Hyperdimensional Analysis of Amino Acid Pair Distributions in Proteins

Our manuscript presents a novel approach to protein structure analyses. We have organized an 8-dimensional data cube with protein 3D-structural information from 8706 high-resolution non-redundant protein-chains with the aim of identifying packing rules at the amino acid pair level. The cube contains information about amino acid type, solvent accessibility, spatial and sequence distance, secondary structure and sequence length. We are able to pose structural queries to the data cube using program ProPack. The response is a 1, 2 or 3D graph. Whereas the response is of a statistical nature, the user can obtain an instant list of all PDB-structures where such pair is found. The user may select a particular structure, which is displayed highlighting the pair in question. The user may pose millions of different queries and for each one he will receive the answer in a few seconds. In order to demonstrate the capabilities of the data cube as well as the programs, we have selected well known structural features, disulphide bridges and salt bridges, where we illustrate how the queries are posed, and how answers are given. Motifs involving cysteines such as disulphide bridges, zinc-fingers and iron-sulfur clusters are clearly identified and differentiated. ProPack also reveals that whereas pairs of Lys residues virtually never appear in close spatial proximity, pairs of Arg are abundant and appear at close spatial distance, contrasting the belief that electrostatic repulsion would prevent this juxtaposition and that Arg-Lys is perceived as a conservative mutation. The presented programs can find and visualize novel packing preferences in proteins structures allowing the user to unravel correlations between pairs of amino acids. The new tools allow the user to view statistical information and visualize instantly the structures that underpin the statistical information, which is far from trivial with most other SW tools for protein structure analysis

Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory

<p>Abstract</p> <p>Background</p> <p>Disulfide bonds constitute one of the most important cross-linkages in proteins and significantly influence protein structure and function. At the state-of-the-art, various methodological frameworks have been proposed for identification of disulfide bonds. These include among others, mass spectrometry-based methods, sequence-based predictive approaches, as well as techniques like crystallography and NMR. Each of these frameworks has its advantages and disadvantages in terms of pre-requisites for applicability, throughput, and accuracy. Furthermore, the results from different methods may concur or conflict in parts.</p> <p>Results</p> <p>In this paper, we propose a novel and theoretically rigorous framework for disulfide bond determination based on information fusion from different methods using an extended formulation of Dempster-Shafer theory. A key advantage of our approach is that it can automatically deal with concurring as well as conflicting evidence in a data-driven manner. Using the proposed framework, we have developed a method for disulfide bond determination that combines results from sequence-based prediction and mass spectrometric inference. This method leads to more accurate disulfide bond determination than any of the constituent methods taken individually. Furthermore, experiments indicate that the method improves the accuracy of bond identification as compared to leading extant methods at the state-of-the-art. Finally, the proposed framework is extensible in that results from any number of approaches can be incorporated. Results obtained using this framework can especially be useful in cases where the complexity of the bonding patterns coupled with specificities of the fragmentation pattern or limitations of computational models impair any single method to perform consistently across a diverse set of molecules.</p

Self-assembly of Organic Molecules at Metal Surfaces

Adsorption and self-assembly of organic molecules at surfaces is a key issue in nanoscience and nanotechnology for the many possible uses of hybrid organic\u2013inorganic interfaces. Depending on the nature of the molecules, applications are foreseen in the fields of molecular electronics, sensoristics, pharmacology, biocompatibility, hygiene and biofouling. As a consequence, there has been a large effort in the last few years to determine the structure of the layers and to unravel the mechanisms at the basis of the self-assembly process

Separated at birth? The functional and molecular divergence of OLIG1 and OLIG2

The basic helix-loop-helix transcription factors oligodendrocyte transcription factor 1 (OLIG1) and OLIG2 are structurally similar and, to a first approximation, coordinately expressed in the developing CNS and postnatal brain. Notwithstanding these similarities, it was apparent from early on after their discovery that OLIG1 and OLIG2 have non-overlapping developmental functions in patterning, neuron subtype specification and the formation of oligodendrocytes. Here, we summarize more recent insights into the separate functions of these transcription factors in the postnatal brain during repair processes and in neurological disease states, including multiple sclerosis and malignant glioma. We discuss how the unique biological functions of OLIG1 and OLIG2 may reflect their distinct genetic targets, co-regulator proteins and/or post-translational modifications