{ "cells": [ { "cell_type": "markdown", "id": "5c785139", "metadata": {}, "source": [ "# MHC pseudosequence (MPS) vs. peptide contacts\n", "\n", "NetMHCpan defines a 34-residue **pseudosequence** per allele — the polymorphic groove\n", "positions thought to line the peptide-binding cleft. `tcren.mhc.annotate_pseudo` marks those\n", "residues (region `MPS`) on a structure by threading the best-matching pseudosequence through\n", "the MHC chain(s) with a fitting alignment (class I → MHCa; class II → split across MHCa+MHCb).\n", "\n", "Here we check, on committed test structures, whether the MPS residues are really the ones in\n", "contact with the peptide — i.e. whether the NetMHCpan groove definition recovers the structural\n", "interface." ] }, { "cell_type": "code", "execution_count": 1, "id": "111fc748", "metadata": { "execution": { "iopub.execute_input": "2026-06-17T00:39:47.994258Z", "iopub.status.busy": "2026-06-17T00:39:47.994132Z", "iopub.status.idle": "2026-06-17T00:39:48.584191Z", "shell.execute_reply": "2026-06-17T00:39:48.583847Z" }, "language": "python" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "python 3.11.15 | numpy 2.4.6 | matplotlib 3.11.0\n" ] } ], "source": [ "# Environment + imports\n", "import sys, platform\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import tcren\n", "from tcren.structure.io import import_structure\n", "from tcren.annotation import classify_chains\n", "from tcren.mhc import annotate_mhc, annotate_pseudo\n", "print('python', platform.python_version(), '| numpy', np.__version__, '| matplotlib', matplotlib.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "id": "90966470", "metadata": { "execution": { "iopub.execute_input": "2026-06-17T00:39:48.585601Z", "iopub.status.busy": "2026-06-17T00:39:48.585490Z", "iopub.status.idle": "2026-06-17T00:39:48.588697Z", "shell.execute_reply": "2026-06-17T00:39:48.588344Z" }, "language": "python" }, "outputs": [], "source": [ "# Helper: annotate a test structure and return per-MHC-groove-residue min distance to the\n", "# peptide, with a flag for whether the residue is part of the MPS pseudosequence.\n", "ASSETS = '../tests/assets/pdb'\n", "\n", "def min_dist_to_peptide(structure, cutoff_chains=('MHCa', 'MHCb')):\n", " pep = [c for c in structure.chains if c.chain_type == 'PEPTIDE']\n", " pa = np.vstack([a.coord for c in pep for r in c.residues for a in r.atoms])\n", " rows = []\n", " for chain in structure.chains:\n", " if chain.chain_type not in cutoff_chains:\n", " continue\n", " mps = {r.seq_index for reg in chain.regions if reg.region_type == 'MPS' for r in reg.residues}\n", " for res in chain.residues:\n", " ra = np.array([a.coord for a in res.atoms])\n", " if not len(ra):\n", " continue\n", " d = float(np.sqrt(((ra[:, None, :] - pa[None, :, :]) ** 2).sum(-1).min()))\n", " rows.append((chain.chain_id, res.seq_index, d, res.seq_index in mps))\n", " return rows\n", "\n", "def load(pdb, organism='human'):\n", " s = import_structure(f'{ASSETS}/{pdb}.pdb', pdb_id=pdb)\n", " classify_chains(s, organism=organism)\n", " annotate_mhc(s)\n", " best = annotate_pseudo(s)\n", " cls = 'MHC-II' if any(c.chain_type == 'MHCb' for c in s.chains) else 'MHC-I'\n", " return s, best, cls" ] }, { "cell_type": "code", "execution_count": 3, "id": "5e4ffe73", "metadata": { "execution": { "iopub.execute_input": "2026-06-17T00:39:48.589854Z", "iopub.status.busy": "2026-06-17T00:39:48.589776Z", "iopub.status.idle": "2026-06-17T00:39:59.655258Z", "shell.execute_reply": "2026-06-17T00:39:59.654719Z" }, "language": "python" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'query_internal_open_gap_score' was renamed to 'open_internal_deletion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n", "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'query_internal_extend_gap_score' was renamed to 'extend_internal_deletion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n", "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'target_internal_open_gap_score' was renamed to 'open_internal_insertion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n", "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'target_internal_extend_gap_score' was renamed to 'extend_internal_insertion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n", "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'target_end_gap_score' was renamed to 'end_insertion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n", "/opt/homebrew/anaconda3/envs/tcren-nb/lib/python3.11/site-packages/Bio/Align/__init__.py:4413: BiopythonDeprecationWarning: The attribute 'query_end_gap_score' was renamed to 'end_deletion_score'. This was done to be consistent with the\n", "AlignmentCounts object returned by the .counts method of an Alignment object.