MOTIVATION: Molecular dynamics simulation is very useful but computationally demanding method of studying dynamics of biomolecular systems. Many enhanced sampling methods were developed in order to obtain the desired results in available computational time. Metadynamics and its variants are common enhanced sampling methods used for this purpose. Metadynamics simulations allow the user to gather large amounts of data, which have to be analyzed to elucidate the properties of the studied system. RESULTS: Here, we present metadynminer.py, a Python package that allows easy and user-friendly analysis and visualization of the results obtained from metadynamics simulations. The built-in functions automate frequent tasks and make the package easy to use for new users, while its many customization options and object-oriented nature allow for integration into specialized data analysis workflows by more advanced users. AVAILABILITY AND IMPLEMENTATION: The "metadynminer.py" Python package is available under the GPL-3.0 license via PyPi and Conda. The development version is available on GitHub along with issue support (https://github.com/Jan8be/metadynminer.py). Documentation, tutorial and Jupyter Notebook (provided through the public mybinder.org service) are available at https://metadynreporter.cz.

Interactions between large biomolecules and smaller bio-active ligands are usually studied through a process called docking. Its aim is to find an energetically favorable orientation of a ligand within an active site of abiomolecule. Chemical reactions take place in active siteand the role of the ligand is either to speed up, slow down or change the reaction (e.g., an enzyme catalyzed hydrolysis), which is why it can have huge pharmaceutical or other commercial impact. We present a tool that supports effective management and control of a typical workflow of docking parametric study. Selected subsets of ligands and protein trajectory snapshots can be displayed in three different views and further analyzed. Finally, the application supports spawning and steering underlying computations running on the Grid.

Abstract Job Provenance (JP), part of the gLite Grid middleware, is a service that keeps long‐term trace on completed computations for further reference. It is a job‐centric service, keeping records about job life cycle, its environment, inputs/outputs, user parameters, and annotations. During the first provenance challenge, we explored the relation between a specific job‐centric Grid‐oriented provenance and a more general data provenance approach. The challenge represents a use case which emphasizes fields that were not priorities in the original JP design. However, we proved that the design is sufficiently general to cope with this mode of use. We also identified several areas where it is feasible to extend the current implementation. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

The Testbed and Applications working group of the European Grid Forum (EGrid) is actively building and experimenting with a grid infrastructure connecting several research-based supercomputing sites located in Europe. The paper reports on our first feasibility study: running a self-migrating version of the Cactus simulation code across the European grid testbed, including "live" remote data visualization and steering from different demonstration booths at Supercomputing 2000, in Dallas, TX. We report on the problems that had to be resolved for this endeavour and identify open research challenges for building production-grade grid environments.