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BioExcel is the leading European Centre of Excellence for Computational Biomolecular Research. Established in 2015, the centre has grown into a major research and innovation hub for scientific computing. BioExcel develops some of the most popular applications for modelling and simulations of biomolecular systems. A broad range of additional pre-/post-processing tools are integrated with the core applications within user-friendly workflows and container solutions.
The software stack comes with
Teams: BioBB Building Blocks
Web page: https://bioexcel.eu/
The BioExcel Building Blocks (biobb) software library is a collection of Python wrappers on top of popular biomolecular simulation tools.
The library offers a layer of interoperability between the wrapped tools, which make them compatible and prepared to be directly interconnected to build complex biomolecular workflows.
All the building blocks share a unique syntax, requiring input files, output files and input parameters (properties), irrespective of the
This tutorial aims to illustrate the process of setting up a simulation system containing a protein in complex with a ligand, step by step, using the BioExcel Building Blocks library (biobb). The particular example used is the T4 lysozyme L99A/M102Q protein (PDB code 3HTB), in complex with the 2-propylphenol small molecule (3-letter Code JZ4).
Workflow engine is a jupyter notebook. It can be run in binder, following the link given, or locally. Auxiliar libraries used are: nb_conda_kernels,
This tutorial aims to illustrate how to compute a fast-growth mutation free energy calculation, step by step, using the BioExcel Building Blocks library (biobb). The particular example used is the Staphylococcal nuclease protein (PDB code 1STN), a small, minimal protein, appropriate for a short tutorial.
Workflow engine is a jupyter notebook. Auxiliary libraries used are nb_conda_kernels, os, and plotly. Environment setup can be carried out using the environment.yml in the code