1. IFP analysis of dissociation trajectories for 3 compounds of HSP90 is implemented in IFP_generation_examples_Analysis.ipynb
   is reported in D. B. Kokh, B. Doser, S. Richter, F. Ormersbach, X. Cheng , R.C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" (2020) submitted to JCP

Authors and Contributors:

• Daria Kokh
• Fabian Ormersbach - preprocessing PDB files using Chimera (Process_pdb.py, chimera_hydrogen_mol2.py; test examples revised)

[email protected]

Heidelberg Institute of Theoretical Studies (HITS, www.h-its.org)

Schloss-Wolfsbrunnenweg 35

69118 Heidelberg, Germany

This open source software code was developed in part in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 785907 (Human Brain Project SGA2).

Python 3.x

Python Libraries: numpy; pandas; matplotlib; MDAnalysis; seaborn; RDkit; sklearn; scipy; ngview

Chimera - only for the scripts used for preprocessing pdb files (structure protonation and generation of the ligand mol2 file); not required if protonation and mol2 file are already prepared by a user)

Codes were written on Python 3.x and tested on Python 3.7

To configure environment in anaconda use: conda env create -f IFP_trajectory.yml

IFP_generation.py - functions for generation of IFPs

Membrane_analysis.py - functions for analysis of membrane-protein systems

Clustering.py - functions for analysis of trajectories using IFP data (is still under developments)

Process_pdb.py - preprocessing PDB files (splitting into ligand and protein files)

chimera_hydrogen_mol2.py - generation of ligand mol2 file

Membrane_analysis.py - computation of the membrane surface area per lipid and membrane/protein/water atom density distribution

1. Generation of the IFP databease for a single MD trajectory of a protein-ligand complex
2. Analysis and visualization of a set of IFP databases for different ligand

can be downloaded from https://zenodo.org/record/3981155#.XzQEUCgzaUk

Protein-Ligand Interaction Fingerprint (IFP) computations (only functions of IFP_generation.py are used) for:

1. a single structure prepared for MD simulations (HSP90; PDB ID 6EI5, dcd format)
2. a trajectory (for selected frames; dcd format)
3. a PDB structure

Generation and analysis of IFPs for conventional MD simulations and for RAMD trajectories for Muscarinic Receptor M2 in a membrane. In this example, Trajectories.py is used for pre-processing trajectories and IFP_generation.py is used for computing IFPs

1. Computing IFPs for a single equilibration trajectory (dcd format)
2. Computing IFPs for a set of trajectories: system equilibration and ligand dissociation (RAMD) trajectories (dcd format) Illustration of PL IFP variation in one of the dissociation trajectories of iperoxo bound to muscarinic receptor M2 .

This example shows how RAMD dissociation trajectories can be analyzed using pre-generated IFP databases

This plot illustrates ligand dissociation pathways in a graph representation derived from clustering ligand trajectories in IFP space and plotting them with respect to the ligand COM from the initial bound position.

Exploring the behavior of a membrane-protein system in MD simulations. Lipid density is computed in the X/Y plane Illustratration of the analysis of the muscarinic receptor M2 GPCR embedded in a mixed membrane with 50% cholesterol content

Example of the Membrane/protein 2D images in the xy plane (two upper plots)