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mlx-examples/esm/notebooks/contact_prediction.ipynb
Vincent Amato eccabdd227 Add ESM
2025-08-15 23:48:57 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "3fbacbe4",
"metadata": {},
"source": [
"## Predicting Protein Contacts with ESM-2\n",
"\n",
"Understanding how amino acids interact within a folded protein is essential for grasping protein function and stability. Contact prediction, the task of identifying which residues are close together in three-dimensional space, is a key step in the sequence to structure process. ESM-2s learned attention patterns capture evolutionary signals that encode structural information, which allows the model to predict residue contacts directly from sequence data.\n",
"\n",
"In this notebook, we'll explore ESM-2's ability to predict protein contacts across three diverse proteins from different organisms:\n",
"\n",
"**Bacterial Transport:**\n",
"- **1a3a (PTS Mannitol Component)**: A phosphoenolpyruvate-dependent sugar phosphotransferase system component from *E. coli*, essential for carbohydrate metabolism\n",
"\n",
"**Stress Response:**\n",
"- **5ahw (Universal Stress Protein)**: A conserved stress response protein from *Mycolicibacterium smegmatis* that helps cells survive harsh conditions\n",
"\n",
"**Human Metabolism:**\n",
"- **1xcr (Ester Hydrolase)**: A human enzyme (C11orf54) involved in lipid metabolism and cellular signaling pathways\n",
"\n",
"We will evaluate how effectively ESM-2 captures the structural relationships present in these sequences, measuring precision across different sequence separation ranges to assess both local and long-range contact prediction performance. This notebook is a modified version of a [notebook by the same name](https://github.com/facebookresearch/esm/blob/main/examples/contact_prediction.ipynb) from the [offical ESM repsitory](https://github.com/facebookresearch/esm)."
]
},
{
"cell_type": "markdown",
"id": "08352b12",
"metadata": {},
"source": [
"### Setup\n",
"\n",
"Here we import all neccessary libraries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c1047c94",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Tuple, Optional, Dict\n",
"import string\n",
"\n",
"import mlx.core as mx\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.spatial.distance import squareform, pdist\n",
"import biotite.structure as bs\n",
"from biotite.database import rcsb\n",
"from biotite.structure.io.pdbx import CIFFile, get_structure\n",
"from Bio import SeqIO"
]
},
{
"cell_type": "markdown",
"id": "5f0af076",
"metadata": {},
"source": [
"Download multiple sequence alignment (MSA) files for our three test proteins from the ESM repository."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3264b66d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 147k 100 147k 0 0 536k 0 --:--:-- --:--:-- --:--:-- 538k\n",
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 127k 100 127k 0 0 485k 0 --:--:-- --:--:-- --:--:-- 486k\n",
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 181k 100 181k 0 0 738k 0 --:--:-- --:--:-- --:--:-- 740k\n"
]
}
],
"source": [
"!mkdir -p data\n",
"!curl -o data/1a3a_1_A.a3m https://raw.githubusercontent.com/facebookresearch/esm/main/examples/data/1a3a_1_A.a3m\n",
"!curl -o data/5ahw_1_A.a3m https://raw.githubusercontent.com/facebookresearch/esm/main/examples/data/5ahw_1_A.a3m\n",
"!curl -o data/1xcr_1_A.a3m https://raw.githubusercontent.com/facebookresearch/esm/main/examples/data/1xcr_1_A.a3m"
]
},
{
"cell_type": "markdown",
