Strategies in the Computational Modelling of Biological Systems: Case Studies with Radical Enzymes

The research conducted in this thesis is based on the application of modern computational techniques to model biological systems. In particular, the focus of the thesis is oriented toward better understanding of structural and functional properties of the selected radical enzymes – pyruvate formate-lyase and (6-4) photolyase. The specific approaches that were used in the current thesis range from the calculation of spectroscopic parameters and pKa values to the construction of potential energy surfaces for chemical reactions and free energy changes associated with structural rearrangements. With such a multifaceted approach, it was possible to address a series of unresolved mechanistic issues relevant to the systems of interest. In particular, the use of a range of techniques enabled the investigation of the interconnectedness of processes taking place at different time and length scales. These techniques include very accurate ab initio methods, based on quantum mechanics, and coarser descriptions of the system relying on the classical mechanics, such as molecular dynamics and implicit solvation models. As molecular modelling continues to develop as an independent discipline, one can expect an ncreased number of similar applications, in which targeted techniques are combined to provide answers to questions of biological relevance

Sharing data from molecular simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations

Sharing Data from Molecular Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simu..