DYNAMIC CONFIGURATION FOR DISTRIBUTED SYSTEMS

Published versio

Components of Coated Vesicles and Nuclear Pore Complexes Share a Common Molecular Architecture

Numerous features distinguish prokaryotes from eukaryotes, chief among which are the distinctive internal membrane systems of eukaryotic cells. These membrane systems form elaborate compartments and vesicular trafficking pathways, and sequester the chromatin within the nuclear envelope. The nuclear pore complex is the portal that specifically mediates macromolecular trafficking across the nuclear envelope. Although it is generally understood that these internal membrane systems evolved from specialized invaginations of the prokaryotic plasma membrane, it is not clear how the nuclear pore complex could have evolved from organisms with no analogous transport system. Here we use computational and biochemical methods to perform a structural analysis of the seven proteins comprising the yNup84/vNup107–160 subcomplex, a core building block of the nuclear pore complex. Our analysis indicates that all seven proteins contain either a β-propeller fold, an α-solenoid fold, or a distinctive arrangement of both, revealing close similarities between the structures comprising the yNup84/vNup107–160 subcomplex and those comprising the major types of vesicle coating complexes that maintain vesicular trafficking pathways. These similarities suggest a common evolutionary origin for nuclear pore complexes and coated vesicles in an early membrane-curving module that led to the formation of the internal membrane systems in modern eukaryotes

Ab initio and homology based prediction of protein domains by recursive neural networks

Background: Proteins, especially larger ones, are often composed of individual evolutionary units, domains, which have their own function and structural fold. Predicting domains is an important intermediate step in protein analyses, including the prediction of protein structures. Results: We describe novel systems for the prediction of protein domain boundaries powered by Recursive Neural Networks. The systems rely on a combination of primary sequence and evolutionary information, predictions of structural features such as secondary structure, solvent accessibility and residue contact maps, and structural templates, both annotated for domains (from the SCOP dataset) and unannotated (from the PDB). We gauge the contribution of contact maps, and PDB and SCOP templates independently and for different ranges of template quality. We find that accurately predicted contact maps are informative for the prediction of domain boundaries, while the same is not true for contact maps predicted ab initio. We also find that gap information from PDB templates is informative, but, not surprisingly, less than SCOP annotations. We test both systems trained on templates of all qualities, and systems trained only on templates of marginal similarity to the query (less than 25% sequence identity). While the first batch of systems produces near perfect predictions in the presence of fair to good templates, the second batch outperforms or match ab initio predictors down to essentially any level of template quality. We test all systems in 5-fold cross-validation on a large non-redundant set of multi-domain and single domain proteins. The final predictors are state-of-the-art, with a template-less prediction boundary recall of 50.8% (precision 38.7%) within ± 20 residues and a single domain recall of 80.3% (precision 78.1%). The SCOP-based predictors achieve a boundary recall of 74% (precision 77.1%) again within ± 20 residues, and classify single domain proteins as such in over 85% of cases, when we allow a mix of bad and good quality templates. If we only allow marginal templates (max 25% sequence identity to the query) the scores remain high, with boundary recall and precision of 59% and 66.3%, and 80% of all single domain proteins predicted correctly. Conclusion: The systems presented here may prove useful in large-scale annotation of protein domains in proteins of unknown structure. The methods are available as public web servers at the address: http://distill.ucd.ie/shandy/ and we plan on running them on a multi-genomic scale and make the results public in the near future.Science Foundation IrelandHealth Research BoardUCD President's Award 2004au, da, sp, ke, ab - kpw2/12/1

