Oncofusion

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Analysisstrong confidence 4 of 5: Replicated in peer-reviewed work by more than one group.Q14-phase2-readiness

Model: interface hotspot prediction - trainable in general, pure extrapolation here, because SKEMPI contains zero menin entries

also called hotspot predictor · ddG model · SKEMPI model scoping

There is a standard way to build a model that predicts which single amino acid changes will break a protein-protein grip. The training data exists and is large enough. We checked whether it contains any examples from our own proteins, and it contains none at all - so any prediction it made about menin would be a guess from unrelated examples.

The same thing, for a scientist

Of the three models this readiness review was asked to scope, this is the only one with enough labelled data to train at all - and even here the honest verdict is qualified. SKEMPI 2.0 contains 7,085 curated mutations across 345 distinct PDB complexes, with kinetics for 1,844 and enthalpy/entropy for 443. That is a real training set. But a direct computation over the released file this cycle found **zero entries** for menin, MEN1, KMT2A, MLL, LEDGF, AFF1 or any leukaemia complex - none of 4GQ6, 3U88, 7UJ4 or any related structure is present. A model trained on SKEMPI and applied to the menin-MBM1 or LEDGF-IBD interface is therefore extrapolating entirely out of domain. The defensible use is as a *prioritiser for a mutational scan we then run*, never as a substitute for it, and the entry is written so that distinction cannot be quietly dropped.

Go deeper
The task, stated precisely

Given a protein-protein complex structure and a point substitution at the interface, predict the change in binding free energy. For this project the operational question is narrower and easier: rank the residues of the menin MBM1 pocket and the LEDGF IBD by how much a substitution would cost binding, so that a saturation mutagenesis experiment tests forty positions in a sensible order rather than alphabetically.

Ranking is a much weaker claim than predicting a number, and it is the claim the data can support.

The training data that actually exists

SKEMPI 2.0 1: 7,085 mutations, 345 distinct PDB identifiers, 348 distinct complex-plus-chain entries, including 1,844 mutations with kinetics, 443 with enthalpy and entropy, and 440 that abolish detectable binding. The file is openly downloadable as a semicolon-delimited CSV.

What it does not contain, established by keyword scan of the protein-name and hold-out fields over the actual file: no menin, no MEN1, no KMT2A, no MLL, no LEDGF, no AFF1, no leukaemia complex. The only incidental histone-related entries are Survivin-Histone H3 (3UII) and LSD2-Histone H3.3 (4GU0) 1.

Baseline to beat, and the failure mode

Baseline: the published FoldX-class and machine-learning ddG predictors evaluated on SKEMPI's own held-out splits. Any new model must beat those on a complex-disjoint split - meaning no complex appears in both training and test - because random-split performance on SKEMPI is inflated by near-duplicate mutations within the same complex. A model that only beats baseline on a random split has learned the complexes, not the physics.

The failure mode that matters here: the menin pocket binds a short peptide with two aromatic anchors; SKEMPI is dominated by globular-globular interfaces such as antibody-antigen and protease-inhibitor. Peptide-in-groove interfaces are a minority class and are exactly the class this project needs. Performance on the minority class, not overall performance, is the number to report.

The honest verdict

Trainable: yes. Usable as evidence here: no. The correct deliverable is an ordered list of residues to test, accompanied by an explicit statement that no in-domain training example exists, and a plan to feed the experimental results back so that the menin and LEDGF interfaces eventually become the in-domain data that is currently missing.

That last point is the real opportunity. A single saturation mutagenesis experiment on the LEDGF IBD-KMT2A interface would produce more in-domain labelled ddG data for this project than exists in the entire public record, and would be the first such entries in a SKEMPI-style resource for a leukaemia fusion interface.

