Conformational Domino: Linker modification enables control of key functional group orientation in macrocycles

This repository containts simulation data, input files and analysis notebook for the molecular dynamics simulations performed for this study.

Publication

• Link to be added

Abstract

Conformational preorganization is generally accepted as key aspect of macrocyclization, but how even minor modifications to a macrocyclic scaffold influence the conformational preorganization remains poorly understood. Here we show how macrocyclization and further derivatization of the linker region can improve affinity, selectivity, and plasma stability in a highly atom-efficient manner. A single, solvent exposed methyl group was found to improve binding affinity up to 10x over the non-methylated analog. This led to highly ligand-efficient macrocycles with a promising in vivo profile for the FK506-binding protein 51 (FKBP51), a key regulator of the human stress response. Using high-resolution co-crystal structures and molecular dynamics simulations, we found that small linker variations can be tuned to shift the orientation of a key carbonyl group into an advantageous position. This effect is specific to macrocycles, highlighting their potential for fine-tuned adjust-ments to enable desired properties.

Usage

# Install environment.
conda env create -f environment.yml
conda activate fkbp_mc_analysis

# Open /code/conformational_analysis.ipynb in your preferred Jupyter interface

Repository structure

• experimental_data contains the starting points for the computational analysis, i.e. crystal structures of compounds 15a, 29a and 29b, and the binding affinities of all compoounds.
• complex_pdb_models contains pdb files of all the protein-ligand complex models used as starting structures for the MD simulations.
• code contains:
  • GROMACS input files (topology + starting coordinates) both in water and in complex with FKBP51 for all systems, as well as MD parameters files .mdp in md_input_files
  • The conformational_analysis.ipynb notebook, a workflow that reproduces all the computational figures in the paper.
  • Python analysis functions in mc_analysis.py required by the notebook
  • Data extracted from the MD trajectories in md_data