The GitHub Page of the Liedl Lab at the University of Innsbruck
This webpage hosts content for some of our GitHub repositories, most notably the latest version of the GIST Tutorial.
Our repositories
- liedllab.github.io
Contains the code to host and render this website.
For the post-analysis of molecular dynamic (MD) simulations of biomolecules:
- X-Entropy
Python 3 package to calculate entropy using a KDE with automatic bandwidth prediction. Primarily used to calculate the dihedral entropy of sidechain movements in biomolecular MD simulations, but applicable to any data.
Cite Kraml, Johannes, et al. “X-Entropy: A parallelized kernel density estimator with automated bandwidth selection to calculate entropy.” Journal of chemical information and modeling 61.4 (2021): 1533-1538. - GetContacts_analysis
Python 3 package to easily analyse and visualise the output of the get_contacts tool.
Cite Fernández-Quintero, Monica L., et al. “The influence of antibody humanization on shark variable domain (VNAR) binding site ensembles.” Frontiers in Immunology 13 (2022): 953917.. - OCD (Orientation of cylindrical domains)
Python 3 script to analyse the orientation of biomolecule dimers during an MD simulation by inscribing a coordinate system based on each monomers moment of inertia.
Cite Hoerschinger, Valentin J., et al. “OCD. py-Characterizing immunoglobulin inter-domain orientations.” bioRxiv (2021): 2021-03.
For the analysis of biomolecule structures:
- surface_analyses / PEP-Patch
Python 3 toolbox to calculate surface patches from biomolecule structures, either for hydrophobic or electrostatic surface properties. Also contains a variety of other surface hydrophobicity related tool implementations.
Cite Hoerschinger, Valentin J., et al. “PEP-patch: electrostatics in protein–protein recognition, specificity, and antibody developability.” Journal of Chemical Information and Modeling 63.22 (2023): 6964-6971. - TopModel
Python 3 tool to inspect of structural issues in biomolecular structure, e.g. to assess ML modelling outputs.
Cite Fernández-Quintero, Monica L., et al. “Challenges in antibody structure prediction.” MAbs. Vol. 15. No. 1. Taylor & Francis, 2023.
For Grid Inhomogeneous Solvation Theory:
- gist_tutorial
Recent tutorial on how to run GIST calculations, which is an MD-based analysis of spatially resolved solvation enthalpy and solvation entropy. A pdf of the tutorial is available here, while the scripts and files are found in the repository.
Currently in review. - gisttools
Python 3 toolkit to work with grid data as generated by GIST, including various post-analysis methods useful for working with GIST data.
- second_disorder
Python 3 implementation of 1st- and 2nd- order entropies for GIST, especially useful to calculate entropies for co-solvents like ions.
Cite Waibl, Franz, et al. “Explicit solvation thermodynamics in ionic solution: extending grid inhomogeneous solvation theory to solvation free energy of salt–water mixtures.” Journal of Computer-Aided Molecular Design (2022): 1-16. - md-setup-gist-cross-solvation
SI data (setup files) for Waibl, Franz, et al. “Grid inhomogeneous solvation theory for cross-solvation in rigid solvents.” The Journal of Chemical Physics 156.20 (2022).
- gigist
C++ / CUDA implementation of GIST on Nvidia GPUs in ambertools’ cpptraj. Now obsolete, since it was included in the cpptraj code base.
Cite Kraml, Johannes, et al. “Solvation free energy as a measure of hydrophobicity: application to serine protease binding interfaces.” Journal of chemical theory and computation 15.11 (2019): 5872-5882.
For more info
Check out our official webpage liedllab.org for a list of our team members, recent publications, and more!
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