lwalkm-simura-works 0.1.7

lwalkm

lwalkm is an integrated protein morphing pipeline designed for research in structural biology. It combines several steps into one tool:

1. Local Interpolation ("Local Walk")
   Optionally, generate intermediate structures by interpolating between two protein configurations over a user‐defined set of residues. Each interpolated structure is refined using GROMACS.

2. CorrectionNet/NEB Optimization
   The refined interpolation frames (or preexisting ones matching the pattern localwalk_*_refined.pdb) are loaded as a baseline pathway ("beads on a string"). A CorrectionNet neural network is then pre‐trained and integrated with a NEB (Nudged Elastic Band) framework—using cyclic simulated annealing and a SAC (Soft Actor-Critic) agent—to drive the pathway toward a lower–energy transition.

3. Final Minimization
   Finally, a separate GROMACS minimization pipeline can be run on each final image, with energy extraction and plotting.

All key parameters (for interpolation, CorrectionNet training, simulated annealing, etc.) are configurable via command-line flags.

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Installation

Installing from PyPI

If you want to install lwalkm from PyPI, simply run:

pip install lwalkm-simura-works

After installation, the console script lwalkm will be available in your environment’s PATH.

Installing from Source

If you prefer to build and install from source, follow these steps:

1. Install Build Tools:
   Ensure you have up-to-date pip, setuptools, and wheel:

   python3 -m pip install --upgrade pip setuptools wheel
   

2. Build the Package:
   From the project root (where pyproject.toml is located):

   python3 -m build
   

   This will create a dist/ folder with your distribution files (e.g., a .whl file).

3. Install Locally:
   To install the package, run:

   python3 -m pip install .
   

   After installation, a console script named lwalkm will be available in your environment’s bin directory.

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Usage

Once installed, you can run the pipeline from the command line. Here are some examples and troubleshooting tips:

Primary Command

If your environment is configured correctly and the lwalkm script is in your PATH, run:

lwalkm -conf1 model_01A.pdb -conf2 model_01B.pdb --interpolate --residues "1-10"

This command:

• Loads the endpoints (model_01A.pdb and model_01B.pdb).
• Performs local interpolation over residues 1–10.
• Uses default parameters for all other settings.

Including Final Minimization

To also run the final GROMACS minimization pipeline, include the --run_minimizer flag:

lwalkm -conf1 model_01A.pdb -conf2 model_01B.pdb --interpolate --residues "1-10" --run_minimizer

Alternate Command Options

If the lwalkm command is not recognized, try one of these alternatives:

• Run via the Python Module:
  You can run the pipeline directly as a module:

  python3 -m lwalkm.pipeline -conf1 model_01A.pdb -conf2 model_01B.pdb --interpolate --residues "1-10" --run_minimizer
  

• Check Your PATH:
  If using a virtual environment, ensure its bin directory is in your PATH:

  source path/to/your/venv/bin/activate
  lwalkm --help
  

No Interpolation Mode

If you prefer not to perform interpolation, ensure that refined files (named localwalk_*_refined.pdb) already exist in your working directory, then run:

lwalkm -conf1 model_01A.pdb -conf2 model_01B.pdb --run_minimizer

In this mode, the pipeline detects the number of images from your existing files (with a fallback default of 16 via the -nimages flag).

Full Example with Additional Parameters

For a complete run with additional customization, try:

lwalkm -conf1 model_01A.pdb -conf2 model_01B.pdb --interpolate --residues "1-313" --steps 10 --run_minimizer

This command:

• Uses model_01A.pdb and model_01B.pdb as endpoints.
• Performs interpolation over residues 1–313 (generating 11 frames: 10 steps + starting configuration).
• Runs the final GROMACS minimization pipeline.
• Uses default values for other parameters (e.g., pretraining epochs, simulated annealing settings, etc.).

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Troubleshooting Tips

• Command Not Found:
  If the lwalkm command is not recognized, ensure your package is installed and your virtual environment’s bin directory is in your PATH. Alternatively, run the module directly:

  python3 -m lwalkm.pipeline --help
  

• Python Version & Virtual Environment:
  Verify that you’re using the correct Python interpreter (e.g., python3) and that your virtual environment is activated.

• Help and Parameters:
  To see all available command-line options:

  lwalkm --help
  

  or

  python3 -m lwalkm.pipeline --help
  

• Final Minimization:
  To include the final minimization step, make sure to add the --run_minimizer flag to your command.

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Let me know if you need any further adjustments or additional help!

Default Parameters

If you omit any flag, the following defaults are used:

Flag	Default Value	Description
Endpoints
-conf1 / -conf2	(Required)	Protein A and B PDB files (must supply).
Interpolation
--interpolate	False	Whether to perform local interpolation.
--residues	None	Global residues to interpolate (required if using --interpolate).
--chain_residues	None	Chain-specific residues, e.g., "A:1-10,B:20-30".
--steps	10	Number of interpolation steps (produces steps + 1 frames).
--chains	None	Comma-separated chain IDs (if omitted, uses all common chains).
--output_prefix	"localwalk"	Prefix for interpolation output files.
Pipeline
-nimages	16	Expected number of images if not interpolating (the code counts existing localwalk files).
-pretrain_epochs	200	CorrectionNet pre-training epochs.
-sac_timesteps	50	Total timesteps for the SAC agent in the NEB environment.
--sa_steps	1000	Number of simulated annealing steps in the NEB environment’s refinement routine.
--max_steps	50	Maximum steps per episode in the NEB environment.
Cyclic SA (Pre-training)
--sa_cycles	3	Number of heat/cool cycles when cyclic SA is triggered.
--sa_steps_per_cycle	300	Steps per cycle during cyclic SA.
--sa_T_high	400	High temperature (K) for cyclic SA.
--sa_T_low	50	Low temperature (K) for cyclic SA.
--sa_update_frequency	5	Frequency (in epochs) to run cyclic SA during CorrectionNet pre-training.
CorrectionNet Hyperparameters.
--lr	0.0005	Learning rate for the CorrectionNet Adam optimizer.
--sigma	0.05	Standard deviation of noise added during CorrectionNet pre-training.
L-BFGS-B Minimization
--lbfgs_maxiter	5000	Maximum iterations for SciPy’s L‑BFGS‑B minimizer per final image.
--lbfgs_disp	False	If specified, displays L‑BFGS‑B optimizer output.
Final GROMACS Minimizer
--run_minimizer	False	If true, runs the final GROMACS minimizer pipeline on each final structure.

Key Points:

• If you omit a flag, its default is used.
• Endpoints (-conf1 and -conf2) are required.
• If you do not specify --interpolate, you must have preexisting localwalk_*_refined.pdb files.
• The package is designed so that you can start with a minimal run and then tweak parameters as needed.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! If you encounter bugs, have feature requests, or want to improve the code, please open an issue or submit a pull request on our GitHub repository.

Repository: https://github.com/simura-works/lwalkm_simura-works

Contact

For questions or suggestions, please contact Bernard Kwadwo Essuman (mailto:bessuman.academia@gmail.com).