Graph theoretic methods for the analysis of structural relationships in biological macromolecules

Subgraph isomorphism and maximum common subgraph isomorphism algorithms from graph theory provide an effective and an efficient way of identifying structural relationships between biological macromolecules. They thus provide a natural complement to the pattern matching algorithms that are used in bioinformatics to identify sequence relationships. Examples are provided of the use of graph theory to analyze proteins for which three-dimensional crystallographic or NMR structures are available, focusing on the use of the Bron-Kerbosch clique detection algorithm to identify common folding motifs and of the Ullmann subgraph isomorphism algorithm to identify patterns of amino acid residues. Our methods are also applicable to other types of biological macromolecule, such as carbohydrate and nucleic acid structures

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

Computation of protein geometry and its applications: Packing and function prediction

This chapter discusses geometric models of biomolecules and geometric constructs, including the union of ball model, the weigthed Voronoi diagram, the weighted Delaunay triangulation, and the alpha shapes. These geometric constructs enable fast and analytical computaton of shapes of biomoleculres (including features such as voids and pockets) and metric properties (such as area and volume). The algorithms of Delaunay triangulation, computation of voids and pockets, as well volume/area computation are also described. In addition, applications in packing analysis of protein structures and protein function prediction are also discussed.Comment: 32 pages, 9 figure

Detection of Aliphatically Bridged Multi-Core Polycyclic Aromatic Hydrocarbons in Sooting Flames with Atmospheric-Sampling High-Resolution Tandem Mass Spectrometry.

This paper provides experimental evidence for the chemical structures of aliphatically substituted and bridged polycyclic aromatic hydrocarbon (PAH) species in gas-physe combustion environments. The identification of these single- and multicore aromatic species, which have been hypothesized to be important in PAH growth and soot nucleation, was made possible through a combination of sampling gaseous constituents from an atmospheric pressure inverse coflow diffusion flame of ethylene and high-resolution tandem mass spectrometry (MS-MS). In these experiments, the flame-sampled components were ionized using a continuous VUV lamp at 10.0 eV and the ions were subsequently fragmented through collisions with Ar atoms in a collision-induced dissociation (CID) process. The resulting fragment ions, which were separated using a reflectron time-of-flight mass spectrometer, were used to extract structural information about the sampled aromatic compounds. The high-resolution mass spectra revealed the presence of alkylated single-core aromatic compounds and the fragment ions that were observed correspond to the loss of saturated and unsaturated units containing up to a total of 6 carbon atoms. Furthermore, the aromatic structures that form the foundational building blocks of the larger PAHs were identified to be smaller single-ring and pericondensed aromatic species with repetitive structural features. For demonstrative purposes, details are provided for the CID of molecular ions at masses 202 and 434. Insights into the role of the aliphatically substituted and bridged aromatics in the reaction network of PAH growth chemistry were obtained from spatially resolved measurements of the flame. The experimental results are consistent with a growth mechanism in which alkylated aromatics are oxidized to form pericondensed ring structures or react and recombine with other aromatics to form larger, potentially three-dimensional, aliphatically bridged multicore aromatic hydrocarbons

Insightful classification of crystal structures using deep learning

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 100 000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018

MAS NMR detection of hydrogen bonds for protein secondary structure characterization

Hydrogen bonds are essential for protein structure and function, making experimental access to long-range interactions between amide protons and heteroatoms invaluable. Here we show that measuring distance restraints involving backbone hydrogen atoms and carbonyl- or α-carbons enables the identification of secondary structure elements based on hydrogen bonds, provides long-range contacts and validates spectral assignments. To this end, we apply specifically tailored, proton-detected 3D (H)NCOH and (H)NCAH experiments under fast magic angle spinning (MAS) conditions to microcrystalline samples of SH3 and GB1. We observe through-space, semi-quantitative correlations between protein backbone carbon atoms and multiple amide protons, enabling us to determine hydrogen bonding patterns and thus to identify β-sheet topologies and α-helices in proteins. Our approach shows the value of fast MAS and suggests new routes in probing both secondary structure and the role of functionally-relevant protons in all targets of solid-state MAS NMR