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arxiv: 2605.26192 · v1 · pith:OQMLY3ZInew · submitted 2026-05-25 · 💻 cs.LG · cs.AI· q-bio.BM

Co-folding model guided by structural proteomics

Alon Shtrikman , Nitzan Simchi , Michal Ran Shchory , Sagie Brodsky , Eran Seger , Kirill Pevzner This is my paper

Pith reviewed 2026-06-29 22:28 UTC · model grok-4.3

classification 💻 cs.LG cs.AIq-bio.BM
keywords protein structure predictiondiffusion modelsXL-MSHDX-MSinduced proximitystructural proteomicsprotein complexes
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The pith

Mass spectrometry data steers diffusion models to more accurate protein complex structures

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Protein structure generative models routinely fail to capture the correct conformational states of protein complexes that matter for induced proximity modalities such as PROTACs and antibodies. AIMS-Fold bridges this gap by converting XL-MS spatial restraints and HDX-MS solvent accessibility profiles into differentiable physical potentials that actively steer the sampling trajectory of pretrained diffusion models at inference time. The framework shows that each data type improves accuracy on its own and that their combination produces further synergistic gains. On challenging induced proximity targets the guided model outperforms unguided state-of-the-art methods such as Boltz-2.

Core claim

AIMS-Fold is an inference-time guided-diffusion framework that converts XL-MS spatial restraints and HDX-MS solvent accessibility profiles into differentiable physical potentials derived from structural proteomics measurements; these potentials steer the generative sampling trajectory of pretrained diffusion models and yield higher accuracy on induced proximity targets than purely computational unguided models.

What carries the argument

AIMS-Fold guided-diffusion framework that turns XL-MS and HDX-MS measurements into differentiable physical potentials to steer the diffusion trajectory

If this is right

  • XL-MS restraints alone improve predictive accuracy on protein complexes
  • HDX-MS data alone improves predictive accuracy on protein complexes
  • Combining the two data types produces synergistic gains in accuracy
  • The guided approach outperforms unguided models on induced proximity targets relevant to drug design

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same steering approach could be tested with other sparse experimental restraints if differentiable potentials can be defined for them
  • Reliable complex structures from this method could directly support computational design of bifunctional molecules that induce proximity
  • The results suggest that inference-time guidance with experimental data is a general route to improve diffusion models on tasks where static sequence-to-structure prediction is insufficient

Load-bearing premise

Sparse heterogeneous XL-MS and HDX-MS measurements can be turned into differentiable physical potentials that guide the model toward correct conformations without introducing systematic bias or degrading the pretrained priors.

What would settle it

A benchmark of induced proximity complexes with available XL-MS and HDX-MS data on which AIMS-Fold fails to exceed the accuracy of Boltz-2 would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2605.26192 by Alon Shtrikman, Eran Seger, Kirill Pevzner, Michal Ran Shchory, Nitzan Simchi, Sagie Brodsky.

Figure 1
Figure 1. Figure 1: AIMS-Fold is an inference-time guided-diffusion framework that actively steers the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: PTPN2-PROTAC-CRBN co-folding. (a) AIMS-Fold predicted conformation partially [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PD-1-Nivolumab co-folding. The guided model covers the right interface area specified in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Post-hoc filtering is insufficient for the detection of the correct conformation. (A) For the [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conformational state of protein complexes, critical for protein design and induced proximity modalities such as antibodies and PROTACs. While structural proteomics techniques like Cross-Linking Mass Spectrometry (XL-MS) and Hydrogen-Deuterium Exchange (HDX-MS) offer valuable spatial and dynamic insights, integrating these sparse, heterogeneous measurements into these models remains an open challenge. Here, we bridge this gap by combining structural proteomics data with the rich biophysical priors learned by pretrained diffusion models. We introduce AIMS-Fold, an inference-time guided-diffusion framework that actively steers the generative sampling trajectory using differentiable physical potentials derived from XL-MS spatial restraints and HDX-MS solvent accessibility profiles. We demonstrate that these structural methods individually enhance predictive accuracy, and their integration yields synergistic improvement. Crucially, by leveraging these experimental restraints, AIMS-Fold achieves higher accuracy on challenging induced proximity targets than purely computational, unguided state-of-the-art models like Boltz-2. This establishes our framework as a powerful, integrative computational approach for the structure based drug design of induced proximity drugs. Evaluation code will be made publicly available upon publication.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces AIMS-Fold, an inference-time guided-diffusion framework that steers pretrained diffusion models for protein complex structure prediction by incorporating differentiable physical potentials derived from XL-MS spatial restraints and HDX-MS solvent accessibility profiles. The central claims are that the individual data types improve accuracy, their combination produces synergistic gains, and the resulting model outperforms unguided state-of-the-art methods such as Boltz-2 on challenging induced-proximity targets relevant to antibody and PROTAC design. Public release of evaluation code is promised.

