├── README.md
└── boltz_generalized_covalent_inference.ipynb


/README.md:
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1 | # Run Boltz-1 Model With Arbitrary Non-Canonical Amino Acids
2 | 
3 | A jupyter notebook which walks you through injecting a new residue into the CCD cache and then using 
4 | that new residue as a modification in a boltz yaml file. 
5 | All you need for the input is a smiles string representing the molecule attached to an amino acid.
6 | 
7 | Runs with any python environment that can run Boltz inference.
8 | 


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/boltz_generalized_covalent_inference.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Boltz Generalized Covalent Residue Modification\n",
  8 |     "\n",
  9 |     "Modifies the Boltz Chemical Component Dictionary cache with a new entry corresponding to an arbitrary covalent modification of a residue.\n",
 10 |     "\n",
 11 |     "___\n",
 12 |     "<I>Benjamin Fry (bfry@g.harvard.edu)</I>"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 1,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "from rdkit import Chem\n",
 22 |     "from rdkit.Chem import AllChem\n",
 23 |     "\n",
 24 |     "import pickle\n",
 25 |     "from pathlib import Path\n",
 26 |     "\n",
 27 |     "# THIS IS ABSOLUTELY NECESSARY TO MAKE SURE THAT ALL PROPERTIES ARE PICKLED OR ELSE BOLTZ WILL CRASH.\n",
 28 |     "Chem.SetDefaultPickleProperties(Chem.PropertyPickleOptions.AllProps)"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 2,
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "ncaa_smiles_str = \"O=C(CCSC[C@H](C(O)=O)N)N1CC(C(F)/C(OC)=C(OC)/OC)OC(OC)C1C2=NC=CC2\"\n",
 38 |     "\n",
 39 |     "# I'm not sure if this matters as long as it can be substructure matched into the covalent ligand, \n",
 40 |     "#   using an alanine might allow this to generalize beyond cysteine modifications and it seems to work in my testing.\n",
 41 |     "#   You'd probably want to use the largest residue that matches the covalent attachment point to avoid matching to the wrong part of the ligand.\n",
 42 |     "reference_residue_to_modify = 'CYS' \n",
 43 |     "\n",
 44 |     "# The new CCD code for the covalent ligand, the first 5 letters are written to the CIF file as the resname.\n",
 45 |     "output_ccd_code = \"CUSTOM\""
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "markdown",
 50 |    "metadata": {},
 51 |    "source": [
 52 |     "### Load the Boltz Cache Chemical Component Dictionary"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 3,
 58 |    "metadata": {},
 59 |    "outputs": [],
 60 |    "source": [
 61 |     "cache_dir = Path(\"/nfs/polizzi/bfry/.boltz/\")\n",
 62 |     "\n",
 63 |     "ccd_path = cache_dir / 'ccd.pkl'\n",
 64 |     "with ccd_path.open(\"rb\") as file:\n",
 65 |     "    ccd = pickle.load(file)  # noqa: S301"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "### Visualize molecule and atom indices for sanity check"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 4,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "image/jpeg": 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",
 84 |       "text/plain": [
 85 |        "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=750x750>"
 86 |       ]
 87 |      },
 88 |      "metadata": {},
 89 |      "output_type": "display_data"
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "# Create drawing options with atom indices \n",
 94 |     "smiles_mol = Chem.MolFromSmiles(ncaa_smiles_str)\n",
 95 |     "draw_options = Chem.Draw.MolDrawOptions()\n",
 96 |     "draw_options.addAtomIndices = True  # Enable atom indices to be displayed\n",
 97 |     "\n",
 98 |     "# Increase size of displayed image\n",
 99 |     "import IPython.display\n",
100 |     "IPython.display.display(Chem.Draw.MolToImage(smiles_mol, size=(750, 750), options=draw_options))"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": 5,
106 |    "metadata": {},
107 |    "outputs": [
108 |     {
109 |      "data": {
110 |       "text/plain": [
