AlloCraft

AlloCraft is a two-step process that aims to modify allosteric behavior in proteins via amino acid design.

The first step of AlloCraft consists in predicting allosteric properties of your protein of interest by analyizing molecular dynamics (MD) simulations. Allosteric coupling is divided into a dynamic and structural element: dynamic allostery is infered by calculating mutual information (MI) for each pair of residues, followed by construction of residue networks (Pathways). The construction maximizes the MI along the path while minimizing its distance (i.e. number of residues connecting the distant pair). Constructed pathways are then clustered into allosteric pipelines. To infer allosteric structural perturbations, dihedral differences are calculated using Kullback-Liebler divergences between a reference and a target simulation. If the reference and target simulation correspond to an apo and ligand-bound states, respectively, then KLdiv will report the structural perturbations induced by ligand binding across the entire receptor. This allosteric analysis has been applied to GPCR-ligand systems, but could be applied to any other protein as well. The second step is the design step: design hotspots inferred from step 1 are mutated to all 20 possible amino acids using RosettaMembrane and assessing mutation stability using Rosetta scoring and coarse grained dynamical coupling using dynamic cross-correlation maps calculated using normal mode analysis. Final amino acid designs are chosen based on both stability and correlated dynamics scores.

Step 1: Analysis of molecular dynamics simulations to extract allosteric properties:

This step offers 3 functionalities: md2path, kldiv, and meta analyses. Architecture of the code can be found in reference.md

• md2path is the main function that extracts allosteric pathways from MD trajectories of a given protein state using mutual information calculated from internal coordinates, all while providing a wide array of analysis methods along the way:

  1. Transform xtc to dcd (if needed)
  2. Fetch reference PDBs from database, and then align sequences and input PDBs to reference PDBs
  3. Load and align trajectories
  4. Calculate RMSD and RMSF of protein and ligand (if present)
  5. Calculate contact map with ligand and/or effector protein (if they exist)
  6. If effector protein is not present in the simulation but is present in reference PDB, contact map is calculated from reference PDB
  7. Calculate GPCR order parameters for activation states (if protein is a GPCR)
  8. PCA of ligand binding poses followed by clustering
  9. Calculate dihedral time series from trajectories (phi, psi, and chiX)
  10. Calculate 1st and 2nd order entropies from dihedrals, followed by mutual information (MI)
  11. Assess convergence of entropies
  12. Run allosteric pathway calculation

• kldiv compares a test ensemble to a reference ensemble at a degree of freedom basis by calculating Kullback−Leibler divergences (KLdiv) between dihedrals:

  1. Fetch reference PDBs, and then align sequences and input PDBs to reference PDBs
  2. Load and align trajectories of test and reference ensembles
  3. Calculate dihedrals from trajectoriess (assumes same receptor IDs between entries)
  4. Reconcile dihedrals between test and reference systems (in case there are mutated residues with different dihedrals)
  5. Calculate 1st order KLdiv
  6. Calculate 2nd order KLdiv and mutual divergence (optional, and very slow, and not very informative)
  7. Plot dihedral distributions with highest divergences
  8. Visualize KLdivs residue-wise

• meta functions analyze the output of md2path or kldiv for a set of systems/proteins. Refer to individual scripts for more details

Installation:

Requirements:

• Matlab 2021b or newer
• Matlab Bioinformatics toolbox
• MDprot package
• VMD

The software has been tested with:

• Windows 11 version 21H2
• Ubuntu 18.04
• Matlab R2021b
• VMD 1.9.3

Download MDprot and add it to your Matlab path. Downloading can be done manually from the aforementioned link or in the command line via:

git clone https://github.com/mahdiofhijaz/MDprot.git

Adding to path in Matlab can be done in more than one way:

• Per session: https://ch.mathworks.com/help/matlab/ref/addpath.html
• Permanently at startup: https://ch.mathworks.com/help/matlab/matlab_env/add-folders-to-matlab-search-path-at-startup.html

Next, get the Bioinformatics Toolbox for Matlab, more details regarding add-on management can be found here: https://ch.mathworks.com/help/matlab/matlab_env/get-add-ons.html

Finally download MD_simulation_analysis folder either manually from this repo or by cloning it

For the smoothest experience, AlloCraft also uses visual molecular dynamics (VMD) to transform .xtc files to .dcd and to predict secondary structure of a protein. You can download it for free from here: https://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD

Don't forget to add VMD to your path variable too!

