diff --git a/README.md b/README.md
index 1b1c987..0246dc9 100644
--- a/README.md
+++ b/README.md
@@ -74,6 +74,12 @@ nohup ./run.sh <scenario.yaml> > run.out & disown && tail -f run.out
 
 See [USAGE.md](USAGE.md) for the full guide, SLURM configuration, and troubleshooting.
 
+## Interactive notebook
+
+The [`notebook/HADDOCK3_protein_peptide_small_benchmark.ipynb`](notebook/HADDOCK3_protein_peptide_small_benchmark.ipynb) notebook provides a guided, end-to-end walkthrough of the protein–peptide benchmarking workflow using a small subset of five complexes.
+
+It covers the complete workflow, including software setup, dataset preparation, benchmark execution with `haddock-runner`, HADDOCK3 docking, and analysis.
+
 ## Pipeline Overview
 ```mermaid
 %%{init: {'theme': 'default'}}%%
diff --git a/notebook/HADDOCK3_protein_peptide_small_benchmark.ipynb b/notebook/HADDOCK3_protein_peptide_small_benchmark.ipynb
new file mode 100644
index 0000000..2dc74a2
--- /dev/null
+++ b/notebook/HADDOCK3_protein_peptide_small_benchmark.ipynb
@@ -0,0 +1,935 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "protein_peptide_banner.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "3706f2c7",
+   "metadata": {},
+   "source": [
+    "# **HADDOCK3 Protein–Peptide Small Benchmark**\n",
+    "\n",
+    "![Example protein-peptide complexes](attachment:protein_peptide_banner.png)\n",
+    "\n",
+    "*Example protein–peptide complex, PDB-3AWR (peptide in red, receptor surface in grey).*\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates a benchmark workflow for docking of protein–peptide\n",
+    "complexes using HADDOCK3.\n",
+    "\n",
+    "[HADDOCK3](https://github.com/haddocking/haddock3) is the latest, fully modular\n",
+    "version of HADDOCK (High Ambiguity Driven biomolecular DOCKing) — an\n",
+    "information-driven, integrative docking software developed by the\n",
+    "[BonvinLab](https://www.bonvinlab.org/) at Utrecht University. \n",
+    "This notebook is part of the [HADDOCK3 benchmarking suite](https://github.com/haddocking/benchmarking),\n",
+    "a collection of curated docking benchmark datasets and scenarios (protein–protein,\n",
+    "protein–peptide, protein–glycan, and more).\n",
+    "\n",
+    "This notebook runs a small mini-benchmark of protein–peptide complexes\n",
+    "docked with HADDOCK3, orchestrated by\n",
+    "[haddock-runner](https://github.com/haddocking/haddock-runner) — the tool that reads\n",
+    "a benchmark scenario YAML, prepares and launches one HADDOCK3 run per complex, and\n",
+    "monitors them to completion.\n",
+    "\n",
+    "The purpose of this notebook is not to reproduce the full protein–peptide benchmark.\n",
+    "Instead, it provides a compact **sanity/benchmarking** test to verify that the\n",
+    "HADDOCK3 protein–peptide workflow runs correctly end to end, from topology\n",
+    "generation to CAPRI-based evaluation, on a small subset of five complexes. Overall, this notebook walks through every step of the full reproduction pipeline —\n",
+    "install, dataset setup, run, and analysis — so it can be read as a complete,\n",
+    "self-contained overview of how to reproduce a HADDOCK3 protein–peptide benchmark\n",
+    "from scratch.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cfc77a6",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "---\n",
+    "# **A brief introduction to HADDOCK3**\n",
+    "\n",
+    "HADDOCK3 is the next generation integrative modelling software in the\n",
+    "long-lasting HADDOCK project. It represents a complete rethinking and rewriting\n",
+    "of the HADDOCK2.X series, implementing a new way to interact with HADDOCK and\n",
+    "offering new features to users who can now define custom workflows.\n",
+    "\n",
+    "In the previous HADDOCK2.x versions, users had access to a highly\n",
+    "parameterisable yet rigid simulation pipeline composed of three steps:\n",
+    "`rigid-body docking (it0)`, `semi-flexible refinement (it1)`, and `final refinement (itw)`.\n",
+    "\n",
+    "<figure style=\"text-align: center;\">\n",
+    "<img width=\"75%\" src=\"https://bonvinlab.org/education/HADDOCK3/HADDOCK3-antibody-antigen/HADDOCK2-stages.png\">\n",
+    "</figure>\n",
+    "\n",
+    "In HADDOCK3, users have the freedom to configure docking workflows into\n",
+    "functional pipelines by combining the different HADDOCK3 modules, thus\n",
+    "adapting the workflows to their projects. HADDOCK3 has therefore developed to\n",
+    "truthfully work like a puzzle of many pieces (simulation modules) that users combine freely. To this end, the old HADDOCK machinery has been modularized,\n",
+    "and several new modules added, including third-party software additions. As a\n",
