Tutorial#

Currently, only the tutorial of absolute binding free energy (ABFE) calculation for the membrane protein system is available. More tutorials will be added in the future.

Absolute Binding Free Energy (ABFE) Workflow with `batter`#

This tutorial walks through a membrane ABFE run powered by batter. The workflow applies λ-dependent Boresch restraints, uses the simultaneous decoupling/recoupling (SDR) protocol with both interacting and dummy ligands present, and relies on softcore electrostatics/van der Waals potentials so the entire calculation completes in a single leg. We reference examples/mabfe_example.yaml so you can reproduce the run locally before adapting it to your own system.

Quick walkthrough#

batter orchestrates an end-to-end AMBER ABFE workflow that starts from protein + embedded protein-membrane system (if applicable) + ligand(s) (3D coordinates) overlayed to the protein binding site. The main steps are:

system staging and loading – A executon folder will be created under <run.output_folder>/executions/ to hold all intermediate files, logs, and results. If a run ID is not provided, a timestamp-based unique ID is generated. If the same run ID already exists, the execution is resumed from the last successful step.
Ligand parameterisation – supports both GAFF/GAFF2 and OpenFF force fields with options to choose charges (AM1-BCC by default)
Equilbration system preparation – builds solvated/membrane-embedded systems with the ligand in the binding site.
Equilibration – Steps to run before FE production run. During this phase, the ligand and protein are not restrained (unless explicitly configured). If the ligand unbound from the binding site during equilibration, the run is marked as unbound and skipped during FE production.
Equilibrium analysis - Find a representative frame from the equilibrated trajectory to start the FE windows from. RMSD analysis is also performed and saved in the equil folder. Adjust the bound/unbound cutoff via fe_sim.unbound_threshold if your system requires a different distance threshold.
FE window generation and submission – λ windows are created based on the configuration.
FE equilbration - very short equilibration runs to allow water relaxation. If flag --only-equil is provided, the workflow stops after this step.
FE production runs – Each window is submitted as an independent SLURM job. The main process monitors job status and streams updates to the terminal. Set run.max_active_jobs in your YAML (default 1000, 0 disables throttling) to cap how many SLURM jobs Batter keeps active at once and avoid overloading the scheduler.
Analysis – Once all windows complete, MBAR analysis is performed and results are summarised in CSV/JSON formats with convergence plots. The worker pool for this stage follows run.max_workers; optionally limit the trajectory range per window via fe_sim.analysis_fe_range ([start, end] defaults to [2, -1] or [0, -1] when num_fe_extends < 4).

Installation#

(Optional) set a persistent pip cache (helpful on shared clusters):
```
export PIP_CACHE_DIR=$SCRATCH/.cache
```

Clone the repository with ssh (or HTTPS if SSH is unavailable) and initialize submodules:

git clone git@github.com:yuxuanzhuang/batter.git
# If SSH is unavailable, use HTTPS instead:
# git clone https://github.com/yuxuanzhuang/batter.git
# For SSH setup tips:
# https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account

cd batter
git submodule update --init --recursive

Create and activate a Conda environment (with environment.yml):

conda create -n batter_env python=3.12 -y
conda env update -n batter_env -f environment.yml
conda activate batter_env

Install editable copies of the bundled dependencies plus batter itself:

pip install -e ./extern/alchemlyb
pip install -e ./extern/rocklinc
pip install -e .

Verify the installation:
```
batter --help
```

Preparing the System#

Use examples/mabfe_example.yaml as the starting configuration. Each field is documented in Configuration Overview , but review the inputs below before running anything:

Required Files#

Protein structure – protein_input.pdb It can be prepared from e.g. Maestro or an equivalent software. Protonation states are inferred from the residue name (AMBER conventions, e.g., ASH denotes protonated ASP). Water or ligand coordinates may remain in the file—they are stripped during staging.
Ligand structures – one ligand per .sdf file with 3D coordinates. Docked poses, aligned experimental structures, or co-folding models all work as long as the coordinates align with the provided protein_input.pdb. Ensure hydrogens/protonation states are correct (Open Babel, unipKa, or a similar tool can help).
System topology and coordinates (optional) – system_input.pdb / system_input.inpcrd Needed for membrane protein system.

The membrane-embedded system can be generated via Dabble (preferred with protein_input.pdb). system_input.pdb must encode the correct unit-cell vectors (box information). If system_input.inpcrd is provided its coordinates take precedence.

The protein does not need to be aligned to protein_input.pdb and the alignment will be done automatically based on the create.protein_align config setting.

Systems from other builders (CHARMM-GUI, Maestro, etc.) may work but are not extensively tested.

Command to generate POPC-embedded systems with Dabble:
```
dabble -i protein_input.mae -o system_input.prmtop --hmr -w 20 -O -ff charmm
```
In batter preparation process, the membrane molecules will be extracted (controlled by create.lipid_mols); water and ion molecules around create.solv_shell will also be extracted.