Chromatin and Epigenetics

Genomics has gathered broad public attention since Lamarck put forward his top-down hypothesis of 'motivated change' in 1809 in his famous book "Philosophie Zoologique" and even more so since Darwin published his famous bottom-up theory of natural selection in "The Origin of Species" in 1859. The public awareness culminated in the much anticipated race to decipher the sequence of the human genome in 2002. Over all those years, it has become apparent that genomic DNA is compacted into chromatin with a dedicated 3D higher-order organization and dynamics, and that on each structural level epigenetic modifications exist. The book "Chromatin and Epigenetics" addresses current issues in the fields of epigenetics and chromatin ranging from more theoretical overviews in the first four chapters to much more detailed methodologies and insights into diagnostics and treatments in the following chapters. The chapters illustrate in their depth and breadth that genetic information is stored on all structural and dynamical levels within the nucleus with corresponding modifications of functional relevance. Thus, only an integrative systems approach allows to understand, treat, and manipulate the holistic interplay of genotype and phenotype creating functional genomes. The book chapters therefore contribute to this general perspective, not only opening opportunities for a true universal view on genetic information but also being key for a general understanding of genomes, their function, as well as life and evolution in general

Parallelisation efficace de larges applications temps-reel

We present a parallel compilation method for embedded control applications. The method is fully automatic and scales up, being based on low-complexity heuristics. Unlike classical compilation, it also takes as input non-functional requirements, e.g. real-time or resource limits.The main objective is not optimization per se, but the respect of requirements. To this end, static resource allocation and code generation algorithms perform a safe accounting of non-functional properties. Accounting starts from per-component time and memory footprint worst-case bounds, automatically obtained through calls to state-of-the-art static analysis tools. Experiments show that our method produces efficient code for large-scale, real-life avionics applications

Computational composition strategies in audiovisual laptop performance

We live in a cultural environment in which computer based musical performances have become ubiquitous. Particularly the use of laptops as instruments is a thriving practice in many genres and subcultures. The opportunity to command the most intricate level of control on the smallest of time scales in music composition and computer graphics introduces a number of complexities and dilemmas for the performer working with algorithms. Writing computer code to create audiovisuals offers abundant opportunities for discovering new ways of expression in live performance while simultaneously introducing challenges and presenting the user with difficult choices. There are a host of computational strategies that can be employed in live situations to assist the performer, including artificially intelligent performance agents who operate according to predefined algorithmic rules. This thesis describes four software systems for real time multimodal improvisation and composition in which a number of computational strategies for audiovisual laptop performances is explored and which were used in creation of a portfolio of accompanying audiovisual compositions

ORGANIZING FORCES AND CONFORMATIONAL ACCESSIBILITY IN THE UNFOLDED STATE OF PROTEINS

For over fifty years, the unfolded state of proteins had been thought to be featureless and random. Experiments by Tanford and Flory confirmed that unfolded proteins possessed the same dimensions as those predicted of a random flight chain in good solvent. In the late eighties and early nineties, however, researchers began to notice structural trends in unfolded proteins. Some experiments showed that the unfolded state was very similar to the native state, while others indicated a conformational preference for the polyproline II helix in unfolded proteins. As a result, a paradox developed. How can unfolded proteins be both random and nonrandom at the same time? Current experiments and most theoretical simulations cannot characterize the unfolded state in high detail, so we have used the simplified hard sphere model of Richards to address this question. By modeling proteins as hard spheres, we can not only determine what interactions are important in the unfolded state of proteins, but we can address the paradox directly by investigating whether nonrandom behavior is in conflict with random coil statistics. Our simulations identify hundreds of disfavored conformations in short peptides, each of which proves that unfolded proteins are not at all random. Some interactions are important for the folded state of proteins as well. For example, we find that an α-helix cannot be followed directly by a β-strand because of steric considerations. The interactions outlined here limit the conformational possibilities of an unfolded protein far beyond what would be expected for a random coil. For a 100-residue protein, we find that approximately 9 orders of magnitude of conformational freedom are lost because of iii local chain organization alone. Furthermore, we show that the existence of this organization is compatible with random coil statistics. Although our simulations cannot settle the controversy surrounding the unfolded state, we can conclude that new methods of characterizing the unfolded state are needed. Since unfolded proteins are not random coils, the methods developed for describing random coils cannot adequately describe the complexities of this diverse structural ensemble