Discovering hidden relationships in crystallographic databases

The wealth of crystallographic data, as any other data in modern world, grows at an enormous pace. The question is how to transfer that data into new knowledge. There are a number of resources available that allow users to more easily access and interpret the vast amounts of data available for molecular structures, such as PDBe and PDBe Knowledge-Base. These resources help users to extract knowledge from the underlying data, by the development of tools to more easily visualise and interpret 3D structure data. Furthermore, the integration of data from multiple resources allows access to enriched information to increase the knowledge that can be derived from these structures.

For more bespoke and in-depth analysis, skills in programmatic access methods can allow researchers to do much more at larger scales. Nowadays, major crystallographic databases such as PDB and CSD allow one to access their data in a programmatic manner via their exposed APIs, which leads to completely new ways to exploit the information contained in them. One example could be uncovering the phenomenon of allostery in enzymes by machine learning algorithms, leading to the discovery of hidden allosteric pathways. The lecture will be given in form of interactive presentations where the participants can be directly involved in trying out and tweaking the presented programmes and algorithms.