Workshop on Integrative modelling with HADDOCK
The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modelling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. We have developed for this purpose a versatile information-driven docking approach HADDOCK (https://www.bonvinlab.org/software) available as a web service at https://wenmr.science.uu.nl/haddock2.4. HADDOCK can integrate information derived from biochemical, biophysical or bioinformatics methods to guide the modelling.
This workshop will consist of lectures and computer tutorials during which some of the recent HADDOCK developments will be discussed and demonstrated in hands-on computer tutorials. In particular we will introduce HADDOCK3, the new modular version of HADDOCK which represents a redesign of the HADDOCK2.X series, implementing new ways to interact with the HADDOCK sub-routines and offering more customization to the end user. Participants are encouraged to bring their own problems to get advice on the best modelling strategy.
The workshop will consist of lectures by Prof. Alexandre Bonvin and guided computer tutorials during which participants will get experience in using both the HADDOCK2.4 web service and the new modular HADDOCK3 version (command line). For these participants are required to bring their own laptops. Knowledge of Linux and command line is required.
Workshop on Novel Computational Methods for Structural Biology
In this special session three invited talks will be presented, which span from the novel mathematical approaches for dealing with the distance geometry problems for determining the protein structures, their new implementation on the GPU platform; and then a novel statistical mechanics-based enhanced sampling molecular dynamics simulation approach for evaluation of binding free energy; and finally to the quantum machine learning for structure-based computational drug discovery.