RNA & protein pocket discovery · structural ranking
Structural analysis for RNA and protein small-molecule targets — with benchmarked limits, and where we won’t guess
From sequence or structure: predicted 3D structure, a conformational ensemble, a ranked pocket shortlist with cross-frame stability, and integrated evidence — family classification, precedent ligands, interaction fingerprints, functional annotations. Validated on real drug discovery targets: HCV IRES IIa recovered from the apo structure alone at strict top-1 (RNA); CDK2, KRAS G12C, and MDM2 rank-1 recovery verified against PDB ligand-contact ground truth (protein).
For RNA, H-type pseudoknots and G-quadruplexes are excluded, not guessed at. For protein, pockets in low-confidence structure regions (pLDDT < 0.7) are flagged as lower-confidence, not silently ranked.
// Get in touch
A structural biology partner, not a subscription
We validate on real benchmarks, publish our own limitations, and recover real drug sites from sequence alone — HCV IRES IIa at strict top-1 (RNA) and CDK2, KRAS G12C, and MDM2 at rank-1 against PDB ligand-contact ground truth (protein) are examples, not cherry picks.
Work with us as:
- →A dedicated inference server built for your program — RNA, protein, or both
- →Integration into an existing discovery pipeline
- →A single-target assessment to evaluate the pipeline on your own data
Engagements are scoped per project — no tiers, no subscription.
// What we built
Why ensemble ranking matters for RNA pocket discovery
The field has converged on the answer that standard cavity detection over-predicts on RNA — fpocket’s default parameters mistake the polar grooves of duplex RNA for binding pockets. The Weeks lab formalised this as an RNA-tuned wrapper, fpocketR (Veenbaas et al., PNAS 2025), and we use the same RNA-tuned parameters. Detection alone, however, is not where customer recovery happens.
On the same seven cleft-binder targets, the single-frame fpocketR-style detection (and ours — the two are empirically equivalent) leaves the rank-1 pocket at the experimental binding site in only 2 of 7 cases at near-recovery and 0 of 7 at strict. The v0.2 contribution is what we add on top: a five-frame ANM conformational ensemble, cross-frame pocket clustering, and a cluster ranker based on persistence × binding-residue stability. Both ranker features are RNA-applicable by construction.
Rank-1 recovery on 7 cleft-binder targets3 / 7 strict· 6 / 7 near — with our ensemble + geometric ranker0 / 7 strict· 2 / 7 near — with fpocketR-style single-frame detection alone
Locked benchmark, deterministic re-run. Per-target lift figures and the full comparison table (vanilla fpocket / fpocketR params / our params / ensemble + ranker) on the methodology page. strict@1 = rank-1 cluster overlaps the experimental binding site by ≥ 50% of residues; near@1 = ≥ 30%.
Read full methodology// Capabilities
What the platform actually does
One integrated workflow, end to end, for RNA or protein. Sequence in, ranked top-3 pocket shortlist out, with integrated evidence, full per-cluster metadata, and a customer-facing PDF report.
// How it works
From sequence to ranked shortlist
A single deterministic pipeline. Input a sequence (or a PDB upload); pick up a top-3 ranked shortlist of candidate pockets with full geometric metadata.
Predict and ensemble
AI structure prediction generates the 3D tertiary structure from sequence. A five-frame conformational ensemble is sampled around the prediction. MSA-driven prediction available where the pre-pilot screen indicates.
Detect and cluster
Cavities are detected on each frame and clustered across the ensemble at 4 Å. Persistent cavities — those that survive the conformational sampling — are kept; transient or single-frame artefacts are filtered out.
Rank and return
Persistent cavities are ranked by our RNA-applicable scoring function. You receive a top-3 shortlist with residue lists, geometric metadata, the ensemble PDB and a branded PDF report.
// Why work with RNAfold
Honest scope, transparent methodology
The differentiators that matter once the science is right.
Explicit scope, before you rely on it
Cleft-shaped binding pockets are in scope. Groove binders, surface interfaces, H-type pseudoknots and G-quadruplexes are flagged out-of-scope upfront by the tractability screen. We tell you whether the pipeline should help on your specific target — not after the fact.
See scope & screen →The finding that qualifies our own assumption
A 78-pair apo/holo survey: the binding site is the part of the molecule that moves most on ligand binding. That limits how much any apo-structure pocket prediction — including ours — can be trusted. Written up as a technical finding, not marketing.
Read the findings →Transparent methodology
Full pipeline, third-party attribution and licences, validation methodology — all on /methodology. Every run gets the same disclosure in the PDF report. We don’t hide what we integrate; we name it where it belongs.
Read methodology →Reproducible benchmark
Seven cleft-binder targets, locked methodology, deterministic re-runs. The numbers below are what the pipeline actually produces, including the one neither case we don’t recover. No survivorship; no cherry-picking.