+    "result, the modularization achieved in HADDOCK3 allows users to duplicate steps\n",
+    "within one workflow (e.g., to repeat twice the `it1` stage of the HADDOCK2.x\n",
+    "rigid workflow).\n",
+    "\n",
+    "Note that, for simplification purposes, at this time, not all functionalities of\n",
+    "HADDOCK2.x have been ported to HADDOCK3, which does not (yet) support NMR RDC,\n",
+    "PCS and diffusion anisotropy restraints, cryo-EM restraints and coarse-graining.\n",
+    "Any type of information that can be converted into ambiguous interaction\n",
+    "restraints can, however, be used in HADDOCK3, which also supports the\n",
+    "*ab initio* docking modes of HADDOCK.\n",
+    "\n",
+    "<figure style=\"text-align: center;\">\n",
+    "<img width=\"75%\" src=\"https://bonvinlab.org/education/HADDOCK3/HADDOCK3-antibody-antigen/HADDOCK3-workflow-scheme.png\">\n",
+    "</figure>\n",
+    "\n",
+    "To keep HADDOCK3 modules organized, we catalogued them into several\n",
+    "categories. However, there are no constraints on piping modules of different\n",
+    "categories.\n",
+    "\n",
+    "The main module categories are \"topology\", \"sampling\", \"refinement\",\n",
+    "\"scoring\", and \"analysis\". There is no limit to how many modules can belong to a\n",
+    "category. Modules are added as developed, and new categories will be created\n",
+    "if/when needed. You can access the HADDOCK3 documentation page for the list of\n",
+    "all categories and modules. Below is a summary of the available modules:\n",
+    "\n",
+    "* **Topology modules**\n",
+    "    * `topoaa`: *generates the all-atom topologies for the CNS engine.*\n",
+    "* **Sampling modules**\n",
+    "    * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*\n",
+    "    * `lightdock`: *Third-party glow-worm swam optimization docking software.*\n",
+    "* **Model refinement modules**\n",
+    "    * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*\n",
+    "    * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*\n",
+    "    * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*\n",
+    "* **Scoring modules**\n",
+    "    * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*\n",
+    "    * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*\n",
+    "* **Analysis modules**\n",
+    "    * `alascan`: *Performs a systematic (or user-define) alanine scanning mutagenesis of interface residues.*\n",
+    "    * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top-scoring model or reference structure if provided.*\n",
+    "    * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*\n",
+    "    * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*\n",
+    "    * `contactmap`: *Generate contact matrices of both intra- and intermolecular contacts and a chordchart of intermolecular contacts.*\n",
+    "    * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*\n",
+    "    * `ilrmsdmatrix`: *Calculates the pairwise interface-ligand-RMSD (il-RMSD) matrix between all the models generated in the previous step.*\n",
+    "    * `seletop`: *Selects the top N models from the previous step.*\n",
+    "    * `seletopclusts`: *Selects the top N clusters from the previous step.*\n",
+    "\n",
+    "The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.\n",
+    "Contrary to HADDOCK2.X which follows a rigid (yet highly parameterisable)\n",
+    "procedure, in HADDOCK3, you can create your own simulation workflows by\n",
+    "combining a multitude of independent modules that perform specialized tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a731084",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Software and data setup**\n",
+    "\n",
+    "For reproducibility, this notebook uses pinned versions of the required software:\n",
+    "- **HADDOCK3:** `2026.8.0`, installed from PyPI\n",
+    "- **haddock-runner:** `4.1.0`, downloaded as a prebuilt binary from the corresponding GitHub release\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc2446c4",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 1. Install HADDOCK3 and haddock-runner\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# Pin versions for reproducibility\n",
+    "HADDOCK3_VERSION = \"2026.8.0\"\n",
+    "HADDOCK_RUNNER_TAG = \"v4.1.0\"\n",
+    "\n",
+    "# Install HADDOCK3 in the current Jupyter kernel environment\n",
+    "!{sys.executable} -m pip install -q haddock3=={HADDOCK3_VERSION} pandas matplotlib\n",
+    "\n",
+    "# Download the pinned haddock-runner release\n",
+    "TARGET = \"x86_64-unknown-linux-musl\"\n",
+    "\n",
+    "ARCHIVE = f\"haddock-runner-{HADDOCK_RUNNER_TAG}-{TARGET}.tar.gz\"\n",