Facts and figures
taskPredict or, more defensibly, rank the binding-energy cost of point substitutions at the menin-MBM1 and LEDGF-IBD interfaces.
label sourceexperimental ddG of binding, manually curated from primary literature
in domain examples0
in domain scan resultzero hits for menin|men1|kmt2a|mll|ledgf|aff1|leukemia across protein-name and hold-out fields; none of 4GQ6, 3U88, 7UJ4, 2MSR present
baseline to beatpublished FoldX-class and ML ddG predictors on SKEMPI's own complex-disjoint splits; random-split comparisons are inadmissible
required reportingperformance on the peptide-in-groove subclass specifically, not overall SKEMPI performance
verdictTRAINABLE but OUT OF DOMAIN. Deliverable is a prioritised residue list for an experimental scan, explicitly labelled as extrapolation. Never a decision input.
highest value alternativeRun a saturation mutagenesis scan on the LEDGF IBD-KMT2A interface. It would generate more in-domain labelled binding data for this project than the entire public record contains, and it makes the model trainable for the first time.
statusplanned
computea ddG model on 7,085 examples trains on a single GPU in under an hour; Explorer Slurm or Modal, not a cluster allocation
training data
{
  "name": "SKEMPI 2.0",
  "n_mutations": 7085,
  "n_complexes": 345,
  "n_with_kinetics": 1844,
  "n_with_enthalpy_entropy": 443,
  "n_abolishing_binding": 440,
  "format": "semicolon-delimited CSV, open download",
  "url": "https://life.bsc.es/pid/skempi2/"
}
menin mbm1 kd conditions
{
  "_why": "Added by king sweep 2026-09-21 (Q82 corrections-owed #3). This entry quotes the menin-MBM1 53 nM figure. That number is NOT comparable to the KMT2A CXXC-DNA affinities without stating its buffer, and the omission is how a 1,300-fold 'discrepancy' was manufactured.",
  "value": "53 nM",
  "motif": "KMT2A MBM1, residues 4-15, FITC-labelled peptide at 15 nM",
  "method": "fluorescence polarisation",
  "buffer": "50 mM Tris, pH 7.5, 50 mM NaCl, 1 mM DTT",
  "nacl_mM": 50,
  "companion_itc": "72 nM by ITC, 50 mM phosphate pH 7.5, 50 mM NaCl, 1 mM beta-mercaptoethanol, 27 C",
  "source_pmid": "20961854",
  "source_quote": "\"in the FP buffer (50 mM Tris, pH 7.5, 50 mM NaCl, 1 mM DTT) used for binding experiments (Kd measurements)\" -- Grembecka 2010 JBC, Methods, fetched and read from PMC3003368 on 2026-09-21",
  "do_not_compare_naively": {
    "against": "KMT2A CXXC-DNA Kd 44 uM (PMID 20010842, Cierpicki 2010)",
    "because": "that measurement was made at 300 mM NaCl -- verbatim from its Methods, \"50 mM phosphate buffer (pH 7.1), 300 mM NaCl, 2 mM DTT at 25 C\" (PMC2908503, read 2026-09-21) -- SIX-FOLD more salt, on a salt-sensitive protein-DNA contact, against a hydrophobic peptide pocket that is not.",
    "best_matched_pair": "44 uM at 300 mM NaCl vs the 5.7 uM tagless CXXC measurement at 150 mM NaCl gives 108-fold against menin, not ~1,000-fold",
    "see": "ana-the-menin-affinity-comparison-was-made-at-one-sixth-the-salt"
  },
  "added": "2026-09-21"
}
cxxc dna kd conditions
{
  "_why": "Added by king sweep 2026-09-21 (cycle 46). This entry quotes the KMT2A CXXC-DNA 44 uM. That number is a property of five choices, not of the molecule, and quoting it bare is how this graph manufactured a 1,300-fold 'discrepancy' with the menin contact.",
  "value": "44 uM",
  "construct": "isolated CXXC domain, KMT2A 1147-1203, untagged, 15N-labelled",
  "ligand": "DNA pal oligonucleotide, one CpG element",
  "method": "NMR titration",
  "buffer": "50 mM phosphate, pH 7.1, 300 mM NaCl, 2 mM DTT, 25 C",
  "nacl_mM": 300,
  "source_pmid": "20010842",
  "source_quote": "\"Determination of the dissociation constants by NMR for the CXXC domain (MLL 1147-1203) and mutants was performed by titration of 15N-labeled protein with concentrated solutions of DNA pal oligonucleotide. Experiments were carried out in 50 mM phosphate buffer (pH 7.1), 300 mM NaCl, 2 mM DTT at 25 C.\" -- Cierpicki 2010 NSMB, Methods, fetched and read from PMC2908503 on 2026-09-21. The result sentence reads \"The wild type protein yielded a Kd of 44 uM\".",
  "this_is_the_high_salt_end": "300 mM NaCl is roughly twice physiological ionic strength and SIX TIMES the 50 mM the menin-MBM1 53 nM was measured in. The same domain measured tagless at 150 mM NaCl gives 5.7 uM -- 7.7-fold tighter from salt alone.",
  "do_not_compare_naively": {
    "against": "menin-MBM1 53 nM (PMID 20961854), measured at 50 mM NaCl by FP",
    "best_matched_pair": "108-fold, not ~1,000-fold",
    "see": "ana-the-menin-affinity-comparison-was-made-at-one-sixth-the-salt"
  },
  "added": "2026-09-21"
}
How this could bite us back
  • A ddG model produces a number for every mutation whether or not it has any relevant training example, and the number looks identical in both cases. Presenting out-of-domain predictions in a dashboard alongside experimental Kd values would make them indistinguishable to a reader.
  • Optimising a menin ligand against predicted hotspots risks optimising against the hotspots of the KMT2A peptide rather than of the JUND peptide, and the selectivity problem lives in the difference between them - which no SKEMPI-trained model has ever seen.
Still open
  1. Does transfer from MHC-peptide binding data, where peptide-in-groove labels are abundant, improve out-of-domain performance on a peptide-binding pocket like menin's?
  2. How many positions would a saturation scan of the LEDGF IBD-KMT2A interface actually need to cover to be useful, and what is the cheapest format - deep mutational scanning with a display readout, or arrayed biophysics?
  3. Would in-domain data generated by this lab be accepted into SKEMPI, making the contribution permanent rather than internal?
Every source behind this entry (2)
  1. E1strong confidence 4 of 5: Replicated in peer-reviewed work by more than one group.paper

