# Binding Site Analysis > Decide whether a protein has a pocket worth targeting, and where it is, before committing to a docking or design campaign. Use this skill to run fpocket cavity detection, rank cavities by druggability and volume, compare apo and holo conformations to spot induced fit, identify allosteric and cryptic cavities that only open in simulation, and convert a chosen cavity into the search box coordinates a docking run needs. Also trigger on fpocket, cavity detection, druggability score, alpha sphere, cryptic pocket, allosteric site, pocket volume, hotspot mapping, or undruggable target assessment. - Skill: `k-dense-ai/binding-site-analysis` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add k-dense-ai/binding-site-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/k-dense-ai/binding-site-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - License: MIT - Author: K-Dense AI (https://skillmd.com/u/k-dense-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/k-dense-ai/binding-site-analysis --- # Binding Site Analysis The step before docking. Every docking skill in this bundle assumes you already know where the ligand goes and that the site is worth the compute — this is where those two assumptions get checked. fpocket runs in seconds and can save a month of screening against a pocket that was never going to bind anything. **Tool:** [fpocket](https://github.com/Discngine/fpocket), MIT, `conda install -c conda-forge fpocket`. Alpha-sphere cavity detection by Voronoi tessellation. **Checked against:** fpocket 4.x output format. Read [references/fpocket-output.md](references/fpocket-output.md) before parsing a run, [references/druggability.md](references/druggability.md) before calling a site druggable or not, and [references/cryptic-and-allosteric.md](references/cryptic-and-allosteric.md) when the answer is "no pocket" — **that one is judgement, not syntax.** ## The three scripts | Script | Answers | |---|---| | `pocket_report.py` | Which cavities are there, and is any of them worth targeting? | | `pocket_box.py` | Where exactly does the docking box go? | | `site_compare.py` | Does a pocket appear only when something is bound? | ## Score and Druggability Score are different, and pocket1 is not the answer This is the thing to get right. fpocket reports two numbers per cavity and they measure different things. **Score** ranks cavities geometrically, and pocket *numbering follows it*. **Druggability Score** is a logistic model trained to separate sites with known drug-like ligands from sites without — it is the one that answers "worth a campaign". They disagree often: ```bash python skills/binding-site-analysis/scripts/pocket_report.py rank --out-dir receptor_out ``` ``` # fpocket ranks pocket 1 first by Score, but pocket 2 is the most druggable. pocket druggability score volume apolar_fraction verdict reason 2 0.871 0.31 720.5 0.7143 druggable resembles sites with known drug-like ligands 1 0.183 0.412 980.4 0.3172 poor does not resemble a small-molecule binding site ``` Pocket 1 is larger and scores higher. It is also 68% polar surface, which is a groove rather than a pocket. **Volume alone is misleading** — the apolar fraction is what distinguishes a site that will bind a small molecule, and this script derives it because fpocket does not. Thresholds applied: druggability ≥ 0.5 is druggable, 0.2–0.5 marginal; volume < 200 ų is too small whatever the score; apolar fraction < 0.35 flags a polar groove. The 0.5 cut is fpocket's own; the others are this skill's conventions, stated so you can argue with them. ## Strip the structure first fpocket contours around whatever is in the file. Waters and ligands left in place get reported as protein surface, and the cavity they occupy disappears: ```bash grep -v HOH input.pdb | grep -v HETATM > receptor.pdb && fpocket -f receptor.pdb ``` A structural metal or covalent prosthetic group should stay; a substrate analogue should go. ## Producing the box ```bash python skills/binding-site-analysis/scripts/pocket_box.py from-pocket --out-dir receptor_out \ --pocket 2 --format vina ``` ``` center_x = 12.0 center_y = 22.0 center_z = 33.0 size_x = 12.0 size_y = 12.0 size_z = 14.0 ``` That output pastes directly into an AutoDock Vina config. Two sizing rules are built in: **4 Å of padding per side**, so the ligand can translate and rotate rather than being pinned; and a warning past 27 000 ų, because Vina spreads a fixed exhaustiveness over the whole volume and a box twice as wide samples eight times as thinly. `from-ligand` centres on a crystallographic ligand instead, and **that is the better option whenever a holo structure exists** — a real bound pose beats a predicted cavity. It lists the candidate HETATM residues when the one you named is not present. ## Cryptic sites, or why the apo structure lied ```bash python skills/binding-site-analysis/scripts/site_compare.py match --apo apo_out --holo holo_out ``` Classifies each cavity as `cryptic` (in holo, absent in apo), `induced fit`, `stable`, `closes on binding`, or `apo only`. **Superpose the structures first** — matching is spatial, and unaligned inputs make every cavity look cryptic. The script says so when nothing matches. This is not a corner case. The KRAS G12C switch II pocket does not exist in unliganded KRAS; thirty years of "undruggable" rested on structures that could not show it. ## Four ways this misleads 1. **A low druggability score means "unlike sites we have drugged before"**, not "impossible". The training set predates degraders, covalent inhibitors, and most protein–protein interface drugs. 2. **You scored one conformation.** Apo structures under-report pockets systematically. 3. **Structure quality propagates.** Missing loops, uncertain rotamers above ~2.5 Å, and AlphaFold's tendency toward closed apo-like states all change the answer. Check with `uniprot-rcsb` first. 4. **A detector that cannot recover a known site should not be trusted on an unknown one.** If a holo structure exists, verify the top cavity contains the crystallographic ligand. ## When the answer is "no druggable pocket" That is a conclusion about conventional reversible small molecules, not about the target. In rough order of how often they work: covalent inhibition (how KRAS G12C fell), targeted degradation (`degraders` — a degrader needs a binding site, not an inhibitory pocket), molecular glues, cryptic sites found by mixed-solvent MD, biologics if the target is extracellular, and `oligonucleotides` to sidestep the protein entirely. ## Composing with the rest of the bundle - `uniprot-rcsb` → before: check resolution, missing residues, and whether a holo structure exists. - This skill → `autodock-vina`: `pocket_box.py --format vina` writes its config directly. - This skill → `diffdock` / `boltz`: which site to focus on before posing. - `molecular-dynamics` → alongside: mixed-solvent simulation to find cryptic pockets. - `chemical-space` → after: only worth a giga-scale library once the site is worth it. ## Reporting results honestly Give the druggability score, the volume, and the apolar fraction together — one number is not an assessment. Say which structure and which conformation was analysed, and whether waters and ligands were stripped. If a known ligand exists, say whether the detector recovered its site. Call a predicted cryptic pocket a hypothesis until a fragment soak or thermal shift confirms it.