"id": "cbf1d0cb",
"metadata": {},
"source": [
"### Loading the model\n",
"\n",
"Load the ESM-2 model. Change the path below to point to your converted checkpoint."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4406e8a0",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append(\"..\")\n",
"\n",
"from esm import ESM2\n",
"\n",
"esm_checkpoint = \"../checkpoints/mlx-esm2_t33_650M_UR50D\"\n",
"tokenizer, model = ESM2.from_pretrained(esm_checkpoint)"
]
},
{
"cell_type": "markdown",
"id": "77596456",
"metadata": {},
"source": [
"### Defining functions"
]
},
{
"cell_type": "markdown",
"id": "eb5f07ed",
"metadata": {},
"source": [
"#### Parsing alignments"
]
},
{
"cell_type": "markdown",
"id": "e754abd7",
"metadata": {},
"source": [
"This function parses multiple sequence alignment files and clean up insertion artifacts. MSA files often contain lowercase letters and special characters (`.`, `*`) to indicate insertions relative to the reference sequence - these need to be removed to get the core aligned sequences."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "43717bea",
"metadata": {},
"outputs": [],
"source": [
"deletekeys = dict.fromkeys(string.ascii_lowercase)\n",
"deletekeys[\".\"] = None\n",
"deletekeys[\"*\"] = None\n",
"translation = str.maketrans(deletekeys)\n",
"\n",
"def read_sequence(filename: str) -> Tuple[str, str]:\n",
" \"\"\" Reads the first (reference) sequences from a fasta or MSA file.\"\"\"\n",
" record = next(SeqIO.parse(filename, \"fasta\"))\n",
" return record.description, str(record.seq)\n",
"\n",
"def remove_insertions(sequence: str) -> str:\n",
" \"\"\" Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. \"\"\"\n",
" return sequence.translate(translation)\n",
"\n",
"def read_msa(filename: str) -> List[Tuple[str, str]]:\n",
" \"\"\" Reads the sequences from an MSA file, automatically removes insertions.\"\"\"\n",
" return [(record.description, remove_insertions(str(record.seq))) for record in SeqIO.parse(filename, \"fasta\")]"
]
},
{
"cell_type": "markdown",
"id": "628d7de1",
"metadata": {},
"source": [
"#### Converting structures to contacts\n",
"\n",
"There are many ways to define a protein contact. Here we're using the definition of 8 angstroms between carbon beta atoms. Note that the position of the carbon beta is imputed from the position of the N, CA, and C atoms for each residue."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "21e0b44b",
"metadata": {},
"outputs": [],
"source": [
"def extend(a, b, c, L, A, D):\n",
" \"\"\"\n",
" input: 3 coords (a,b,c), (L)ength, (A)ngle, and (D)ihedral\n",
" output: 4th coord\n",
" \"\"\"\n",
" def normalize(x):\n",
" return x / np.linalg.norm(x, ord=2, axis=-1, keepdims=True)\n",
"\n",
" bc = normalize(b - c)\n",
" n = normalize(np.cross(b - a, bc))\n",
" m = [bc, np.cross(n, bc), n]\n",
" d = [L * np.cos(A), L * np.sin(A) * np.cos(D), -L * np.sin(A) * np.sin(D)]\n",
" return c + sum([m * d for m, d in zip(m, d)])\n",
"\n",
"def contacts_from_pdb(\n",
" structure: bs.AtomArray,\n",
" distance_threshold: float = 8.0,\n",
" chain: Optional[str] = None,\n",
") -> np.ndarray:\n",
" \"\"\"Extract contacts from PDB structure.\"\"\"\n",
" mask = ~structure.hetero\n",
" if chain is not None:\n",
" mask &= structure.chain_id == chain\n",
"\n",
" N = structure.coord[mask & (structure.atom_name == \"N\")]\n",
" CA = structure.coord[mask & (structure.atom_name == \"CA\")]\n",
" C = structure.coord[mask & (structure.atom_name == \"C\")]\n",
"\n",
" Cbeta = extend(C, N, CA, 1.522, 1.927, -2.143)\n",
