Integrating Flagship Pioneering protein inverse folding model (RLDIF) into bionemo

sub-packages/bionemo-rldif/LICENSE

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sub-packages/bionemo-rldif/README.md

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+# RLDIF 
+
+Welcome to RLDIF! RLDIF is a protein inverse folding model built to provide diverse sequences that fold into your target structure. Our paper demonstrates that the sequences generated by RLDIF have higher diversity than those generated by ProteinMPNN and PiFold. 
+
+To run RLDIF from this directory, first run
+
+```bash
+pip install -e .
+
+```
+from this directory to install the package. 
+
+Then to generate sequences for your pdb files of interest run the following code
+
+```python
+pdb_file_paths = ['pdb_path_one', 'pdb_path_two']
+config = RLDIFConfig()
+model = RLDIF(config).cuda()
+
+model.initialize()
+dataloader = RLDIFDataset(pdb_file_paths).return_dataloader(model)
+
+#You can modify num_samples to change the number of sequences generated per pdb input
+result = RLDIF_Generator(model, dataloader, num_samples=4)
+```
+
+The resulting file contains the following columns:
+
+- `name`: for the name of the PDB file
+- `pred`: for the RLDIF generated sequence
+- `real`: for the real sequence from the PDB file
+- `tm_score`: If you have the means to fold the result, this is a column where the tm-score between the folded predicted structure and the folded real structure would show up. Right now this is default to None, if users want this feature raise an issue and I can add it.

sub-packages/bionemo-rldif/pyproject.toml

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+[build-system]
+requires = ["setuptools-scm>=8", "setuptools>=64", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "bionemo-rldif"
+license.file = "LICENSE"
+readme = "README.md"
+description = "BioNeMo RLDIF"
+authors = [{ name = "Flagship Pioneering", email = "[email protected]" }]
+dynamic = ["version"]
+dependencies = ['bionemo-core', 'torch-scatter']
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["bionemo.*"]
+namespaces = true
+exclude = ["test*."]
+
+[tool.setuptools_scm]
+root = '../..'
+version_scheme = "post-release"
+local_scheme = "no-local-version"
+
+[tool.uv]
+cache-keys = [{ git = true }]

sub-packages/bionemo-rldif/src/bionemo/rldif/__init__.py

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+from bionemo.rldif.model.mod_pifold import InverseFoldingDiffusionPiFoldModel as RLDIF, RLDIFConfig
+from bionemo.rldif.data.dataset import RLDIFDataset
+from bionemo.rldif.run.run import RLDIF_Generator

sub-packages/bionemo-rldif/src/bionemo/rldif/api.py

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+from typing import Sequence
+from bionemo.rldif.model.mod_pifold import InverseFoldingDiffusionPiFoldModel as RLDIF, RLDIFConfig
+from bionemo.rldif.data.dataset import RLDIFDataset
+from bionemo.rldif.run.run import RLDIF_Generator
+
+__all__: Sequence[str] = (
+    "RLDIF",
+    "RLDIFDataset",
+    "RLDIFConfig",
+    "RLDIF_Generator",
+)

sub-packages/bionemo-rldif/src/bionemo/rldif/data/dataset.py

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+from torch.utils.data import Dataset
+import json 
+import numpy as np 
+import pandas as pd
+import torch 
+from tqdm import tqdm
+import ast
+from Bio.PDB import PDBParser
+from torch.utils.data import DataLoader
+
+AMINO_ACIDS = {
+    'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E',
+    'PHE': 'F', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I',
+    'LYS': 'K', 'LEU': 'L', 'MET': 'M', 'ASN': 'N',
+    'PRO': 'P', 'GLN': 'Q', 'ARG': 'R', 'SER': 'S',
+    'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y'
+}
+
+
+class RLDIFDataset(Dataset):
+    def __init__(
+        self,
+        pdb_paths
+    ):
+        super().__init__()
+        self.RS = []
+
+        iterator = tqdm(pdb_paths)
+
+        for block in iterator:
+            self.RS.append(self.preprocess_pdb(block))
+
+        print(f"Processed {len(self.RS)} PDBs to generate sequences for")
+
+    def preprocess_pdb(self, pdb_path):
+        parser = PDBParser()
+        structure = parser.get_structure("name", pdb_path)
+        model = structure[0]
+        chain = model["A"]
+        seq = ""
+        coords = {'CA': [], 'C': [], 'N': [], 'O': []}
+        for residue in chain:
+            if residue.get_resname() not in AMINO_ACIDS.keys():
+                continue
+            seq += AMINO_ACIDS[residue.get_resname()]
+            coords['CA'].append(residue["CA"].get_coord())
+            coords['C'].append(residue["C"].get_coord())
+            coords['N'].append(residue["N"].get_coord())
+            coords['O'].append(residue["O"].get_coord())
+
+        return {
+            "name": pdb_path.split("/")[-1].split('.')[0],
+            "seq": seq,
+            "coords": coords,
+        }
+
+    def preprocess_src(
+        self,
+        src: pd.DataFrame,
+        index_for_redis: int = 0,
+    ) -> list:
+
+        src = src.reset_index()
+        sample_dict_list = {
+            int(index_for_redis + idx): self.get_sample_dict(src.loc[idx])
+            for idx in src.index.values
+        }
+        return sample_dict_list
+
+    def preprocess_src_samples_dict(
+        self, src: pd.DataFrame, index_for_redis: int = 0
+    ) -> dict:
+        # reset the index as src dataframe might be filtered, these needs to be linear for redis indexing
+        src = src.reset_index()
+        sample_dict_dict = {
+            int(index_for_redis + idx): self.get_sample_dict(src.loc[idx])
+            for idx in src.index.values
+        }
+        return sample_dict_dict
+
+    def get_sample_dict(self, row) -> dict:
+        return {
+            "title": row["name"],
+            "seq": row["seq"],
+            "CA": np.stack(row["CA"]),
+            "N": np.stack(row["N"]),
+            "C": np.stack(row["C"]),
+            "O": np.stack(row["O"]),
+            "score": 100.0,
+        }
+
+    def __len__(self) -> int:
+        return len(self.RS)
+
+    def __getitem__(self, idx):
+        return self.RS[idx]
+
+    def return_dataloader(self, model):
+        return DataLoader(
+                self,
+                batch_size=4,
+                shuffle=False,
+                collate_fn=model.collate_fn,
+            )