NRG-CING: integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB

For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists are available, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. The accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculation using the available experimental data and present-day software. Such efforts, however, generally fail on half of all NMR ensembles due to the syntactic and semantic heterogeneity of the underlying data and the wide variety of formats used for their deposition. We have combined the remediated restraint information from our NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank and the Common Interface for NMR structure Generation (CING) structure validation reports into the weekly updated NRG-CING database (http://nmr.cmbi.ru.nl/NRG-CING). Eleven programs have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available experimental NMR data. The longitudinal validation of these data in a publicly available relational database yields a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The remediated NMR experimental data sets and validation reports are freely available online

Astro-WISE: Chaining to the Universe

The recent explosion of recorded digital data and its processed derivatives threatens to overwhelm researchers when analysing their experimental data or when looking up data items in archives and file systems. While current hardware developments allow to acquire, process and store 100s of terabytes of data at the cost of a modern sports car, the software systems to handle these data are lagging behind. This general problem is recognized and addressed by various scientific communities, e.g., DATAGRID/EGEE federates compute and storage power over the high-energy physical community, while the astronomical community is building an Internet geared Virtual Observatory, connecting archival data. These large projects either focus on a specific distribution aspect or aim to connect many sub-communities and have a relatively long trajectory for setting standards and a common layer. Here, we report "first light" of a very different solution to the problem initiated by a smaller astronomical IT community. It provides the abstract "scientific information layer" which integrates distributed scientific analysis with distributed processing and federated archiving and publishing. By designing new abstractions and mixing in old ones, a Science Information System with fully scalable cornerstones has been achieved, transforming data systems into knowledge systems. This break-through is facilitated by the full end-to-end linking of all dependent data items, which allows full backward chaining from the observer/researcher to the experiment. Key is the notion that information is intrinsic in nature and thus is the data acquired by a scientific experiment. The new abstraction is that software systems guide the user to that intrinsic information by forcing full backward and forward chaining in the data modelling.Comment: To be published in ADASS XVI ASP Conference Series, 2006, R. Shaw, F. Hill and D. Bell, ed

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions

Writing in Britain and Ireland, c. 400 to c. 800

No abstract available

The NMR restraints grid at BMRB for 5,266 protein and nucleic acid PDB entries

Several pilot experiments have indicated that improvements in older NMR structures can be expected by applying modern software and new protocols (Nabuurs et al. in Proteins 55:483–186, 2004; Nederveen et al. in Proteins 59:662–672, 2005; Saccenti and Rosato in J Biomol NMR 40:251–261, 2008). A recent large scale X-ray study also has shown that modern software can significantly improve the quality of X-ray structures that were deposited more than a few years ago (Joosten et al. in J. Appl Crystallogr 42:376–384, 2009; Sanderson in Nature 459:1038–1039, 2009). Recalculation of three-dimensional coordinates requires that the original experimental data are available and complete, and are semantically and syntactically correct, or are at least correct enough to be reconstructed. For multiple reasons, including a lack of standards, the heterogeneity of the experimental data and the many NMR experiment types, it has not been practical to parse a large proportion of the originally deposited NMR experimental data files related to protein NMR structures. This has made impractical the automatic recalculation, and thus improvement, of the three dimensional coordinates of these structures. We here describe a large-scale international collaborative effort to make all deposited experimental NMR data semantically and syntactically homogeneous, and thus useful for further research. A total of 4,014 out of 5,266 entries were ‘cleaned’ in this process. For 1,387 entries, human intervention was needed. Continuous efforts in automating the parsing of both old, and newly deposited files is steadily decreasing this fraction. The cleaned data files are available from the NMR restraints grid at http://restraintsgrid.bmrb.wisc.edu

The structure of the KlcA and ArdB proteins reveals a novel fold and antirestriction activity against Type I DNA restriction systems in vivo but not in vitro

Plasmids, conjugative transposons and phage frequently encode anti-restriction proteins to enhance their chances of entering a new bacterial host that is highly likely to contain a Type I DNA restriction and modification (RM) system. The RM system usually destroys the invading DNA. Some of the anti-restriction proteins are DNA mimics and bind to the RM enzyme to prevent it binding to DNA. In this article, we characterize ArdB anti-restriction proteins and their close homologues, the KlcA proteins from a range of mobile genetic elements; including an ArdB encoded on a pathogenicity island from uropathogenic Escherichia coli and a KlcA from an IncP-1b plasmid, pBP136 isolated from Bordetella pertussis. We show that all the ArdB and KlcA act as anti-restriction proteins and inhibit the four main families of Type I RM systems in vivo, but fail to block the restriction endonuclease activity of the archetypal Type I RM enzyme, EcoKI, in vitro indicating that the action of ArdB is indirect and very different from that of the DNA mimics. We also present the structure determined by NMR spectroscopy of the pBP136 KlcA protein. The structure shows a novel protein fold and it is clearly not a DNA structural mimic

Temperature synchronizes temporal variation in laying dates across European hole-nesting passerines

Publisher Copyright: © 2022 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.Identifying the environmental drivers of variation in fitness-related traits is a central objective in ecology and evolutionary biology. Temporal fluctuations of these environmental drivers are often synchronized at large spatial scales. Yet, whether synchronous environmental conditions can generate spatial synchrony in fitness-related trait values (i.e., correlated temporal trait fluctuations across populations) is poorly understood. Using data from long-term monitored populations of blue tits (Cyanistes caeruleus, n = 31), great tits (Parus major, n = 35), and pied flycatchers (Ficedula hypoleuca, n = 20) across Europe, we assessed the influence of two local climatic variables (mean temperature and mean precipitation in February–May) on spatial synchrony in three fitness-related traits: laying date, clutch size, and fledgling number. We found a high degree of spatial synchrony in laying date but a lower degree in clutch size and fledgling number for each species. Temperature strongly influenced spatial synchrony in laying date for resident blue tits and great tits but not for migratory pied flycatchers. This is a relevant finding in the context of environmental impacts on populations because spatial synchrony in fitness-related trait values among populations may influence fluctuations in vital rates or population abundances. If environmentally induced spatial synchrony in fitness-related traits increases the spatial synchrony in vital rates or population abundances, this will ultimately increase the risk of extinction for populations and species. Assessing how environmental conditions influence spatiotemporal variation in trait values improves our mechanistic understanding of environmental impacts on populations.Peer reviewe

Adaptive versus eductive learning: Theory and evidence

Adaptive and eductive learning are two widely used ways of modeling the process by which agents learn a rational expectation equilibrium (REE). In this paper we report an experiment where we exploit differences in the conditions under which adaptive and eductive learning converge to REE so as to investigate which approach provides the better description of the learning behavior of human subjects. Our results suggest that the path by which the system converges appears to be a mixture of both adaptive and eductive learning model predictions.Accepted versio

The Future of Agent-Based Modeling

In this paper, I elaborate on the role of agent-based (AB) modeling for macroeconomic research. My main tenet is that the full potential of the AB approach has not been realized yet. This potential lies in the modular nature of the models, which is bought by abandoning the straitjacket of rational expectations and embracing an evolutionary perspective. I envisage the foundation of a Modular Macroeconomic Science, where new models with heterogeneous interacting agents, endowed with partial information and limited computational ability, can be created by recombining and extending existing models in a unified computational framework