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot A: per-residue min-distance-to-peptide for the MHC groove chains of an MHC-I (1ao7)\n", "# and an MHC-II (4ozg) complex; MPS residues highlighted. MPS points should sit low (close to\n", "# the peptide) relative to the bulk of groove residues.\n", "examples = [('1ao7', 'human'), ('4ozg', 'human')]\n", "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", "for ax, (pdb, org) in zip(axes, examples):\n", " s, best, cls = load(pdb, org)\n", " rows = min_dist_to_peptide(s)\n", " other = [d for _, _, d, m in rows if not m]\n", " mps = [d for _, _, d, m in rows if m]\n", " ax.scatter(np.random.normal(0, 0.05, len(other)), other, s=12, color='#999999', alpha=0.6, label='other groove')\n", " ax.scatter(np.random.normal(1, 0.05, len(mps)), mps, s=18, color='#D55E00', alpha=0.8, label='MPS')\n", " ax.axhline(5.0, ls='--', lw=1, color='#0072B2')\n", " ax.set_xticks([0, 1]); ax.set_xticklabels(['other', 'MPS'])\n", " ax.set_ylabel('min distance to peptide (Å)')\n", " ax.set_title(f'{pdb} ({cls})\\nbest pseudoseq: {best}')\n", " ax.legend(fontsize=8)\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad730a3f", "metadata": { "execution": { "iopub.execute_input": "2026-06-17T00:39:59.656766Z", "iopub.status.busy": "2026-06-17T00:39:59.656663Z", "iopub.status.idle": "2026-06-17T00:40:25.818994Z", "shell.execute_reply": "2026-06-17T00:40:25.818624Z" }, "language": "python" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot B: across several test complexes, what fraction of MPS residues are within 5 Å of the\n", "# peptide (precision), and what fraction of the 5-Å peptide-contacting MHC residues are MPS\n", "# (recall). MPS captures the groove-lining residues; ~half are direct (<5 Å) peptide contacts.\n", "panel = [('1ao7', 'human'), ('1bd2', 'human'), ('5jhd', 'human'), ('1fo0', 'mouse'), ('4ozg', 'human')]\n", "labels, precision, recall = [], [], []\n", "for pdb, org in panel:\n", " s, best, cls = load(pdb, org)\n", " rows = min_dist_to_peptide(s)\n", " mps = [(d <= 5.0) for _, _, d, m in rows if m]\n", " contact_is_mps = [m for _, _, d, m in rows if d <= 5.0]\n", " labels.append(f'{pdb}\\n{cls}')\n", " precision.append(np.mean(mps) if mps else 0)\n", " recall.append(np.mean(contact_is_mps) if contact_is_mps else 0)\n", "\n", "x = np.arange(len(labels)); w = 0.38\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", "ax.bar(x - w / 2, precision, w, color='#D55E00', label='MPS within 5 Å of peptide (precision)')\n", "ax.bar(x + w / 2, recall, w, color='#0072B2', label='5 Å contacts that are MPS (recall)')\n", "ax.set_xticks(x); ax.set_xticklabels(labels)\n", "ax.set_ylim(0, 1); ax.set_ylabel('fraction')\n", "ax.set_title('MPS pseudosequence vs. structural peptide contacts')\n", "ax.legend(fontsize=8, loc='upper right')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "7a14316d", "metadata": {}, "source": [ "**Reading the plots.** MPS residues sit markedly closer to the peptide than the rest of the\n", "groove (Plot A), and roughly half of them are direct (<5 Å) peptide contacts (Plot B). The\n", "NetMHCpan pseudosequence captures the peptide-binding groove well; the residues it does *not*\n", "place within 5 Å are the groove-lining positions that point toward the TCR or sit at the cleft\n", "ends — still part of the functional groove, just not in van-der-Waals contact with this peptide." ] } ], "metadata": { "kernelspec": { "display_name": "Python (tcren-nb)", "language": "python", "name": "tcren-nb" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 5 }