See benchmark →// v0.2.0 benchmark
What the pipeline actually recovers
Seven cleft-binder targets with deposited co-crystal structures — six riboswitch families plus one group I intron. As-shipped configuration: single-sequence prediction by default; MSA mode where the pre-pilot screen indicates. Numbers are exact, deterministic, and reproducible.
2GDI
2GDI
TPP RF00059 · 78 nt
4GXY
4GXY
B12 RF00174 · 161 nt
2GIS
2GIS
SAM-I RF00162 · 94 nt
5C45
5C45
FMN RF00050 · 54 nt
3DIL
out-of-scope fold class
3DIL
Group I intron · 174 nt
2HOJ
2HOJ
TPP (thi-box) · 83 nt
4LVV
4LVV
THF RF01831 · 89 nt
Strict @1 = at least one cluster in the rank-1 position with ≥ 50 % binding-site residue overlap.Near @1 = ≥ 30 %. RMSD = backbone C3′ RMSD vs experimental chain. Top-cluster overlap is shown after the result.
3DIL (group I intron) is a known out-of-scope fold class for v0.2 — we surface it in the table rather than hide it. Reproducer + per-cell records described on the methodology page.
// protein validation
What the pipeline actually recovers on protein targets
Seven real drug discovery targets, verified against ligand-contact ground truth pulled directly from PDB structures — not tested against memory or reputation. Rank-1 recovery on four targets (CDK2 canonical ATP site; KRAS G12C switch-II/sotorasib pocket; MDM2 p53-binding interface; carbonic anhydrase II catalytic zinc site); top-3 recovery — but rank-2, not rank-1 — on two targets (FKBP12 rapamycin-binding pocket; ADRB2 orthosteric site). The correct pocket is present in every report; the ranker is a shortlisting aid, not a definitive top-1 predictor.
Report the top-3, never “the top.” That framing isn’t hedging — it’s the direct, load-bearing implication of the FKBP12 and ADRB2 results in the table below, where the correct pocket is real and present, just not ranked first.
CDK2
CDK2
Kinase (ATP site) · 298 aa
KRAS G12C
KRAS G12C
GTPase (switch-II pocket) · 189 aa
MDM2
MDM2
PPI (p53-binding interface) · 491 aa
Carbonic anhydrase II
Carbonic anhydrase II
Metalloenzyme (catalytic zinc site) · 260 aa
FKBP12
correct pocket at rank 2, not rank 1
FKBP12
Immunophilin (rapamycin pocket) · 108 aa
ADRB2
correct pocket at rank 2, not rank 1
ADRB2
GPCR (orthosteric site) · 413 aa
TEM-1 β-lactamase
lands on a real, independently-published allosteric site (Ambler 99–114) — not the specific Bowman-lab cryptic pocket this target was originally chosen to test against
TEM-1 β-lactamase
Enzyme (allosteric, ensemble-only) · 286 aa
Ground-truth overlap = pipeline pocket residues that match real ligand-contact residues (≤4.5–6.0 Å) from a deposited PDB co-crystal structure, computed directly against the structure file with the numbering crosswalk applied where the target’s literature convention (e.g. Ambler, Kabat) differs from pipeline-sequential numbering.
All seven targets use AlphaFold DB as the structure provider — the ESMFold fallback exists for targets without a precomputed AlphaFold DB entry but has not yet been exercised on a real target. Structure providers, pocket detectors, and confidence flags are stated per-target in every report, never assumed.
Six rules-based confidence flags (no learned calibration) and per-pocket evidence — PLIP interaction fingerprints, Foldseek structural neighbors, ClinVar + UniProt functional overlap — run on every target and are shown in full in the per-target reports on /demos. The RNA methodology write-up on /methodology predates the protein pipeline and does not yet cover it.
// Worked examples
See the pipeline output
Three live worked examples spanning the v0.2 outcome classes — strict@1 (2GDI, single-seq), strict@1 via the opt-in MSA path (4LVV) and near@1 with global RMSD honestly reported (5C45).
TPP riboswitch (2GDI)
Live worked example. The pipeline recovers the TPP binding-site cluster at rank 1 with 71% binding-site residue overlap. Top-3 with full per-cluster metadata + interactive 3D viewer.
THF riboswitch (4LVV)
Live worked example. The pre-pilot screen flags 4LVV’s diverse-tail homologs; MSA mode lifts rank-1 recovery from neither (19% overlap, single-seq) to strict (50% overlap, MSA).
FMN riboswitch (5C45)
Live worked example. Smallest target in the benchmark (54 nt). Backbone RMSD 10 Å but the rank-1 cluster picks up 40% of the FMN binding-site residues — near@1, not strict. The case for reporting both global and local quality metrics.