Significance. If the quantitative results and potential formulations hold, the work could offer a practical route to hybrid experimental-computational modeling of conformationally dynamic complexes that current generative models handle poorly. The emphasis on inference-time guidance rather than retraining preserves the base model's learned priors while adding experimental constraints, and the promised code release aids reproducibility.

major comments (2)
  1. [Abstract] Abstract: The headline claim of synergistic improvement and superiority over Boltz-2 is asserted without any quantitative metrics, dataset sizes, validation protocols, or numerical comparisons. This absence prevents evaluation of whether the reported accuracy gains are statistically meaningful or merely nominal.
  2. [Abstract] Abstract (framework description): No functional form, scaling factors, weighting scheme, or conflict-resolution procedure is supplied for converting sparse XL-MS cross-link distances and HDX-MS accessibility profiles into differentiable potentials. Because the guidance mechanism is the sole link between the experimental inputs and the claimed accuracy gains, this omission is load-bearing for the central thesis.
minor comments (1)
  1. [Abstract] The abstract refers to 'challenging induced proximity targets' without naming the specific complexes or providing even a high-level description of the test set.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments regarding the abstract. We address each point below and will revise the abstract accordingly to improve clarity and support for the central claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim of synergistic improvement and superiority over Boltz-2 is asserted without any quantitative metrics, dataset sizes, validation protocols, or numerical comparisons. This absence prevents evaluation of whether the reported accuracy gains are statistically meaningful or merely nominal.

    Authors: We agree that the abstract would benefit from inclusion of key quantitative results to substantiate the claims of synergistic improvement and outperformance over Boltz-2. The full manuscript reports these details, including specific accuracy metrics on a dataset of induced-proximity targets, validation protocols, and statistical comparisons. We will revise the abstract to incorporate concise numerical highlights of the main results while preserving its length constraints. revision: yes

  2. Referee: [Abstract] Abstract (framework description): No functional form, scaling factors, weighting scheme, or conflict-resolution procedure is supplied for converting sparse XL-MS cross-link distances and HDX-MS accessibility profiles into differentiable potentials. Because the guidance mechanism is the sole link between the experimental inputs and the claimed accuracy gains, this omission is load-bearing for the central thesis.

    Authors: The functional forms of the differentiable potentials (harmonic restraints for XL-MS distances and accessibility-derived terms for HDX-MS), along with scaling, weighting, and multi-potential conflict handling during guidance, are fully specified in the Methods section. We acknowledge that a high-level description of this mechanism would strengthen the abstract. We will add a brief clause to the abstract summarizing the guidance approach. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper presents AIMS-Fold as an inference-time guided diffusion framework whose guidance potentials are derived directly from external experimental inputs (XL-MS spatial restraints and HDX-MS solvent accessibility profiles). No equations, fitted parameters, or self-citations are shown that would reduce the reported accuracy gains over Boltz-2 to quantities defined internally by the model itself. The central claim rests on the integration of independent structural proteomics measurements with pretrained model priors, which constitutes an externally grounded step rather than a self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond reliance on pretrained diffusion models and the convertibility of proteomics data into differentiable potentials.

axioms (1)
  • domain assumption Pretrained diffusion models encode useful biophysical priors for protein structures that can be steered by external potentials
    The framework depends on this to justify inference-time guidance without retraining.
invented entities (1)
  • AIMS-Fold guided-diffusion framework no independent evidence
    purpose: To integrate XL-MS and HDX-MS restraints into diffusion sampling
    New named method introduced in the abstract; no independent evidence provided.

pith-pipeline@v0.9.1-grok · 5765 in / 1327 out tokens · 30240 ms · 2026-06-29T22:28:32.436422+00:00 · methodology

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