Generating Simulation Inputs#

Copy and edit the template. Start from examples/mabfe_example.yaml and save a copy beside your project data. Update:
- run.output_folder – dedicated directory for outputs/logs.
- create.system_name – label used in reports.
- create.ligand_input – JSON file mapping unique ligand IDs to .sdf files (see examples/ligand_dict.json).
- create.* paths – point at your receptor, system, membrane, and restraint files.
- create.anchor_atoms – choose stable backbone atoms (CA/C/N) with the guidelines below.
  
  Anchors (P1, P2, P3) should avoid loop regions, keep P1–P2 and P2–P3 ≥ 8 Å, and target ∠(P1–P2–P3) near 90°.
  
  P1 should preferably form a consistent electrostatics interaction with available bound ligands (e.g., a salt bridge).
  
  For GPCR orthosteric sites, a common choice is P1=3x32, P2=2x53, P3=7x42.
Additional field that may need adjustment based on your cluster environment:
- run.email_on_completion – email address to notify when SLURM jobs complete.
- run.email_sender – email address to send notifications from. Default to nobody@stanford.edu if unset.
- run.slurm.partition – SLURM partition/queue to submit jobs to.
- run.max_active_jobs – cap on how many SLURM jobs to keep active at once (default 1000, 0 disables throttling).
Validate the configuration before heavy computation (Optional):
```
batter run examples/mabfe_example.yaml --dry-run
```
This command runs ligand parameterisation (WARNING: heavy load), and equilibration system preparation. On shared clusters, run the dry-run on a compute node if possible to avoid overloading login nodes.
Inspect the staged system (Optional) Once the dry-run completes, review <run.output_folder>/executions/<run_id>/:
- simulations/<LIGAND>/equil/full.pdb – ligand-specific equilibration systems. Check if the ligand is correctly placed in the binding site, and that membranes/solvent boxes look reasonable.
Launch the full workflow manager (local execution):
```
batter run examples/mabfe_example.yaml
```
Production runs take hours to days depending on system size, the number of ligands, and available hardware. Progress is streamed to the terminal and to executions/<run_id>/logs/batter.log.

Submitting the manager job via SLURM (RECOMMENDED)#

To submit the same run through SLURM:

batter run examples/mabfe_example.yaml --slurm-submit

Provide --slurm-manager-path if you maintain a custom SLURM header template (accounts, modules, partitions, etc.). Copy and modify the default template from batter/data/job_manager.sbatch.

The job manager stages the system locally, writes an sbatch script based on the YAML hash, and streams updates as windows finish.

Handy CLI Flags#

batter run exposes many overrides so you rarely have to edit YAML mid-iteration:

--on-failure {prune,raise,retry}: Decide how to handle per-ligand failures. retry clears FAILED sentinels and reruns that phase once.
--only-equil / --full: Stop after shared prep/equilibration—useful for debugging system setup before FE windows.
--dry-run: Stage the system and prepare equilibration inputs without running any MD.
--run-id and --output-folder: Override execution paths without touching system.* fields.
--slurm-submit / --slurm-manager-path: Switch between local execution and SLURM submission (with an optional custom header).

Run batter run --help anytime you need the full list of switches and defaults.

Monitoring Jobs#

Keep an eye on SLURM progress with:

batter report-jobs

It summarises queued, running, and completed windows per system. If you stop the manager process but the SLURM jobs keep running, cancel them via:

batter cancel-jobs --contains <system_path_reported_above>

Optional: Additional Conformational Restraints#

Use the restraint-generation notebook from bat_mem (or an equivalent script) to build a restraints.json describing the distance constraints you need.
Point create.extra_conformation_restraints at the resulting JSON file:
```
extra_conformation_restraints: path/to/restraints.json
```

See examples/conformational_restraints for a full example.

Optional: Additional Positioinal Restraints#

Add selection string for the atoms to be positionally restraint to create.extra_restraints at the resulting JSON file:
```
extra_restraints: "selection_string"
```

See examples/extra_restraints for a full example.

Analysis#

Completed runs automatically write MBAR summaries under executions/<run_id>/results. Use the CLI helpers to inspect them:

batter fe list <run.output_folder>
batter fe show <run.output_folder> <run_id> --ligand <ligand>

fe list prints a high-level table (ΔG, SE, protocol, originals, status) for every stored run, while fe show dives into per-window data; use --ligand when the run produced multiple ligand records. CSV/JSON exports live alongside the results on disk, and convergence plots appear under results/<run_id>/<ligand>/Results. See Analysis Toolkit for deeper post-processing (MBAR diagnostics and REMD parsing).

Additional Resources#

Start from SMILES and protein sequence (with or without available structures) to absolute

binding free energy: bat_mem

Unsure about the protonation state of the ligand: unipKa.