Computational Resources for Bioscience Education

With the ongoing laboratory restrictions, it is often challenging for bioscience students to make satisfactory progress in their projects. A long-standing practice in multi-disciplinary research is to use computational and theoretical method to corroborate with experiment findings. In line with the lack of opportunity to access laboratory instruments, the pandemic situation is a win-win scenario for scholars to focus on computational methods. This communication outline some of the standalone tools and webservers that bioscience students can successfully learn and adopt to obtain in-depth insights into biochemistry, biophysics, biotechnology, and bioengineering research work.TU Berlin, Open-Access-Mittel – 202

Investigation of molecular interactions of prenylflavonoids at GABAA receptor subtypes

Prenylated flavonoids derived from Hops (Humulus lupulus) activate the γ-aminobutyric acid (GABA) type A receptors through positive and negative modulation. Currently, these compounds’ binding site at the different GABAAR subtypes is still unknown. Molecular interactions of several prenylated flavonoids were investigated at different GABAAR binding sites. The focus was on the receptor subtypes containing αβγ and αβδ subunits and the aim was to identify the most likely binding site for the prenylflavonoids by studying the relation between a ligand structure and the residues defining its putative pocket and the ligand’s calculated binding affinity. Available GABAAR crystal structures were obtained from the Protein Data Bank, and a comparative model of the α6β3δ receptor subtype was built using the MODELLER software. The compounds were docked at the putative binding sites of the studied GABAAR subtype structures with the GLIDE tool of the Maestro molecular modeling package. An estimate of the free energy of binding was calculated with the Prime/MMGBSA tool of Maestro for all the docked receptor-ligand complexes. The obtained results suggest that prenylflavonoids may bind to more than one pocket in the extracellular domain of the studied GABAAR subtypes. It was not possible to distinguish high affinity binding sites from low affinity binding sites as the docking results varied for each compound in the studied pockets. Discrepancy in results is likely caused by modeling binding site without knowing the correct conformations of the side chains forming the pockets. Based on the modelled α6β3δ subtype, β3δ interface may be the most likely binding site for the hops compounds. To determine the binding of the prenylated flavonoids most accurately, experimental structure determination by X-ray crystallography could be attempted

Generic Metadata Handling in Scientific Data Life Cycles

Scientific data life cycles define how data is created, handled, accessed, and analyzed by users. Such data life cycles become increasingly sophisticated as the sciences they deal with become more and more demanding and complex with the coming advent of exascale data and computing. The overarching data life cycle management background includes multiple abstraction categories with data sources, data and metadata management, computing and workflow management, security, data sinks, and methods on how to enable utilization. Challenges in this context are manifold. One is to hide the complexity from the user and to enable seamlessness in using resources to usability and efficiency. Another one is to enable generic metadata management that is not restricted to one use case but can be adapted with limited effort to further ones. Metadata management is essential to enable scientists to save time by avoiding the need for manually keeping track of data, meaning for example by its content and location. As the number of files grows into the millions, managing data without metadata becomes increasingly difficult. Thus, the solution is to employ metadata management to enable the organization of data based on information about it. Previously, use cases tended to only support highly specific or no metadata management at all. Now, a generic metadata management concept is available that can be used to efficiently integrate metadata capabilities with use cases. The concept was implemented within the MoSGrid data life cycle that enables molecular simulations on distributed HPC-enabled data and computing infrastructures. The implementation enables easy-to-use and effective metadata management. Automated extraction, annotation, and indexing of metadata was designed, developed, integrated, and search capabilities provided via a seamless user interface. Further analysis runs can be directly started based on search results. A complete evaluation of the concept both in general and along the example implementation is presented. In conclusion, generic metadata management concept advances the state of the art in scientific date life cycle management