arXiv subjects

Jean-Noël Grad

Publications and source records attributed to Jean-Noël Grad.

5 recordsLinked to original sources

arXiv2025

Foundational Competencies and Responsibilities of a Research Software Engineer: Current State and Suggestions for Future Directions

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of fundamental competencies as well as values that define the general profile of an RSE. On this basis, we elaborate on the progression of these skills along different dimensions, looking at specific types of RSE roles, proposing recommendations for organisations, and giving examples of future specialisations. An appendix details how existing curricula fit into this framework.

cs.SE↗

arXiv2024

pyMBE: the Python-based Molecule Builder for ESPResSo

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software to design custom Coarse-Grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of \espresso offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is error-prone, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in \espresso using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems which contain several types of reactive groups is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in \espresso, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE) which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.

cond-mat.soft↗

arXiv2020

Modeling the Current Modulation of Bundled DNA Structures in Nanopores

We investigate the salt-dependent current modulation of bundled DNA nanostructures in a nanopore. To this end, we developed four simulation models for a 2x2 origami structure with increasing level of detail: from the mean-field level to an all-atom representation of the DNA structure. We observe a consistent pore conductivity as a function of salt concentration for all four models. However, a comparison of our data to recent experimental investigations on similar systems displays significant deviations. We discuss possible reasons for the discrepancies and propose extensions to our models for future investigations.

cond-mat.soft↗

arXiv2020

An Environment for Sustainable Research Software in Germany and Beyond: Current State, Open Challenges, and Call for Action

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

cs.GL↗

arXiv2017

Locating large flexible ligands on proteins

Many biologically important ligands of proteins are large, flexible, and often charged molecules that bind to extended regions on the protein surface. It is infeasible or expensive to locate such ligands on proteins with standard methods such as docking or molecular dynamics (MD) simulation. The alternative approach proposed here is the scanning of a spatial and angular grid around the protein with smaller fragments of the large ligand. Energy values for complete grids can be computed efficiently with a well-known Fast Fourier Transform accelerated algorithm and a physically meaningful interaction model. We show that the approach can readily incorporate flexibility of protein and ligand. The energy grids (EGs) resulting from the ligand fragment scans can be transformed into probability distributions, and then directly compared to probability distributions estimated from MD simulations and experimental structural data. We test the approach on a diverse set of complexes between proteins and large, flexible ligands, including a complex of Sonic Hedgehog protein and heparin, three heparin sulfate substrates or non-substrates of an epimerase, a multi-branched supramolecular ligand that stabilizes a protein-peptide complex, and a flexible zwitterionic ligand that binds to a surface basin of a Kringle domain. In all cases the EG approach gives results that are in good agreement with experimental data or MD simulations.

q-bio.BM↗