111 |        "{10: 'N', 6: 'CA', 7: 'C', 9: 'O', 5: 'CB', 4: 'SG', 8: 'OXT'}"
112 |       ]
113 |      },
114 |      "execution_count": 5,
115 |      "metadata": {},
116 |      "output_type": "execute_result"
117 |     }
118 |    ],
119 |    "source": [
120 |     "# Load Cysteine from the CCD and remove hydrogens\n",
121 |     "cys_mol = ccd[reference_residue_to_modify]\n",
122 |     "reference_cys_mol = AllChem.RemoveHs(cys_mol)\n",
123 |     "\n",
124 |     "# Search for the cysteine substructure in the ncaa molecule\n",
125 |     "has_match = smiles_mol.HasSubstructMatch(reference_cys_mol)\n",
126 |     "if has_match:\n",
127 |     "    match_indices = smiles_mol.GetSubstructMatch(reference_cys_mol)\n",
128 |     "    substruct_to_match = {i.GetProp('name'): match_indices[idx] for idx, i in enumerate(reference_cys_mol.GetAtoms())}\n",
129 |     "\n",
130 |     "# Construct mapping of cysteine atom name to atom index in the ncaa molecule\n",
131 |     "idx_to_name = {j: i for i, j in substruct_to_match.items()}\n",
132 |     "idx_to_name"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "markdown",
137 |    "metadata": {},
138 |    "source": [
139 |     "### Add the metadata boltz expects to the atoms"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "code",
144 |    "execution_count": 6,
145 |    "metadata": {},
146 |    "outputs": [],
147 |    "source": [
148 |     "for idx, atom in enumerate(smiles_mol.GetAtoms()):\n",
149 |     "    default_name = f'{atom.GetSymbol()}{str(atom.GetIdx())}'\n",
150 |     "\n",
151 |     "    # If the index is a canonical cysteine atom, use the cysteine atom name\n",
152 |     "    name = idx_to_name.get(idx, default_name)\n",
153 |     "\n",
154 |     "    # Set atom properties\n",
155 |     "    atom.SetProp('name', name)\n",
156 |     "    atom.SetProp('alt_name', name) \n",
157 |     "    is_leaving = False\n",
158 |     "    if name == 'OXT':\n",
159 |     "        is_leaving = True\n",
160 |     "    atom.SetBoolProp('leaving_atom', is_leaving)"
161 |    ]
162 |   },
163 |   {
164 |    "cell_type": "markdown",
165 |    "metadata": {},
166 |    "source": [
167 |     "### Reorder atoms to canonical ordering (N, Ca, C, O, CB, S, ...)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 7,
173 |    "metadata": {},
174 |    "outputs": [],
175 |    "source": [
176 |     "# Map atom name to atom index in the ncaa molecule \n",
177 |     "curr_atom_order = {atom.GetProp('name'): idx for idx, atom in enumerate(smiles_mol.GetAtoms()) if atom.GetSymbol() != 'H'}\n",
178 |     "\n",
179 |     "# Map atom name to atom index in the reference cysteine molecule\n",
180 |     "target_atom_order = {}\n",
181 |     "for atom in reference_cys_mol.GetAtoms():\n",
182 |     "    if atom.GetSymbol() == 'H':\n",
183 |     "        continue\n",
184 |     "    target_atom_order[atom.GetProp('name')] = atom.GetIdx()\n",
185 |     "\n",
186 |     "# There are atoms not in target_atom_order that are in curr_atom_order so we need to these\n",
187 |     "remapped_atom_order = {}\n",
188 |     "offset_idx = len(target_atom_order)\n",
189 |     "for atom in curr_atom_order:\n",
190 |     "    if atom in target_atom_order:\n",
191 |     "        remapped_atom_order[atom] = target_atom_order[atom]\n",
192 |     "    else:\n",
193 |     "        remapped_atom_order[atom] = offset_idx\n",
194 |     "        offset_idx += 1\n",
195 |     "\n",
196 |     "# Remove hydrogens and reorder atoms according to the order in the reference cysteine.\n",
197 |     "trim = AllChem.RemoveHs(smiles_mol)\n",
198 |     "remap_order = {x.GetProp('name'): (remapped_atom_order[x.GetProp('name')], x.GetIdx()) for x in trim.GetAtoms()}\n",
199 |     "remap_idx_list = [x[1] for x in sorted(remap_order.values())]\n",
200 |     "trim_reordered = Chem.RenumberAtoms(trim, remap_idx_list)"
201 |    ]
202 |   },
203 |   {
204 |    "cell_type": "markdown",
205 |    "metadata": {},
206 |    "source": [
207 |     "### Generate a conformer, could make this more sophisticated..."