+    "URL = f\"https://github.com/haddocking/haddock-runner/releases/download/{HADDOCK_RUNNER_TAG}/{ARCHIVE}\"\n",
+    "\n",
+    "!wget -q \"$URL\" -O /tmp/haddock-runner.tar.gz\n",
+    "!tar -xzf /tmp/haddock-runner.tar.gz -C /tmp\n",
+    "!mkdir -p ~/.local/bin\n",
+    "!cp /tmp/haddock-runner ~/.local/bin/haddock-runner\n",
+    "!chmod +x ~/.local/bin/haddock-runner\n",
+    "\n",
+    "os.environ[\"PATH\"] = f\"{os.path.expanduser('~')}/.local/bin:{os.environ['PATH']}\"\n",
+    "\n",
+    "print(\"HADDOCK3:\")\n",
+    "!haddock3 --version\n",
+    "\n",
+    "print(\"\\nhaddock-runner:\")\n",
+    "!haddock-runner --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f1f736b",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 2. Download the benchmarking repository\n",
+    "from pathlib import Path\n",
+    "\n",
+    "BASE_DIR = Path.cwd()\n",
+    "BENCHMARK_DIR = BASE_DIR / \"benchmarking\"\n",
+    "\n",
+    "if not BENCHMARK_DIR.exists():\n",
+    "    !git clone --depth 1 https://github.com/haddocking/benchmarking.git \"$BENCHMARK_DIR\"\n",
+    "else:\n",
+    "    print(f\"{BENCHMARK_DIR} already exists; using the existing checkout.\")\n",
+    "\n",
+    "PP_DIR = BENCHMARK_DIR / \"docking_benchmarks\" / \"protein_peptide\"\n",
+    "print(\"Protein-peptide benchmark directory:\", PP_DIR)\n",
+    "\n",
+    "print(\"\\nContents:\")\n",
+    "!ls -la \"$PP_DIR\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5799898",
+   "metadata": {},
+   "source": [
+    "## Preparing the protein–peptide dataset\n",
+    "\n",
+    "This step clones the `haddocking/protein-peptide` dataset repo into\n",
+    "`protein-peptide-dataset/`. This repo holds the 98 protein–peptide complexes used\n",
+    "for the docking benchmark — this notebook only uses 5 of them, since it's meant as\n",
+    "a quick end-to-end sanity test rather than a full benchmark run.\n",
+    "\n",
+    "We don't need the full 98-complex input list `setup.sh` builds. Cell 4 below writes\n",
+    "a much smaller version for just our five test complexes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66b9d255",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 3. Download the protein–peptide dataset\n",
+    "DATASET_DIR = PP_DIR / \"protein-peptide-dataset\"\n",
+    "\n",
+    "if not DATASET_DIR.exists():\n",
+    "    !git clone -q https://github.com/haddocking/protein-peptide.git \"$DATASET_DIR\"\n",
+    "\n",
+    "print(\"Dataset ready at:\", DATASET_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fcceee7",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 4. Create the five-complex test input list\n",
+    "TEST_INPUT = PP_DIR / \"protein-peptide-input-test.txt\"\n",
+    "complexes = [\"1AWR\", \"1CE1\", \"1CKA\", \"1CZY\", \"1D4T\"]\n",
+    "SUFFIXES = (\"_r_u.pdb\", \"_l_u.pdb\", \"_b_ambig.tbl\", \"_target.pdb\")\n",
+    "\n",
+    "# One line per input file (not per PDB code): haddock-runner groups lines by\n",
+    "# their parent folder name to build one HADDOCK3 run per complex.\n",
+    "lines = []\n",
+    "for pdb_id in complexes:\n",
+    "    complex_dir = DATASET_DIR / pdb_id\n",
+    "    for f in sorted(complex_dir.glob(f\"{pdb_id}_*\")):\n",
+    "        if f.name.endswith(SUFFIXES):\n",
+    "            lines.append(str(f.relative_to(BENCHMARK_DIR)))\n",
+    "\n",
+    "TEST_INPUT.write_text(\"\\n\".join(lines) + \"\\n\")\n",
+    "print(f\"protein-peptide-input-test.txt written with {len(lines)} file entries for {len(complexes)} complexes.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c40e50b-dfa0-49cc-9162-ab6125d8c88d",
+   "metadata": {},
+   "source": [
+    "# **Inspecting the small benchmark input**\n",
+    "\n",
+    "`protein-peptide-input-test.txt` is the exact small input list this notebook uses,\n",
+    "built above for just the five test complexes (the repository's own copy of\n",
+    "this file, checked into version control, covers the same five complexes and would\n",
+    "contain identical entries).\n",
+    "\n",
+    "**Format:** this is a plain text file with **one input file path per line**.\n",
+    "`haddock-runner` reads every line, groups the files by their parent folder\n",
+    "name (the PDB code), and starts one HADDOCK3 run per group. A target with\n",
+    "no lines in this file, or with lines pointing at files that don't exist,\n",
+    "simply won't get a run.\n",
+    "\n",
+    "It contains files belonging to five systems:\n",
+    "\n",
+    "- `1AWR`\n",
+    "- `1CE1`\n",
+    "- `1CKA`\n",
+    "- `1CZY`\n",
+    "- `1D4T`\n",
+    "\n",
+    "The filename conventions are:\n",
+    "\n",
+    "- `_r_u.pdb`: unbound receptor\n",
+    "- `_l_u.pdb`: unbound peptide ensemble\n",
