MSMSeeder

Software pipeline used to generate diverse protein structural ensembles, for the purpose of seeding multiple parallel molecular dynamics simulations, and subsequent construction of Markov state models.

Authors

• Daniel L. Parton | [email protected]
• John D. Chodera | [email protected]
• Patrick B. Grinaway | [email protected]

Overview of pipeline

1. Retrieve protein target sequences and template structures.
2. Build models by mapping each target sequence onto every available template structure, using Modeller (http://salilab.org/modeller/).
3. Filter out non-unique models (based on a RMSD cutoff).
4. Refine models with implicit solvent molecular dynamics simulation.
5. Refine models with explicit solvent molecular dynamics simulation.
6. (optional) Package and/or compress the final models, ready for transfer or for set-up on other platforms such as Folding@Home.

Manifest

scripts/ - Python wrapper scripts which accept parameters from the command-line or project metadata file

MSMSeeder/ - main code; can be used as a standalone Python library package

tests/ - to test whether the code is working correctly, run nosetests from this top-level directory

Installation

git clone https://github.com/choderalab/msmseeder.git
cd msmseeder
python setup.py install

Dependencies

• OpenMM - https://simtk.org/home/openmm
• Modeller - http://salilab.org/modeller/
• mpi4py - http://mpi4py.scipy.org/
• mdtraj - http://mdtraj.org/
• PyMOL (optional, for model alignment/visualization) - http://www.pymol.org/
• Various other Python packages commonly used in scientific computing. Recommended aproach is to install either Continuum Anaconda (https://store.continuum.io/) or Enthought Canopy (https://www.enthought.com/products/canopy/)

Basic Usage

The package can be used either by running the command-line scripts listed below, or by writing scripts to interact with the MSMSeeder Python library. Each script can be run from the command-line with a '-h' flag, which will print information on their usage. They are intended to be run in the following order:

1. InitMSMSeederProject.py
2. GatherTargets.py
3. GatherTemplates.py
4. BuildModels.py
5. RefineImplicitMD.py
6. Solvate.py
7. RefineExplicitMD.py
8. PackageModels.py

The scripts have been parallelized with MPI where useful. Furthermore, the MD refinement steps use the OpenMM simulation package, which has particularly high performance on GPUs.

Some scripts will write certain parameters to a project metadata file (named "project-data.yaml" and stored in the project top-level directory). The purpose of this is partly to keep a record of the input parameters used, but if wanted, this file can be used to provide the same input parameters the next time the same script is run. Note that input parameters read from command-line arguments take precedence over those read from the project metadata file. A template project metadata file is provided in the source code.

Sometimes it is necessary to manually override certain automatic aspects of the target and template retrieval scripts. This can be achieved using a file named 'manual-specifications.yaml', to be placed in the project top-level directory (a template is provided in the source code). Currently, this allows one to manually specify individual domain spans for targets and templates retrieved from UniProt, as well as minimum and maximum acceptable domain lengths.

Example commands

InitMSMSeederProject.py
GatherTargets.py --gather_from TargetExplorerDB --db_path ~/kinomeDB/database/database.xml
GatherTemplates.py --gather_from UniProt --uniprot_query 'domain:"Protein kinase" AND reviewed:yes' --uniprot_domain_regex '^Protein kinase(?!; truncated)(?!; inactive)' --structure_paths ~/pdbfiles ~/siftsfiles
BuildModels.py --targets ABL1_HUMAN_D0
RefineImplicitMD.py --targets ABL1_HUMAN_D0
Solvate.py --targets ABL1_HUMAN_D0
RefineExplicitMD.py --targets ABL1_HUMAN_D0
PackageModels.py --targets ABL1_HUMAN_D0 --package_for FAH

Additional notes on scripts

(run each script with '-h' flag for general info)

GatherTargets.py and GatherTemplates.py

These scripts are used to provide target data (ids and sequences) and template data (ids, sequences and structures). Two different methods are provided for each script, specified with a command-line flag:

• --gather_from TargetExplorerDB
  • Retrieves data from an existing TargetExplorer database
• --gather_from UniProt
  • retrieves data from the UniProt web server, as specified by two additional flags:
  • --uniprot_query
    • Specifies the search string used to select UniProt entries
    • This uses the same syntax as the search function on the UniProt site; not all types of syntax may be supported, but most basic searches will work.
    • Note that this flag used on its own will return UniProt entries (i.e. full-length proteins)
  • --uniprot_domain_regex
    • Pass a regular expression to this flag to subselect only certain target/template domains

It should also be noted that the output from these two scripts is simply a list of target sequences (targets/targets.fa), a list of template sequences (templates/templates.fa), and a set of template structures (templates/structures/[id].pdb). The subsequent model-building and refinement scripts can thus be run using any set of data specified in this way, allowing the GatherTargets.py and GatherTemplates.py scripts to be bypassed or modified if necessary. The main restrictions are that modified residues (e.g. phosphotyrosines) cannot be included, and that ids and residues in the templates.fa file must match with the template structure files.