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 8,
213 |    "metadata": {},
214 |    "outputs": [],
215 |    "source": [
216 |     "# Generate a simple conformer, might want to do something more sophisticated or start with a DFT conformer in an SDF file.\n",
217 |     "trim_reordered = AllChem.AddHs(trim_reordered)\n",
218 |     "AllChem.EmbedMolecule(trim_reordered)\n",
219 |     "AllChem.UFFOptimizeMolecule(trim_reordered)\n",
220 |     "\n",
221 |     "# Set conformer properties\n",
222 |     "for c in trim_reordered.GetConformers():\n",
223 |     "    c.SetProp('name', 'Ideal')"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "markdown",
228 |    "metadata": {},
229 |    "source": [
230 |     "### Sanity check renumbering was successful."
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 9,
236 |    "metadata": {},
237 |    "outputs": [
238 |     {
239 |      "name": "stdout",
240 |      "output_type": "stream",
241 |      "text": [
242 |       "0 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'N', 'alt_name': 'N', 'leaving_atom': False}\n",
243 |       "1 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, '_ChiralityPossible': 1, '_CIPCode': 'S', 'name': 'CA', 'alt_name': 'CA', 'leaving_atom': False}\n",
244 |       "2 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C', 'alt_name': 'C', 'leaving_atom': False}\n",
245 |       "3 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O', 'alt_name': 'O', 'leaving_atom': False}\n",
246 |       "4 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'CB', 'alt_name': 'CB', 'leaving_atom': False}\n",
247 |       "5 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'SG', 'alt_name': 'SG', 'leaving_atom': False}\n",
248 |       "6 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'OXT', 'alt_name': 'OXT', 'leaving_atom': True}\n",
249 |       "7 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O0', 'alt_name': 'O0', 'leaving_atom': False}\n",
250 |       "8 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C1', 'alt_name': 'C1', 'leaving_atom': False}\n",
251 |       "9 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C2', 'alt_name': 'C2', 'leaving_atom': False}\n",
252 |       "10 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C3', 'alt_name': 'C3', 'leaving_atom': False}\n",
253 |       "11 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'N11', 'alt_name': 'N11', 'leaving_atom': False}\n",
254 |       "12 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C12', 'alt_name': 'C12', 'leaving_atom': False}\n",
255 |       "13 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, '_ChiralityPossible': 1, 'name': 'C13', 'alt_name': 'C13', 'leaving_atom': False}\n",
256 |       "14 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, '_ChiralityPossible': 1, 'name': 'C14', 'alt_name': 'C14', 'leaving_atom': False}\n",
257 |       "15 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'F15', 'alt_name': 'F15', 'leaving_atom': False}\n",
258 |       "16 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C16', 'alt_name': 'C16', 'leaving_atom': False}\n",
259 |       "17 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O17', 'alt_name': 'O17', 'leaving_atom': False}\n",
260 |       "18 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C18', 'alt_name': 'C18', 'leaving_atom': False}\n",
261 |       "19 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C19', 'alt_name': 'C19', 'leaving_atom': False}\n",
262 |       "20 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O20', 'alt_name': 'O20', 'leaving_atom': False}\n",
263 |       "21 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C21', 'alt_name': 'C21', 'leaving_atom': False}\n",
264 |       "22 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O22', 'alt_name': 'O22', 'leaving_atom': False}\n",