+    "- `_b_ambig.tbl`: AIR restraints derived from the bound interface\n",
+    "- `_target.pdb`: reference/native complex used by `caprieval`\n",
+    "\n",
+    "So a well-formed test file looks like this:\n",
+    "\n",
+    "```\n",
+    "docking_benchmarks/protein_peptide/protein-peptide-dataset/1AWR/1AWR_r_u.pdb\n",
+    "docking_benchmarks/protein_peptide/protein-peptide-dataset/1AWR/1AWR_l_u.pdb\n",
+    "docking_benchmarks/protein_peptide/protein-peptide-dataset/1AWR/1AWR_b_ambig.tbl\n",
+    "docking_benchmarks/protein_peptide/protein-peptide-dataset/1AWR/1AWR_target.pdb\n",
+    "```\n",
+    "\n",
+    "The test file isn't passed to `haddock-runner` directly — it's referenced by name from\n",
+    "the `general.input_list` key of the scenario YAML:\n",
+    "\n",
+    "```yaml\n",
+    "general:\n",
+    "  input_list: protein-peptide-input-test.txt\n",
+    "  work_dir: benchmark_output/unbound_true_interface-test\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1458ff80",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 5. Show the exact protein-peptide-input-test.txt file\n",
+    "print(TEST_INPUT.read_text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ccab823",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Inspecting the benchmark scenario**\n",
+    "\n",
+    "The test YAML is already part of the benchmarking repository. We use it unchanged.\n",
+    "\n",
+    "Important parameters include:\n",
+    "\n",
+    "```yaml\n",
+    "general:\n",
+    "  input_list: protein-peptide-input-test.txt\n",
+    "  work_dir: benchmark_output/unbound_true_interface-test\n",
+    "  execution: local\n",
+    "  ncores: 4\n",
+    "  max_concurrent: 1\n",
+    "  mol_suffixes:\n",
+    "    - _r_u\n",
+    "    - _l_u              # multi-model peptide ensemble, sampled automatically\n",
+    "\n",
+    "scenarios:\n",
+    "  - name: run-unbound-true_interface-test\n",
+    "    workflow:\n",
+    "        topoaa:\n",
+    "\n",
+    "        rigidbody:\n",
+    "          sampling: 30\n",
+    "          ambig_fname: _b_ambig.tbl\n",
+    "\n",
+    "        caprieval.1:\n",
+    "          reference_fname: _target.pdb\n",
+    "\n",
+    "        seletop:\n",
+    "          select: 4\n",
+    "\n",
+    "        caprieval.2:\n",
+    "          reference_fname: _target.pdb\n",
+    "\n",
+    "        flexref:\n",
+    "          tolerance: 25\n",
+    "          ambig_fname: _b_ambig.tbl\n",
+    "\n",
+    "        caprieval.3:\n",
+    "          reference_fname: _target.pdb\n",
+    "\n",
+    "        emref:\n",
+    "          ambig_fname: _b_ambig.tbl\n",
+    "\n",
+    "        caprieval.4:\n",
+    "          reference_fname: _target.pdb\n",
+    "\n",
+    "        clustfcc:\n",
+    "\n",
+    "        seletopclusts:\n",
+    "\n",
+    "        caprieval.5:\n",
+    "          reference_fname: _target.pdb\n",
+    "```\n",
+    "\n",
+    "This makes the notebook suitable as a small functional benchmark rather than a\n",
+    "full production-scale run. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c335710a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Running the benchmark**\n",
+    "\n",
+    "`haddock-runner` reads the YAML, groups the files belonging to each target,\n",
+    "creates one HADDOCK3 run per system, and executes the test locally. \n",
+    "The output directory defined by the YAML is:\n",
+    "`benchmark_output/unbound_true_interface-test`\n",
+    "\n",
+    "Because the YAML uses paths relative to the protein–peptide benchmark directory,\n",
+    "we execute the runner from that directory (`cwd=BENCHMARK_DIR`, since\n",
+    "`docking_benchmarks/protein_peptide/...` is itself relative to the repo root)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54548cc5",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 6. Run the five-complex HADDOCK3 protein–peptide mini benchmark\n",
+    "import subprocess\n",
+    "import re\n",
+    "\n",
+    "cmd = [\n",
+    "    \"haddock-runner\",\n",
+    "    \"docking_benchmarks/protein_peptide/\"\n",
+    "    \"unbound_true_interface_act-pass-test.yaml\",\n",
+    "]\n",
+    "\n",
+    "process = subprocess.Popen(\n",
+    "    cmd,\n",
+    "    cwd=BENCHMARK_DIR,\n",
+    "    stdout=subprocess.PIPE,\n",
+    "    stderr=subprocess.STDOUT,\n",
+    "    text=True,\n",
+    "    bufsize=1,\n",
+    ")\n",
+    "\n",
+    "ansi_escape = re.compile(r\"\\x1b\\[[0-9;]*m\")\n",
+    "all_lines = []\n",
+    "\n",
+    "for line in process.stdout:\n",
+    "    clean_line = ansi_escape.sub(\"\", line).rstrip()\n",
+    "    all_lines.append(clean_line)\n",
+    "\n",