" dist = squareform(pdist(Cbeta))\n",
" \n",
" contacts = dist < distance_threshold\n",
" contacts = contacts.astype(np.int64)\n",
" contacts[np.isnan(dist)] = -1\n",
" return contacts"
]
},
{
"cell_type": "markdown",
"id": "5473f306",
"metadata": {},
"source": [
"#### Computing contact precisions"
]
},
{
"cell_type": "markdown",
"id": "e361a9f3",
"metadata": {},
"source": [
"Calculate precision metrics to evaluate contact prediction quality. The `compute_precisions` function ranks predicted contacts by confidence and measures how many of the top predictions are true contacts, while `evaluate_prediction` breaks this down by sequence separation ranges (local, short, medium, long-range) since predicting distant contacts is typically much harder than nearby ones."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "62c37bbd",
"metadata": {},
"outputs": [],
"source": [
"def compute_precisions(\n",
" predictions: mx.array,\n",
" targets: mx.array,\n",
" minsep: int = 6,\n",
" maxsep: Optional[int] = None,\n",
" override_length: Optional[int] = None,\n",
") -> Dict[str, mx.array]:\n",
" \"\"\"Compute precision metrics for contact prediction.\"\"\"\n",
" batch_size, seqlen, _ = predictions.shape\n",
" \n",
" if maxsep is not None:\n",
" sep_mask_2d = mx.abs(mx.arange(seqlen)[None, :] - mx.arange(seqlen)[:, None]) <= maxsep\n",
" targets = targets * sep_mask_2d[None, :]\n",
" \n",
" targets = targets.astype(mx.float32)\n",
" src_lengths = (targets >= 0).sum(axis=-1).sum(axis=-1).astype(mx.float32)\n",
" \n",
" x_ind, y_ind = [], []\n",
" for i in range(seqlen):\n",
" for j in range(i + minsep, seqlen):\n",
" x_ind.append(i)\n",
" y_ind.append(j)\n",
" \n",
" x_ind = mx.array(x_ind)\n",
" y_ind = mx.array(y_ind)\n",
" \n",
" predictions_upper = predictions[:, x_ind, y_ind]\n",
" targets_upper = targets[:, x_ind, y_ind]\n",
"\n",
" topk = seqlen if override_length is None else max(seqlen, override_length)\n",
" indices = mx.argsort(predictions_upper, axis=-1)[:, ::-1][:, :topk]\n",
" \n",
" batch_indices = mx.arange(batch_size)[:, None]\n",
" topk_targets = targets_upper[batch_indices, indices]\n",
" \n",
" if topk_targets.shape[1] < topk:\n",
" pad_shape = (topk_targets.shape[0], topk - topk_targets.shape[1])\n",
" padding = mx.zeros(pad_shape)\n",
" topk_targets = mx.concatenate([topk_targets, padding], 1)\n",
"\n",
" cumulative_dist = mx.cumsum(topk_targets, -1)\n",
"\n",
" gather_lengths = src_lengths[:, None]\n",
" if override_length is not None:\n",
" gather_lengths = override_length * mx.ones_like(gather_lengths)\n",
"\n",
" precision_fractions = mx.arange(0.1, 1.1, 0.1)\n",
" gather_indices = (precision_fractions[None, :] * gather_lengths) - 1\n",
" gather_indices = mx.clip(gather_indices, 0, cumulative_dist.shape[1] - 1)\n",
" gather_indices = gather_indices.astype(mx.int32)\n",
"\n",
" binned_cumulative_dist = cumulative_dist[batch_indices, gather_indices]\n",
" binned_precisions = binned_cumulative_dist / (gather_indices + 1)\n",
"\n",
" pl5 = binned_precisions[:, 1]\n",
" pl2 = binned_precisions[:, 4]\n",
" pl = binned_precisions[:, 9]\n",
" auc = binned_precisions.mean(-1)\n",
"\n",
" return {\"AUC\": auc, \"P@L\": pl, \"P@L2\": pl2, \"P@L5\": pl5}\n",
"\n",
"def evaluate_prediction(\n",
" predictions: mx.array,\n",
" targets: mx.array,\n",
") -> Dict[str, float]:\n",
" \"\"\"Evaluate contact predictions across different sequence separation ranges.\"\"\"\n",
" contact_ranges = [\n",
" (\"local\", 3, 6),\n",
" (\"short\", 6, 12),\n",
" (\"medium\", 12, 24),\n",