265 |       "23 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C23', 'alt_name': 'C23', 'leaving_atom': False}\n",
266 |       "24 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O24', 'alt_name': 'O24', 'leaving_atom': False}\n",
267 |       "25 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, '_ChiralityPossible': 1, 'name': 'C25', 'alt_name': 'C25', 'leaving_atom': False}\n",
268 |       "26 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'O26', 'alt_name': 'O26', 'leaving_atom': False}\n",
269 |       "27 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C27', 'alt_name': 'C27', 'leaving_atom': False}\n",
270 |       "28 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, '_ChiralityPossible': 1, 'name': 'C28', 'alt_name': 'C28', 'leaving_atom': False}\n",
271 |       "29 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C29', 'alt_name': 'C29', 'leaving_atom': False}\n",
272 |       "30 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'N30', 'alt_name': 'N30', 'leaving_atom': False}\n",
273 |       "31 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C31', 'alt_name': 'C31', 'leaving_atom': False}\n",
274 |       "32 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C32', 'alt_name': 'C32', 'leaving_atom': False}\n",
275 |       "33 {'__computedProps': <rdkit.rdBase._vectSs object at 0x7f3303133040>, 'name': 'C33', 'alt_name': 'C33', 'leaving_atom': False}\n"
276 |      ]
277 |     }
278 |    ],
279 |    "source": [
280 |     "for atom in trim_reordered.GetAtoms():\n",
281 |     "    if atom.GetSymbol() == 'H':\n",
282 |     "        continue\n",
283 |     "    print(atom.GetIdx(), atom.GetPropsAsDict())"
284 |    ]
285 |   },
286 |   {
287 |    "cell_type": "markdown",
288 |    "metadata": {},
289 |    "source": [
290 |     "### Save the conformer to the ccd cache and overwrite it."
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 10,
296 |    "metadata": {},
297 |    "outputs": [],
298 |    "source": [
299 |     "ccd[output_ccd_code] = trim_reordered\n",
300 |     "\n",
301 |     "ccd_path = cache_dir / 'ccd.pkl'\n",
302 |     "with ccd_path.open(\"wb\") as file:\n",
303 |     "    pickle.dump(ccd, file)"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "markdown",
308 |    "metadata": {},
309 |    "source": [
310 |     "### To Run Inference:\n",
311 |     "Run `boltz predict ./path_to_yaml_file` with the following yaml file contents to test the custom modification at residue 2:\n",
312 |     "___\n",
313 |     "\n",
314 |     "\n",
315 |     "```yaml\n",
316 |     "version: 1  # Optional, defaults to 1\n",
317 |     "sequences:\n",
318 |     "  - protein:\n",
319 |     "      id: [A]\n",
320 |     "      sequence: AAAAACAAAAAAAAAAA # This doesn't have to have a CYS at position 6 to apply the modification, but it does in this case.\n",
321 |     "      msa: empty\n",
322 |     "      modifications:\n",
323 |     "        - position: 6 \n",
324 |     "          ccd: 'CUSTOM' # Use the custom CCD code we just injected.\n",
325 |     "```"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "markdown",
330 |    "metadata": {},
331 |    "source": []
332 |   }
333 |  ],
334 |  "metadata": {
335 |   "kernelspec": {
336 |    "display_name": "boltz",
337 |    "language": "python",
338 |    "name": "python3"
339 |   },
340 |   "language_info": {
341 |    "codemirror_mode": {
342 |     "name": "ipython",
343 |     "version": 3
344 |    },
345 |    "file_extension": ".py",
346 |    "mimetype": "text/x-python",
347 |    "name": "python",
348 |    "nbconvert_exporter": "python",
349 |    "pygments_lexer": "ipython3",
350 |    "version": "3.9.20"
351 |   }
352 |  },
353 |  "nbformat": 4,
354 |  "nbformat_minor": 2
355 | }
356 | 


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