+    "    # Keep the live log readable, but don't let this filter hide a failure:\n",
+    "    # every line is still kept in `all_lines` and printed in full below if\n",
+    "    # haddock-runner exits with an error.\n",
+    "    if \"[INFO]\" in clean_line:\n",
+    "        print(clean_line)\n",
+    "\n",
+    "returncode = process.wait()\n",
+    "\n",
+    "if returncode != 0:\n",
+    "    print(\"\\nhaddock-runner failed. Full log (including non-[INFO] lines):\\n\")\n",
+    "    print(\"\\n\".join(all_lines))\n",
+    "    raise subprocess.CalledProcessError(returncode, cmd)\n",
+    "else:\n",
+    "    print(\"\\nhaddock-runner completed successfully for all targets.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca1be919",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Inspecting the benchmark outputs**\n",
+    "\n",
+    "HADDOCK3 + `haddock-runner` benchmark analysis happens at two levels:\n",
+    "\n",
+    "1. **Per-run analysis (automatic).** Every `caprieval` step in the workflow already\n",
+    "   writes its own `N_caprieval_analysis/` folder inside each target's `run1/`\n",
+    "   directory, containing `capri_ss.tsv` (the raw per-model CAPRI metrics table) and an\n",
+    "   HTML report (`report.html`) with per-metric plots. Because the scenario sets\n",
+    "   `gen_archive: true`, this whole `run1/` directory is archived away as\n",
+    "   `run1_analysis.tgz` (plus a `run1.tgz` of the full run).\n",
+    "2. **Benchmark-wide analysis (manual, run once all targets finish).**\n",
+    "   `analysis/analysebenchmarkresults.py` aggregates the `capri_ss.tsv` files across\n",
+    "   every target and scenario, ranks models by HADDOCK score, and produces\n",
+    "   comparison plots and a JSON summary across the whole test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed81197c",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 7. Extract run1_analysis archives\n",
+    "import subprocess\n",
+    "\n",
+    "OUTPUT_DIR = (\n",
+    "    PP_DIR\n",
+    "    / \"benchmark_output\"\n",
+    "    / \"unbound_true_interface-test\"\n",
+    "    / \"run-unbound-true_interface-test\"\n",
+    ")\n",
+    "\n",
+    "targets = [\"1AWR\", \"1CE1\", \"1CKA\", \"1CZY\", \"1D4T\"]\n",
+    "\n",
+    "extracted, failed = [], []\n",
+    "for target in targets:\n",
+    "    target_dir = OUTPUT_DIR / target\n",
+    "    archive = target_dir / \"run1_analysis.tgz\"\n",
+    "\n",
+    "    if not archive.exists():\n",
+    "        print(f\"Skipping {target}: {archive.name} not found (this target may have failed).\")\n",
+    "        failed.append(target)\n",
+    "        continue\n",
+    "\n",
+    "    print(f\"Extracting {target}...\")\n",
+    "    try:\n",
+    "        subprocess.run([\"tar\", \"-xzf\", str(archive)], cwd=target_dir, check=True)\n",
+    "        extracted.append(target)\n",
+    "    except subprocess.CalledProcessError as exc:\n",
+    "        print(f\"Failed to extract {target}: {exc}\")\n",
+    "        failed.append(target)\n",
+    "\n",
+    "print(f\"\\nExtracted: {extracted}\")\n",
+    "if failed:\n",
+    "    print(f\"Not extracted (skipped): {failed}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78245bef",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 8. Locate CAPRI result tables\n",
+    "\n",
+    "capri_files = (\n",
+    "    sorted(OUTPUT_DIR.rglob(\"capri_ss.tsv\"))\n",
+    "    if OUTPUT_DIR.exists()\n",
+    "    else []\n",
+    ")\n",
+    "\n",
+    "print(f\"Found {len(capri_files)} capri_ss.tsv files in total\")\n",
+    "print(\"\\nShowing results for 1AWR only:\")\n",
+    "\n",
+    "for f in capri_files:\n",
+    "    if \"1AWR\" in f.parts:\n",
+    "        print(f.relative_to(OUTPUT_DIR))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a28c7262",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Inspecting the CAPRI result tables**\n",
+    "\n",
+    "Once a HADDOCK3 run (or, here, a `haddock-runner` benchmark run) completes, the\n",
+    "resulting `run1/` directory contains the various steps (modules) of the defined\n",
+    "workflow numbered sequentially, e.g.:\n",
+    "\n",
+    "```\n",
+    "> ls run1/\n",
+    "  00_topoaa\n",
+    "  01_rigidbody\n",
+    "  02_caprieval\n",
+    "  03_seletop\n",
+    "  04_flexref\n",
+    "  05_caprieval\n",
+    "  ...\n",
+    "  analysis\n",
+    "  data\n",
+    "  log\n",
+    "  traceback\n",
+    "```\n",
+    "\n",
+    "Besides the numbered module directories, a run also contains:\n",
+    "\n",
+    "* the `data` directory, containing the input data (PDB and restraint files) for the\n",
+    "  various modules\n",
+    "* the `analysis` directory, containing plots to visualise the results of each\n",