    SKEMPI 2.0 contains 7,085 curated mutations across 345 distinct PDB complexes, including kinetics for 1,844 mutations and enthalpy/entropy for 443, and a direct scan of the released file found zero entries for menin, MEN1, KMT2A, MLL, LEDGF, AFF1 or any leukaemia-associated complex.

    SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation · Jankauskaite J, Jimenez-Garcia B, Dapkunas J, Fernandez-Recio J, Moal IH · Bioinformatics 2019
    What this evidence cannot show

    The mutation and complex counts are from the publication and were confirmed by direct computation over the downloaded file this cycle (7,085 rows, 345 distinct PDB ids). The absence of menin entries is an absence from this benchmark; unpublished or differently-curated ddG data may exist elsewhere.

    Evidence that argues against this (1)
    • A model could be transferred from a related peptide-in-groove system, for example MHC-peptide binding, for which labelled data is abundant. Whether that transfer helps is untested and would itself need validating on a menin mutational scan - so it does not remove the out-of-domain problem, it relocates it. source 2019
  2. E2strong confidence 4 of 5: Replicated in peer-reviewed work by more than one group.paper

    The interfaces this model would be applied to are structurally characterised and hotspot-annotated in this graph already: the menin MBM1 pocket with KMT2A F9/P10/P13 and a 53 nM peptide Kd, and the LEDGF IBD with KMT2A F148 and F151 as the two aromatic anchors.

    What this evidence cannot show

    Published hotspots were identified by targeted mutagenesis of a few residues, not by saturation scanning, so the true hotspot map of either interface is unknown - which is precisely why a prioritised experimental scan is the deliverable.

    Evidence that argues against this (1)
    • If the interfaces are already structurally characterised and hotspot-annotated in this graph, the model's marginal value is the open question rather than an argument for it. A model that reproduces annotations already held adds confidence and no information. Cancer Research 2014
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