical method for successful removal of genotyping errors from high-density genetic linkage data

High-density genetic linkage maps can be used for purposes such as fine-scale targeted gene cloning and anchoring of physical maps. However, their construction is significantly complicated by even relatively small amounts of scoring errors. Currently available software is not able to solve the ordering ambiguities in marker clusters, which inhibits the application of high-density maps. A statistical method named SMOOTH was developed to remove genotyping errors from genetic linkage data during the mapping process. The program SMOOTH calculates the difference between the observed and predicted values of data points based on data points of neighbouring loci in a given marker order. Highly improbable data points are removed by the program in an iterative process with a mapping algorithm that recalculates the map after cleaning. SMOOTH has been tested with simulated data and experimental mapping data from potato. The simulations prove that this method is able to detect a high amount of scoring errors and demonstrates that the program enables mapping software to successfully construct a very accurate high-density map. In potato the application of the program resulted in a reliable placement of nearly 1,000 markers in one linkage group.

Identification of Substrate Binding Sites in Enzymes by Computational Solvent Mapping

Enzyme structures determined in organic solvents show that most organic molecules cluster in the active site, delineating the binding pocket. We have developed algorithms to perform solvent mapping computationally, rather than experimentally, by placing molecular probes (small molecules or functional groups) on a protein surface, and finding the regions with the most favorable binding free energy. The method then finds the consensus site that binds the highest number of different probes. The probe–protein interactions at this site are compared to the intermolecular interactions seen in the known complexes of the enzyme with various ligands (substrate analogs, products, and inhibitors). We have mapped thermolysin, for which experimental mapping results are also available, and six further enzymes that have no experimental mapping data, but whose binding sites are well characterized. With the exception of haloalkane dehalogenase, which binds very small substrates in a narrow channel, the consensus site found by the mapping is always a major subsite of the substrate-binding site. Furthermore, the probes at this location form hydrogen bonds and non-bonded interactions with the same residues that interact with the specific ligands of the enzyme. Thus, once the structure of an enzyme is known, computational solvent mapping can provide detailed and reliable information on its substrate-binding site. Calculations on ligand-bound and apo structures of enzymes show that the mapping results are not very sensitive to moderate variations in the protein coordinates.

Nucleotide sequence-directed mapping of the nucleosomes.

The concept of sequence-dependent deformational anisotropy of DNA proposed earlier is further elaborated and a computational procedure is developed for the sequence-directed mapping of the nucleosomes along chromatin DNA nucleotide sequences. The deformational anisotropy is found to be nonuniform along the molecule of the nucleosomal DNA, suggesting that the DNA superhelix in the nucleosome is slightly oval rather than circular in projection. The number of superhelical turns in the nucleosome core particle is estimated to be 2.0 +/- 0.2. Preliminary mapping of the nucleosomes in various chromatin DNA sequences yields the distribution of linker lengths which shows several minima separated by about 10 base-pairs. This is explained by sterical exclusion effects due to overlapping of the nucleosomes in space when some specific linker lengths are chosen. The mapping procedure described is tested by comparing its results with all the most accurate experimental mapping data reported so far. The comparison demonstrates that the exact positions of all the nucleosomes appear to be determined exclusively by the nucleotide sequences.