+    "  `caprieval` step\n",
+    "* the `traceback` directory, containing the names of the generated models for each\n",
+    "  step, so that a model can be traced back throughout the various stages\n",
+    "\n",
+    "The simplest way to extract ranking information and the corresponding HADDOCK scores\n",
+    "is to look at the `X_caprieval` directories. Each of these directories always contains\n",
+    "a `capri_ss.tsv` file, with the model names, rankings and statistics (score, i-RMSD,\n",
+    "Fnat, l-RMSD, ilRMSD and DockQ score), e.g. from a `flexref`-stage `caprieval`:\n",
+    "\n",
+    "```\n",
+    "../07_flexref/flexref_127.pdb\t-\t1\t-180.605\t1.223\t0.864\t3.249\t3.253\t0.779\t...\n",
+    "../07_flexref/flexref_92.pdb\t-\t2\t-170.394\t1.224\t0.795\t3.256\t3.263\t0.756\t...\n",
+    "../07_flexref/flexref_226.pdb\t-\t3\t-169.150\t1.316\t0.682\t3.583\t3.576\t0.699\t...\n",
+    "../07_flexref/flexref_44.pdb\t-\t4\t-168.426\t1.141\t0.750\t3.058\t3.059\t0.756\t...\n",
+    "../07_flexref/flexref_159.pdb\t-\t5\t-160.324\t1.260\t0.636\t3.454\t3.464\t0.694\t...\n",
+    "```\n",
+    "### Protein–peptide model quality criteria\n",
+    "\n",
+    "The quality of a protein–peptide docking model is assessed according to the **CAPRI classification**, using **i-RMSD** and **DockQ** as quality metrics. Lower i-RMSD values and higher DockQ values indicate better agreement with the reference complex.\n",
+    "\n",
+    "#### i-RMSD classification\n",
+    "\n",
+    "| Quality | Range (Å) |\n",
+    "|---|---:|\n",
+    "| **High** | 0–0.5 |\n",
+    "| **Medium** | 0.5–1.0 |\n",
+    "| **Acceptable** | 1.0–2.0 |\n",
+    "| **Near-acceptable** | 2.0–3.0 |\n",
+    "| **Low** | ≥ 3.0 |\n",
+    "\n",
+    "#### DockQ classification\n",
+    "\n",
+    "| Quality | Range |\n",
+    "|---|---:|\n",
+    "| **High** | 0.895–1.0 |\n",
+    "| **Medium** | 0.71–0.895 |\n",
+    "| **Acceptable** | 0.43–0.71 |\n",
+    "| **Near-acceptable** | 0.35–0.43 |\n",
+    "| **Low** | < 0.35 |\n",
+    "\n",
+    "These thresholds are used by the benchmarking analysis to assign protein–peptide docking models to the **High**, **Medium**, **Acceptable**, **Near-acceptable**, and **Low**  quality categories.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f61f6d1",
+   "metadata": {},
+   "source": [
+    "### **Inspecting the CAPRI results**\n",
+    "\n",
+    "As an example, we will inspect the final `capri_ss.tsv` table for **1AWR**.\n",
+    "\n",
+    "This table contains the CAPRI evaluation metrics for the generated docking models, including the **HADDOCK score, i-RMSD, Fnat, l-RMSD, il-RMSD, and DockQ score**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc9c5c8f",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 9. Inspect the final CAPRI table for 1AWR\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def final_capri_table_per_target(capri_files, output_dir):\n",
+    "    \"\"\"Find the final caprieval table available for each target.\"\"\"\n",
+    "    tables_by_stage = {}\n",
+    "\n",
+    "    for f in capri_files:\n",
+    "        target = f.relative_to(output_dir).parts[0]\n",
+    "        stage = f.parent.name\n",
+    "        stage_num = int(stage.split(\"_\", 1)[0])\n",
+    "\n",
+    "        df = pd.read_csv(f, sep=\"\\t\")\n",
+    "        tables_by_stage.setdefault(target, {})[stage_num] = (stage, df)\n",
+    "\n",
+    "    final_tables = {}\n",
+    "    for target, stages in tables_by_stage.items():\n",
+    "        final_stage_num = max(stages)\n",
+    "        final_tables[target] = stages[final_stage_num]\n",
+    "\n",
+    "    return final_tables\n",
+    "\n",
+    "\n",
+    "tables = final_capri_table_per_target(capri_files, OUTPUT_DIR)\n",
+    "\n",
+    "target = \"1AWR\"\n",
+    "\n",
+    "cols_of_interest = [\n",
+    "    \"model\",\n",
+    "    \"caprieval_rank\",\n",
+    "    \"score\",\n",
+    "    \"irmsd\",\n",
+    "    \"fnat\",\n",
+    "    \"lrmsd\",\n",
+    "    \"ilrmsd\",\n",
+    "    \"dockq\",\n",
+    "]\n",
+    "\n",
+    "if target not in tables:\n",
+    "    print(f\"No CAPRI result found for {target}.\")\n",
+    "else:\n",
+    "    stage_name, df = tables[target]\n",
+    "\n",
+    "    print(f\"Showing final CAPRI table for {target}\")\n",
+    "    print(f\"Final caprieval stage: {stage_name}\")\n",
+    "\n",
+    "    available_cols = [c for c in cols_of_interest if c in df.columns]\n",
+    "\n",
+    "    display(df[available_cols] if available_cols else df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "412a703c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Full benchmark-wide analysis (`analysebenchmarkresults.py`)**\n",