You can run the main scripts without having VMD installed if you provide .dcd trajectories instead of .xtc and if you provide helices variable in the input scripts (input_md2path.m and input_kldiv.m).

Instructions:

All the input parameters needed for the analysis are provided in input_md2path.m for md2path and input_kldiv.m for kldiv. Copy the corresponding input script to your local directory and set up the input parameters. All parameters are explained in the input scripts.

Create a PDB file with separate chains for receptor, ligand, and G-protein/effector site, and define the chains in the input script.

NOTE: all input residues should be in the same pdb numbering as the input PDB

The script takes in either .xtc or .dcd trajectory files, and every trajectory file needs to be in its own directory (runN, where N is the number of the run). Prepare your directory in the following manner:

Main directory: prot.pdb (your pdb file)

• run1/ -> traj.dcd OR traj.xtc (your trajectory files)
• run2/ -> traj.dcd OR traj.xtc
• .
• .
• .
• runN/ -> traj.dcd OR traj.xtc

Where run1->N are directories that contain your trajectories

Run input_md2path.m or input_kldiv.m

Run md2pathMain.m or kldivMain.m. All the output can be found in a folder called md2pathdev created in your defined directory in your input script. To run pathway calculation for individual trajectory clusters, setup and run pca_1b_ligandPathCalcClusters.m after the PCA and dihedrals sections in md2pathmain.m

Typical runtime for calculations:

• md2path: ~ 1 hour from start to finish on a typical PC and for around 15000-20000 frames of trajectory of a 300-400 residue GPCR
• kldiv: a few minutes assuming you only calculate 1st order KL divergences

Acknowledgements:

• Mutual information statistical filtering: McClendon et al., J Chem Theory Comput (2009)
• Using Kullback−Leibler divergences to compare conformational ensembles: McClendon et al., J Chem Theory Comput (2012)
• Clustering mutual information into allosteric pathways: Bhattacharya and Vaidehi, Biophy J (2014); Nivedha et al., Mol Pharmacol (2018)
• MDToolbox for Matlab: Matsunaga, and Sugita, J Chem Phys (2018)

Step 2: Amino acid design to modify allosteric behavior

This is the second step of the process, where we will do in silico mutagenesis to the hubs that we extracted from step1, followed by coarse grained dynamical coupling calculations.

Installation:

Requirements: Rosetta software suit (https://www.rosettacommons.org/software/license-and-download) Python (tested with python 3.6.7 and pandas 0.23.4) R (bio3D package: http://thegrantlab.org/bio3d/)

Instructions:

The first part is performing in silico mutagenesis to specific residues, which will be completed using RosettaMembrane: For this step, we will need an input PDB and a span file that specifies the position of the membrane, for reference on how to prepare input files for membrane simulations with Rosetta: https://www.rosettacommons.org/docs/latest/application_documentation/membrane_proteins/RosettaMP-GettingStarted-PreparingInputs#span-files

The python scripts provided will take care of running RosettaMembrane in an automated fashion.

We then extract the lowest energy pdbs from each mutagenesis and use as input for stability and dynamical coupling calculations. The R scripts will perform NMA to extract DCCM and will extract energy differences upon mutation from the mutant PDB files.

More details can be found in the demo.

Acknowledgements:

• bio3D from the Grant lab

Contact: mahdi.hijazi@epfl.ch (2024)