" (\"long\", 24, None),\n",
" ]\n",
" metrics = {}\n",
" \n",
" for name, minsep, maxsep in contact_ranges:\n",
" rangemetrics = compute_precisions(\n",
" predictions,\n",
" targets,\n",
" minsep=minsep,\n",
" maxsep=maxsep,\n",
" )\n",
" for key, val in rangemetrics.items():\n",
" metrics[f\"{name}_{key}\"] = float(val[0])\n",
" return metrics"
]
},
{
"cell_type": "markdown",
"id": "5873e052",
"metadata": {},
"source": [
"#### Predicting contacts"
]
},
{
"cell_type": "markdown",
"id": "2d5778a9",
"metadata": {},
"source": [
"This function wraps the tokenization and model inference steps, converting a raw amino acid sequence into token IDs and passing them through ESM-2's contact prediction head to produce a contact probability matrix."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dddf31a7",
"metadata": {},
"outputs": [],
"source": [
"def predict_contacts(sequence: str, model, tokenizer) -> mx.array:\n",
" tokens = tokenizer.encode(sequence)\n",
" tokens = mx.array([tokens])\n",
" contacts = model.predict_contacts(tokens)\n",
" return contacts"
]
},
{
"cell_type": "markdown",
"id": "62562401",
"metadata": {},
"source": [
"#### Plotting results\n",
"\n",
"This function visualizes contacts as a symmetric matrix where both axes index residue positions. The lower triangle shows the models confidence as a blue heatmap, with darker cells indicating higher confidence. The upper triangle overlays evaluation markers: blue dots are correctly predicted contacts (true positives), red dots are predicted but not real (false positives), and grey dots are real contacts the model missed (false negatives)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "03e03791",
"metadata": {},
"outputs": [],
"source": [
"def plot_contacts_and_predictions(\n",
" predictions: mx.array,\n",
" contacts: np.ndarray,\n",
" ax,\n",
" title: str,\n",
" cmap: str = \"Blues\",\n",
" ms: float = 1,\n",
"):\n",
" \"\"\"Plot contact predictions and true contacts.\"\"\"\n",
" if isinstance(predictions, mx.array):\n",
" predictions = np.array(predictions)\n",
" \n",
" seqlen = contacts.shape[0]\n",
" relative_distance = np.add.outer(-np.arange(seqlen), np.arange(seqlen))\n",
" bottom_mask = relative_distance < 0\n",
" masked_image = np.ma.masked_where(bottom_mask, predictions)\n",
" invalid_mask = np.abs(np.add.outer(np.arange(seqlen), -np.arange(seqlen))) < 6\n",
" predictions_copy = predictions.copy()\n",
" predictions_copy[invalid_mask] = float(\"-inf\")\n",
"\n",
" topl_val = np.sort(predictions_copy.reshape(-1))[-seqlen]\n",
" pred_contacts = predictions_copy >= topl_val\n",
" true_positives = contacts & pred_contacts & ~bottom_mask\n",
" false_positives = ~contacts & pred_contacts & ~bottom_mask\n",
" other_contacts = contacts & ~pred_contacts & ~bottom_mask\n",
"\n",
" ax.imshow(masked_image, cmap=cmap)\n",
" ax.plot(*np.where(other_contacts), \"o\", c=\"grey\", ms=ms)\n",
" ax.plot(*np.where(false_positives), \"o\", c=\"r\", ms=ms)\n",
" ax.plot(*np.where(true_positives), \"o\", c=\"b\", ms=ms)\n",
" ax.set_title(title)\n",
" ax.axis(\"square\")\n",
" ax.set_xlim([0, seqlen])\n",
" ax.set_ylim([0, seqlen])"
]
},
{
"cell_type": "markdown",
"id": "9364c984",
"metadata": {},
"source": [
"### Predict and visualize\n",
"Here we'll use ESM-2 contact prediction on our three test proteins and evaluate the results. We'll compute precision metrics across different sequence separation ranges and create contact maps that visualize both the model's predictions and how well they match the true protein structures."