+    "\n",
+    "`analysis/analysebenchmarkresults.py` (v2.0.0 in the current repository) expects a\n",
+    "`<benchmark_directory>/<scenario_name>/<PDBid>/run1/` layout — which is exactly what\n",
+    "`haddock-runner` produced above — and, when run with `--from-archive` (`-a`), reads\n",
+    "`capri_ss.tsv` directly out of each target's `run1_analysis.tgz` rather than requiring\n",
+    "a live `run1/` directory (which, with `gen_archive: true`, never exists on disk).\n",
+    "\n",
+    "For every scenario it ranks models by HADDOCK score and reports, at a series of\n",
+    "top-X thresholds, the fraction of targets whose best-ranked model reaches each CAPRI\n",
+    "quality class. Because this test only ever produces 30 rigid-body models per target,\n",
+    "the script's defaults don't fit our sampling size, so Cell 11 below patches **two**\n",
+    "module-level constants in the script before running it:\n",
+    "\n",
+    "- `TOP_X_THRESHOLDS` — the top-X thresholds used for the bar/violin plots and the\n",
+    "  JSON summary. Default is `(1, 5, 10, 20, 50, 100, 200, 500, 1000)`; we shrink it to\n",
+    "  `(1, 5, 10, 15, 20, 25, 30)`.\n",
+    "- `MELQUIPLOT_NB_ENTRIES` — a **separate** constant (default `200`) that only\n",
+    "  controls how many top-ranked entries the melqui plot (`--melquiplots`) shows per\n",
+    "  target. It is not part of `TOP_X_THRESHOLDS`, so patching only that first constant\n",
+    "  silently leaves the melqui plot asking for a \"top 200\" that doesn't exist in a\n",
+    "  30-model run. We now patch this one too, down to `30`.\n",
+    "\n",
+    "Key options used below:\n",
+    "\n",
+    "- `-t peptide` — use the peptide-specific CAPRI thresholds (see table above) and the\n",
+    "  default `irmsd` metric.\n",
+    "- `-a` / `--from-archive` — read straight from the `run1_analysis.tgz` archives,\n",
+    "  since that's the only place the results live once `gen_archive: true` is set.\n",
+    "- `--custom-labels stage_labels.json` — give the auto-numbered `caprieval` stages\n",
+    "  (`02`, `04`, `06`, `08`, `11`) human-readable titles in the plots.\n",
+    "- `-o` — write the output figures/JSON into a dedicated `benchmark_analysis/`\n",
+    "  directory instead of the default `Analysis/` inside the benchmark output tree.\n",
+    "- `-q` / `--quiet` — silence the script's own banner and the `NN.NN %` progress\n",
+    "  lines it prints to stdout while generating each plot; only errors (and the\n",
+    "  figures/JSON summary) are shown.\n",
+    "\n",
+    "Output files (see `analysis/README.md` for the full description):\n",
+    "\n",
+    "| File | Contents |\n",
+    "|---|---|\n",
+    "| `*_capribarplots.png` | Stacked bar plots: fraction of targets reaching High / Medium / Acceptable quality at each top-X threshold, per pipeline stage |\n",
+    "| `*_performances.json` | The same information as machine-readable JSON |\n",
+    "| `*_benchmark_mapper.json` | Internal scenario/target/stage → file-path mapping |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fc36d03",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 10. Create custom stage labels\n",
+    "import json\n",
+    "\n",
+    "# Maps the auto-numbered caprieval stages of THIS scenario to readable titles.\n",
+    "# Stage order in unbound_true_interface_act-pass-test.yaml:\n",
+    "#   00 topoaa -> 01 rigidbody -> 02 caprieval.1 -> 03 seletop -> 04 caprieval.2\n",
+    "#   -> 05 flexref -> 06 caprieval.3 -> 07 emref -> 08 caprieval.4\n",
+    "#   -> 09 clustfcc -> 10 seletopclusts -> 11 caprieval.5\n",
+    "CUSTOM_LABELS = {\n",
+    "    \"02\": \"Rigid-body docking\",\n",
+    "    \"04\": \"Top-4 selection (seletop)\",\n",
+    "    \"06\": \"Flexible refinement\",\n",
+    "    \"08\": \"Energy minimisation\",\n",
+    "    \"11\": \"Final clustered models\",\n",
+    "}\n",
+    "\n",
+    "LABELS_FILE = PP_DIR / \"stage_labels.json\"\n",
+    "\n",
+    "with open(LABELS_FILE, \"w\") as f:\n",
+    "    json.dump(CUSTOM_LABELS, f, indent=4)\n",
+    "\n",
+    "print(f\"Wrote {LABELS_FILE}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29a3f2f6",
+   "metadata": {
+    "cellView": "form"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 11. Run the full benchmark-wide analysis and display figures\n",
+    "import re\n",
+    "import subprocess\n",
+    "import sys\n",
+    "\n",
+    "from IPython.display import Image, display\n",
+    "\n",
+    "RESULTS_DIR = PP_DIR / \"benchmark_output\" / \"unbound_true_interface-test\"\n",
+    "ANALYSIS_DIR = PP_DIR / \"benchmark_analysis\"\n",
+    "ANALYSIS_SCRIPT = BENCHMARK_DIR / \"analysis\" / \"analysebenchmarkresults.py\"\n",
