Tutorial 5: EXESS Interaction Energy¶

What you get: A single number (in Hartrees) quantifying how strongly a ligand interacts with its protein pocket — computed from first principles, no force field fitting required.


Time	~2–5 minutes (cloud compute)
Skill level	Beginner
Prerequisites	Python 3.10+, `rush-py` installed, `RUSH_TOKEN` and `RUSH_PROJECT` set

Why This Matters¶

In structure-based drug design, you constantly ask: how tightly does this molecule sit in the binding pocket? Docking scores approximate this with empirical potentials. Molecular mechanics (MM-GBSA, MM-PBSA) improve on it. But quantum mechanics gives you the most physically rigorous answer — it captures polarization, charge transfer, and dispersion effects that classical methods miss.

What this is (and isn’t)

This interaction energy is not a binding free energy — it doesn’t include entropy, solvation, or conformational sampling. Think of it as a high-quality electronic interaction energy for a single pose. It’s most useful for rank-ordering poses or comparing ligands in the same pocket.

Quick Start: Get an Interaction Energy¶

import json
from rush import exess
from rush.client import RunOpts, download_object

out = exess.interaction_energy(
    "tyk2_ejm_31_t.json",       # TRC file for TYK2 + ligand EJM-31
    93,                          # Fragment index of the ligand
    method="RestrictedHF",       # Explicit: minimal method for testing
    basis="STO-3G",              # Minimal basis set for tutorials only – use cc-pVDZ or larger for production
    frag_keywords=exess.FragKeywords(
        level="Trimer",          # Include up to 3-body interactions
        dimer_cutoff=10.0,       # Å — pairs within this distance
        trimer_cutoff=5.0,       # Å — trimers within this distance
        cutoff_type="Centroid",
        distance_metric="Min",
    ),
    run_opts=RunOpts(name="Tutorial: Interaction Energy"),
    collect=True,
)

# Extract the result
json_bytes = download_object(out[0]["path"])
result = json.loads(json_bytes.decode())
print(f"Interaction energy: {result['qmmbe']['expanded_hf_energy']} Eh")

Example output:

Interaction energy: -0.08234 Eh

That negative value means the ligand is stabilized by its protein environment. The magnitude tells you the strength — more negative = stronger interaction.

⚠️ Tutorial Basis Set Warning

This tutorial example code uses STO-3G, a minimal basis set chosen purely for speed so examples run quickly. STO-3G is not suitable for research or production calculations. For meaningful results, use at least cc-pVDZ or larger. See the electronic structure methods reference for available basis sets.

Where to get the input file¶

The tyk2_ejm_31_t.json topology file is in the rush-py repo’s tests/data/ folder.

Understanding the Parameters¶

Parameter	What it controls	Rule of thumb
`93` (fragment index)	Which fragment is your ligand	Check your topology to find the right index
`level="Trimer"`	Include 3-body corrections	More accurate but slower; `"Dimer"` is faster
`dimer_cutoff=10.0`	How far out to include fragment pairs (Å)	10 Å captures most important interactions; increase for charged systems
`trimer_cutoff=5.0`	How far out for 3-body terms (Å)	Keep smaller than dimer_cutoff; 3-body terms decay faster
`included_fragments`	Restrict which fragments participate	Use to limit to the binding pocket (faster, often sufficient)

End-to-End: From PDB to Interaction Energy¶

Don’t have a TRC file? Start from a PDB. Rush’s Prepare Complex module handles protonation, missing atoms, and charge assignment automatically.

Step 1: Prepare the system¶

import json
from pathlib import Path
from rush.client import RunOpts
from rush.prepare_complex import prepare_complex

trc = prepare_complex(
    Path("1hsg.pdb"),
    ligand_names=["MK1", "HOH"],  # Residue names for ligands/waters
    run_opts=RunOpts(name="Tutorial: Prepare Complex"),
    collect=True,
)

# Inspect what Prepare Complex determined about charges
print("Charged residues:")
for i, (name, charge) in enumerate(
    zip(trc.residues.seqs, trc.topology.fragment_formal_charges)
):
    if int(charge) != 0:
        print(f"  {i:>4} {name}: {int(charge):+}")

What Prepare Complex does under the hood

It uses PDBFixer to fill missing atoms/residues, PDB2PQR for protonation states, and RDKit for ligand processing. The output TRC (Topology + Residues + Coordinates) is fully annotated with connectivity and formal charges — everything EXESS needs.

Step 2: Focus on the binding pocket¶

Running QM on the entire protein can be wasteful. Most residues far from the ligand contribute negligibly to the interaction energy. Use get_fragments_near_fragment to select just the pocket:

# Find fragments within 5 Å of the ligand
lig_idx = trc.residues.seqs.index("MK1")
pocket_frags = trc.topology.get_fragments_near_fragment(lig_idx, 5.0) + [lig_idx]
print(f"Pocket contains {len(pocket_frags)} fragments (out of {len(trc.residues.seqs)} total)")

Step 3: Run the interaction energy calculation¶

from rush import exess

# EXESS needs the Topology (not the full TRC)
with open("1hsg_t.json", "w") as f:
    json.dump(trc.topology.to_json(), f, indent=2)

out = exess.interaction_energy(
    "1hsg_t.json",
    lig_idx,
    frag_keywords=exess.FragKeywords(
        level="Dimer",
        included_fragments=pocket_frags,
    ),
    run_opts=RunOpts(name="Tutorial: Interaction Energy E2E"),
    collect=True,
)

json_bytes = download_object(out[0]["path"])
result = json.loads(json_bytes.decode())
print(f"Interaction energy: {result['qmmbe']['expanded_hf_energy']} Eh")

The second output

exess.interaction_energy returns two outputs. The first is the JSON with energies. The second contains additional exported data (density matrices, etc.) — only populated if you request exports. See the Exports tutorial for details.

Interpreting the Results¶

The key value is result['qmmbe']['expanded_hf_energy']:

Negative → ligand is stabilized by the protein (favorable interaction)
More negative → stronger interaction
Units are Hartrees (1 Eh ≈ 627.5 kcal/mol)

For comparing two ligands in the same pocket, the difference in interaction energies is more meaningful than the absolute values — especially at lower levels of theory.

Try It Yourself¶

The complete example is available in the rush-py repository:

👉 examples/exess-interaction-energy/

You can download the files directly or clone the repository to run the full example.