]
},
{
"cell_type": "markdown",
"id": "9fa9e59e",
"metadata": {},
"source": [
"#### Read Data"
]
},
{
"cell_type": "markdown",
"id": "7da50dc2",
"metadata": {},
"source": [
"Load experimental protein structures from the Protein Data Bank and extract true contact maps for evaluation, while also parsing the reference sequences from our MSA files that will serve as input to ESM-2."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2d276137",
"metadata": {},
"outputs": [],
"source": [
"PDB_IDS = [\"1a3a\", \"5ahw\", \"1xcr\"]\n",
"\n",
"structures = {\n",
" name.lower(): get_structure(CIFFile.read(rcsb.fetch(name, \"cif\")))[0]\n",
" for name in PDB_IDS\n",
"}\n",
"\n",
"contacts = {\n",
" name: contacts_from_pdb(structure, chain=\"A\") \n",
" for name, structure in structures.items()\n",
"}\n",
"\n",
"msas = {\n",
" name: read_msa(f\"data/{name.lower()}_1_A.a3m\")\n",
" for name in PDB_IDS\n",
"}\n",
"\n",
"sequences = {\n",
" name: msa[0] for name, msa in msas.items()\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "4ce64f18",
"metadata": {},
"source": [
"#### ESM-2 predictions"
]
},
{
"cell_type": "markdown",
"id": "1f2da88f",
"metadata": {},
"source": [
"##### Evaluate predictions"
]
},
{
"cell_type": "markdown",
"id": "0adb0a11",
"metadata": {},
"source": [
"This loop generates contact predictions for each protein using ESM-2, compares them against the experimentally determined structures, and computes precision metrics across different sequence separation ranges to evaluate model performance."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "941b4afa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>model</th>\n",
" <th>local_AUC</th>\n",
" <th>local_P@L</th>\n",
" <th>local_P@L2</th>\n",
" <th>local_P@L5</th>\n",
" <th>short_AUC</th>\n",
" <th>short_P@L</th>\n",
" <th>short_P@L2</th>\n",
" <th>short_P@L5</th>\n",
" <th>medium_AUC</th>\n",
" <th>medium_P@L</th>\n",
" <th>medium_P@L2</th>\n",
" <th>medium_P@L5</th>\n",
" <th>long_AUC</th>\n",
" <th>long_P@L</th>\n",
" <th>long_P@L2</th>\n",
" <th>long_P@L5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1a3a</td>\n",
" <td>ESM-2 (Unsupervised)</td>\n",
" <td>0.193103</td>\n",
" <td>0.193103</td>\n",
" <td>0.193103</td>\n",
" <td>0.193103</td>\n",
" <td>0.172414</td>\n",
" <td>0.172414</td>\n",
" <td>0.172414</td>\n",
" <td>0.172414</td>\n",
" <td>0.262069</td>\n",
" <td>0.262069</td>\n",
" <td>0.262069</td>\n",
" <td>0.262069</td>\n",
" <td>0.689655</td>\n",
" <td>0.689655</td>\n",
" <td>0.689655</td>\n",
" <td>0.689655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5ahw</td>\n",
" <td>ESM-2 (Unsupervised)</td>\n",
" <td>0.024000</td>\n",
" <td>0.024000</td>\n",
" <td>0.024000</td>\n",
" <td>0.024000</td>\n",
" <td>0.136000</td>\n",
" <td>0.136000</td>\n",
" <td>0.136000</td>\n",
" <td>0.136000</td>\n",
" <td>0.144000</td>\n",
" <td>0.144000</td>\n",
" <td>0.144000</td>\n",
" <td>0.144000</td>\n",
" <td>0.864000</td>\n",
" <td>0.864000</td>\n",
" <td>0.864000</td>\n",
" <td>0.864000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1xcr</td>\n",
" <td>ESM-2 (Unsupervised)</td>\n",
" <td>0.111821</td>\n",
" <td>0.111821</td>\n",
" <td>0.111821</td>\n",
" <td>0.111821</td>\n",
" <td>0.159744</td>\n",
" <td>0.159744</td>\n",
" <td>0.159744</td>\n",
" <td>0.159744</td>\n",
" <td>0.175719</td>\n",
" <td>0.175719</td>\n",
" <td>0.175719</td>\n",
" <td>0.175719</td>\n",
" <td>0.408946</td>\n",
" <td>0.408946</td>\n",
" <td>0.408946</td>\n",
" <td>0.408946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id model local_AUC local_P@L local_P@L2 local_P@L5 \\\n",
"0 1a3a ESM-2 (Unsupervised) 0.193103 0.193103 0.193103 0.193103 \n",
"1 5ahw ESM-2 (Unsupervised) 0.024000 0.024000 0.024000 0.024000 \n",
"2 1xcr ESM-2 (Unsupervised) 0.111821 0.111821 0.111821 0.111821 \n",
"\n",
" short_AUC short_P@L short_P@L2 short_P@L5 medium_AUC medium_P@L \\\n",
"0 0.172414 0.172414 0.172414 0.172414 0.262069 0.262069 \n",
"1 0.136000 0.136000 0.136000 0.136000 0.144000 0.144000 \n",