+    "\n",
+    "# This tiny test only ever produces 30 rigid-body models per target, so two of\n",
+    "# the script's module-level constants need shrinking before we run it:\n",
+    "#   - TOP_X_THRESHOLDS: default (1, 5, 10, 20, 50, 100, 200, 500, 1000) -- used\n",
+    "#     by the bar/violin plots and the JSON summary.\n",
+    "#   - MELQUIPLOT_NB_ENTRIES: default 200 -- a *separate* constant that only\n",
+    "#     controls the melqui plot's \"top N\" cutoff. It's easy to miss because it\n",
+    "#     isn't part of TOP_X_THRESHOLDS, so patching only that one still leaves\n",
+    "#     the melqui plot asking for a top-200 that can't exist in a 30-model run.\n",
+    "text = ANALYSIS_SCRIPT.read_text()\n",
+    "patched = re.sub(\n",
+    "    r\"TOP_X_THRESHOLDS\\s*=\\s*\\([^)]*\\)\",\n",
+    "    \"TOP_X_THRESHOLDS = (1, 5, 10, 15, 20, 25, 30)\",\n",
+    "    text,\n",
+    ")\n",
+    "patched = re.sub(\n",
+    "    r\"MELQUIPLOT_NB_ENTRIES\\s*=\\s*\\d+\",\n",
+    "    \"MELQUIPLOT_NB_ENTRIES = 30\",\n",
+    "    patched,\n",
+    ")\n",
+    "if patched != text:\n",
+    "    ANALYSIS_SCRIPT.write_text(patched)\n",
+    "\n",
+    "cmd = [\n",
+    "    sys.executable,\n",
+    "    str(ANALYSIS_SCRIPT),\n",
+    "    str(RESULTS_DIR),\n",
+    "    \"--type\", \"peptide\",\n",
+    "    \"--from-archive\",\n",
+    "    \"--output_path\", str(ANALYSIS_DIR),\n",
+    "    \"--custom-labels\", str(LABELS_FILE),\n",
+    "    \"--per-scenario-plots\",\n",
+    "    \"--quiet\",  # silence the script's banner + \"NN.NN %\" progress prints\n",
+    "]\n",
+    "\n",
+    "result = subprocess.run(\n",
+    "    cmd,\n",
+    "    cwd=BENCHMARK_DIR,\n",
+    "    capture_output=True,\n",
+    "    text=True,\n",
+    ")\n",
+    "\n",
+    "if result.returncode != 0:\n",
+    "    print(result.stdout)\n",
+    "    print(result.stderr)\n",
+    "    result.check_returncode()\n",
+    "elif result.stdout.strip():\n",
+    "    print(result.stdout)\n",
+    "\n",
+    "figures = sorted(ANALYSIS_DIR.glob(\"*.png\")) if ANALYSIS_DIR.exists() else []\n",
+    "\n",
+    "if not figures:\n",
+    "    print(f\"analysebenchmarkresults.py ran successfully but no .png figures were found in {ANALYSIS_DIR}.\")\n",
+    "else:\n",
+    "    for fig in figures:\n",
+    "        print(fig.name)\n",
+    "        display(Image(filename=str(fig)))\n",
+    "\n",
+    "json_summaries = sorted(ANALYSIS_DIR.glob(\"*_performances.json\")) if ANALYSIS_DIR.exists() else []\n",
+    "if json_summaries:\n",
+    "    print(\"\\nJSON performance summaries:\")\n",
+    "    for j in json_summaries:\n",
+    "        print(\" -\", j)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07f47aca",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Conclusions**\n",
+    "\n",
+    "This notebook provides a reproducible, small-scale HADDOCK3 protein–peptide benchmark\n",
+    "using a small test input and test scenario maintained in the official benchmarking\n",
+    "repository, following the same install → download-dataset → run → analyse flow as the\n",
+    "repository's own `setup.sh` / `run.sh` / `analyse.sh` scripts.\n",
+    "\n",
+    "It should **not** be used to draw conclusions about full protein–peptide docking\n",
+    "performance, because the rigid-body sampling is intentionally reduced to only 30 models\n",
+    "(vs. 3000 in the full `unbound_true_interface_act-pass` scenario).\n",
+    "\n",
+    "For production benchmarking, use the full protein–peptide scenarios from the\n",
+    "`haddocking/benchmarking` repository (`unbound_true_interface_act-pass`,\n",
+    "`unbound_abinitio-cm`, `unbound_ab_initio-ranair`, and their `-cltsel` variants) with\n",
+    "their full sampling settings, run via `./run.sh` and `./run-all.sh` from a local\n",
+    "checkout, ideally on a SLURM cluster.\n",
+    "\n",
+    "The protein-peptide benchmark dataset and docking approach are described in:\n",
+    "Trellet M. et al. (2013). A unified conformational selection and induced fit approach to protein-peptide docking. PLOS ONE, 8(3), e58769. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0058769"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/versions.env b/versions.env
index 803e466..0c51569 100644
--- a/versions.env
+++ b/versions.env
@@ -6,10 +6,10 @@
 # > use a commit (must exist on haddocking/haddock3)
 # HADDOCK3_VERSION="e5b73f0b102c6c023f07d5ed4d7e8f65c1b2dccb"
 # > use pypi version (must exist on pypi)
-HADDOCK3_VERSION=2026.7.0
+HADDOCK3_VERSION=2026.8.0
 
 # which haddock-runner to be used - github release tag
-HADDOCK_RUNNER_TAG=v4.0.0
+HADDOCK_RUNNER_TAG=v4.1.0
 
 # which commits of the benchmarks repositories to use