---
license: cc-by-4.0
pretty_name: ACE2–B0AT1 Ligand Designs — Ligand–Receptor Complexes
task_categories:
- graph-ml
- other
size_categories:
- n<1K
language:
- en
tags:
- chemistry
- drug-discovery
- molecular-docking
- protein-ligand-complex
- ACE2
- B0AT1
- SLC6A19
- amino-acid-transporter
- metabolic-disease
- PKU
- biology
- de-novo-design
- generative-design
- structure-based-drug-design
- synthetic
- synthetic-data
- ai-generated
- computational-chemistry
- virtual-screening
- cheminformatics
- SMILES
- transporter
- phenylketonuria
- neutral-amino-acid-transporter
- solute-carrier
- membrane-protein
- Hartnup-disorder
- phenylalanine
- rare-disease
- generative-ai
- small-molecule
- SDF
- genetic-algorithm
- GA-II
- autodock-vina
- TC-43
- technetium
---

# ACE2–B0AT1 Ligand Designs — Ligand–Receptor Complexes

> **Why this target matters.** B0AT1 (SLC6A19) is a rare transporter target with direct human genetic validation — people who lack it (Hartnup disorder) are largely healthy — making it an unusually de-risked oral route to lowering blood phenylalanine in phenylketonuria.

**38** computationally designed small-molecule ligands docked into the **ACE2 · B0AT1 (SLC6A19)** complex,
each provided as a single-file **protein–ligand complex in PDB format** (31 unique ligand structures).

**B0AT1 (SLC6A19)** is the neutral amino-acid transporter that traffics to the cell surface with a partner (**ACE2** in the gut). It is an emerging target for phenylketonuria and chronic kidney disease — interest reinforced by positive Phase I data for Maze Therapeutics' oral SLC6A19 inhibitor MZE782 (2025).

> **Receptor note:** coordinates correspond to the ACE2 · B0AT1 heterocomplex (chain A res 5–609; chain B res 20–768; chain C res 20–768; chain D res 5–609).
> *TODO: add the source RCSB PDB accession for the receptor template used to generate these complexes.*

## Dataset summary

| | |
|---|---|
| Complex files | **38** (`*_cmpx.pdb`) |
| Unique ligand SMILES | **31** |
| Receptor | ACE2 · B0AT1 heterocomplex (chain A res 5–609; chain B res 20–768; chain C res 20–768; chain D res 5–609) |
| Generator | Technetium GA-II pocket-conditioned generative platform |
| Generation date | 2025-10-04 – 2025-10-07 |
| Pose scoring | AutoDock Vina |

These are de novo, scaffold-constrained generative designs produced by the **Technetium GA-II** pocket-conditioned generative platform. Each design is docked into the target pocket and scored with **AutoDock Vina**; a `REMARK CORE` record preserves the scaffold/attachment context.

Each complex file is self-contained — receptor structure, the ligand's 3D docked pose, and a
2D↔3D atom map all travel inside the single PDB.

## Property profile

Physicochemical ranges are computed with RDKit over the **31** unique ligand structures; docking
energy is from the generation/docking pipeline.

| Property | Range | Median |
|---|---|---|
| Docking energy (AutoDock Vina) | ≤ -9.5 kcal/mol (down to -11.4) | — |
| Molecular weight | 287.4 – 367.5 Da | 336.4 |
| cLogP | 1.7 – 4.6 | 3.5 |
| TPSA | 49.3 – 98.7 Ų | 70.7 |
| Fsp3 (fraction sp³ C) | 0.2 – 0.6 | 0.3 |
| H-bond donors | 1 – 3 | 2 |
| H-bond acceptors | 2 – 5 | 3 |
| Rotatable bonds | 2 – 5 | 4 |

## File format

Each `*_cmpx.pdb` bundles the receptor and one docked ligand pose:

| Record | Content |
|---|---|
| `REMARK VINA RESULT <energy> …` | AutoDock Vina docking score (kcal/mol) |
| `REMARK CORE <smiles>` | the scaffold / attachment context of the design |
| `REMARK SMILES <smiles>` | the docked ligand (2D structure) |
| `REMARK SMILES IDX <pos> <serial> …` | map of each SMILES heavy-atom position ↔ its ligand atom serial (the 2D↔3D key) |
| `ATOM … <chain>` | receptor heavy atoms |
| `ATOM … UNL` (after `MODEL 1`) | ligand 3D pose (residue name `UNL`) |

## Usage

```python
import glob

def read_complex(path):
    smiles, idx = None, {}
    with open(path) as fh:
        for line in fh:
            if line.startswith("REMARK SMILES IDX"):
                toks = line.split()[3:]            # flat list of (smiles_pos, atom_serial)
                for i in range(0, len(toks), 2):
                    idx[int(toks[i])] = int(toks[i + 1])
            elif line.startswith("REMARK SMILES"):
                smiles = line.split(None, 2)[2].strip()
    return smiles, idx                              # idx[smiles_atom_position] -> ligand atom serial

for f in glob.glob("*_cmpx.pdb"):
    smi, idx = read_complex(f)
    # ligand atoms are the `ATOM ... UNL` records following `MODEL 1`
```

## Provenance & intended use

- These are **computationally generated** designs and docked poses — **not** experimentally
  validated binders. No claim of activity or selectivity is made.
- Intended for machine-learning, cheminformatics, generative-model benchmarking, and
  docking-pose research on a well-defined target.

## Citation

Generated by **Technetium Therapeutics**. Poses scored with AutoDock Vina.