"2 0.159744 0.159744 0.159744 0.159744 0.175719 0.175719 \n",
"\n",
" medium_P@L2 medium_P@L5 long_AUC long_P@L long_P@L2 long_P@L5 \n",
"0 0.262069 0.262069 0.689655 0.689655 0.689655 0.689655 \n",
"1 0.144000 0.144000 0.864000 0.864000 0.864000 0.864000 \n",
"2 0.175719 0.175719 0.408946 0.408946 0.408946 0.408946 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictions = {}\n",
"results = []\n",
"\n",
"for pdb_id in sequences:\n",
" _, sequence = sequences[pdb_id]\n",
" prediction = predict_contacts(sequence, model, tokenizer)\n",
" predictions[pdb_id] = prediction[0]\n",
" \n",
" true_contacts = mx.array(contacts[pdb_id])\n",
" \n",
" min_len = min(prediction.shape[1], true_contacts.shape[0])\n",
" pred_trimmed = prediction[:, :min_len, :min_len]\n",
" true_trimmed = true_contacts[:min_len, :min_len]\n",
" true_trimmed = mx.expand_dims(true_trimmed, axis=0)\n",
" \n",
" metrics = evaluate_prediction(pred_trimmed, true_trimmed)\n",
" result = {\"id\": pdb_id, \"model\": \"ESM-2 (Unsupervised)\"}\n",
" result.update(metrics)\n",
" results.append(result)\n",
"\n",
"results_df = pd.DataFrame(results)\n",
"display(results_df)"
]
},
{
"cell_type": "markdown",
"id": "c5c7418a",
"metadata": {},
"source": [
"The results demonstrate that ESM-2 excels at predicting long-range contacts, with precision scores ranging from 40.9% to 86.4% for residues more than 24 positions apart. Performance is consistently higher for distant contacts compared to local ones. For example, the universal stress protein (5ahw) achieves 86.4% precision for long-range contacts but only 2.4% for local contacts between 3 and 6 residues apart. This trend is observed across all three proteins, with medium-range contacts (1224 residues apart) and short-range contacts (612 residues apart) showing intermediate accuracy. These results suggest that ESM-2 has learned to identify evolutionarily conserved structural motifs that connect distant regions of the sequence, which are often critical for protein fold stability and function."
]
},
{
"cell_type": "markdown",
"id": "487cff51",
"metadata": {},
"source": [
"##### Plot contacts and predictions"
]
},
{
"cell_type": "markdown",
"id": "10291191",
"metadata": {},
"source": [
"This analysis generates contact map visualizations for all three proteins, presenting ESM-2s predictions as heatmaps and overlaying the true experimental contacts as colored dots."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "628efc10",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"proteins = [r['id'] for r in results]\n",
"fig, axes = plt.subplots(figsize=(6 * len(proteins), 6), ncols=len(proteins))\n",
"if len(proteins) == 1:\n",
" axes = [axes]\n",
"\n",
"for ax, pdb_id in zip(axes, proteins):\n",
" prediction = predictions[pdb_id]\n",
" target = contacts[pdb_id]\n",
" \n",
" result = next(r for r in results if r['id'] == pdb_id)\n",
" long_pl = result['long_P@L']\n",
" \n",
" plot_contacts_and_predictions(\n",
" prediction, target, ax=ax, \n",
" title=f\"{pdb_id}: Long Range P@L: {100 * long_pl:.1f}%\"\n",
" )\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "99e1edaf",
"metadata": {},
"source": [
"The contact maps highlight ESM-2s strong ability to detect long-range structural relationships. In each panel, the lower triangle shows model predictions, where darker blue regions indicate high-confidence contacts, and the upper triangle shows the corresponding experimental data. Correct predictions appear as blue dots, forming distinct off-diagonal patterns in 5ahw and 1a3a that capture key global fold interactions. Red dots mark false positives, which are relatively rare, while grey dots represent missed contacts. These missed contacts are notably more frequent in 1xcr, consistent with its lower long-range precision. The dense clusters of blue true positives in 5ahw, compared to the sparser, fragmented patterns in 1xcr, clearly illustrate the variation in predictive performance across proteins."
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}