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arxiv: 2605.23960 · v1 · pith:BC4C3EHEnew · submitted 2026-05-12 · 🧬 q-bio.BM · cs.LG

Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition

Mingqing Wang , Zhiwei Nie , Athanasios V. Vasilakos , Yonghong He , Zhixiang Ren This is my paper

Pith reviewed 2026-06-30 22:18 UTC · model grok-4.3

classification 🧬 q-bio.BM cs.LG
keywords protein representation learningknowledge-guided decompositioninformation bottleneckstructure-function relationshipsprotein embeddingsbiophysical signalsdeep learning for biology
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The pith

ProtDiS decomposes pretrained protein embeddings into biologically grounded dimensions via knowledge guidance and the information bottleneck principle.

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

Proteins encode functions in their three-dimensional structures, but most deep learning representations keep the relevant signals mixed together. This paper introduces ProtDiS, which takes existing protein micro-environment embeddings and splits them into clearer, independent parts using biological knowledge as a guide. The split follows the information bottleneck principle to keep useful details while removing unnecessary entanglement. The resulting features improve results on twelve different prediction tasks, with the biggest lifts when the test structures differ from those seen in training. Protein- and residue-level checks show the method can separate proteins that share folds yet differ in function and can pick up subtle physical properties that matter for activity.

Core claim

ProtDiS decomposes pretrained protein micro-environment embeddings into biologically grounded and task-relevant dimensions, yielding structural features that are more specific, independent, and information-efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure-based splits.

What carries the argument

ProtDiS, the knowledge-guided decomposition framework that applies the information bottleneck principle to separate entangled signals in protein embeddings into independent dimensions.

Load-bearing premise

That the information bottleneck principle combined with knowledge guidance can reliably separate entangled signals in pretrained embeddings into independent biologically meaningful dimensions without discarding task-critical information or introducing artifacts.

What would settle it

Observing no improvement or outright worse performance on structure-based splits across the twelve downstream protein tasks after applying the decomposition would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.23960 by Athanasios V. Vasilakos, Mingqing Wang, Yonghong He, Zhiwei Nie, Zhixiang Ren.

Figure 1
Figure 1. Figure 1: Overview of the proposed ProtDiS framework. We disentangle entangled structural representations into eight semantically grounded knowledge spaces, each corresponding to a specific local structural or physicochemical property. tion with its corresponding knowledge signal while minimiz￾ing redundant information shared with other channels. To avoid lossy or degenerate factorization, ProtDiS maintains an addit… view at source ↗
Figure 2
Figure 2. Figure 2: Knowledge-specific representation analysis. Left: mutual information gain heatmap comparing each knowledge embedding against the original structural embedding (s-embedding) across different knowledge dimensions. Significant improvements are observed only when the embedding matches its corresponding knowledge channel. Right: distribution visualizations of four types of embeddings under secondary structure a… view at source ↗
Figure 3
Figure 3. Figure 3: Independence and completeness analysis of the knowl￾edge channels. (a) Distance correlation coefficient (DCC) based independence test. Pairwise correlations between different knowl￾edge embeddings are consistently low, indicating effective dis￾entanglement. (b) Progressive reconstruction from knowledge channels visualized as a radar plot. Reconstruction starts from the Ollivier–Ricci curvature (ORC) channe… view at source ↗
Figure 4
Figure 4. Figure 4: Knowledge-aware representations improve discrimination of structurally similar proteins. (a) AUC of EC-label consistency prediction using XGBoost, stratified by TM-score. Knowledge embeddings outperform structural embeddings at high TM-scores. (b) Cosine similarity versus TM-score for protein pairs, showing increased dispersion of knowledge embeddings under high structural similarity. (c) Example protein p… view at source ↗
Figure 5
Figure 5. Figure 5: Main architectures of ProtDiS. a, Pipeline for computing structural descriptors. Core structural features are obtained using DSSP, including secondary-structure assignments, solvent-accessible surface area, and hydrogen-bond energies. Additional microenvironmental properties are computed from atomic coordinates using Python/Biopython, yielding B-factors, weighted contact number, Kyte–Doolittle hydropathy, … view at source ↗
Figure 6
Figure 6. Figure 6: Mutual Information (MI) heatmap among the eight selected structural and physicochemical labels, demonstrating low off￾diagonal redundancy. Gene Ontology (GO) The Gene Ontology dataset compiles functional annotations from UniProt/Swiss-Prot and maps them to the GO hierarchy, which describes proteins from three complementary biological perspectives: molecular function (MF), biological process (BP), and cellu… view at source ↗
Figure 7
Figure 7. Figure 7: Probe-based importance analysis of the eight knowledge dimensions across twelve downstream tasks, revealing distinct biological dependencies. Beyond single-task analysis, cross-task consistency patterns ( [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Cross-task consistency patterns highlight shared and divergent biophysical drivers across functional groups. Neighborhood aware ESM3 Structural Encoder Task Backbone Esm3ST embeddings KB 1\2\3\k embeddings Expert 1 Expert 2 Expert k Gating Network Weight Knowledge Decomposition Network Gated Fusion Network Knowledge Bottleneck ProteinShake Protein-level • Enzyme Class • Gene Ontology • Protein Family • SCO… view at source ↗
Figure 9
Figure 9. Figure 9: Integration of disentangled knowledge features in downstream tasks. Task-relevant knowledge components are selected and fused before being fed into downstream predictors, enabling explicit incorporation of interpretable structural knowledge into diverse functional tasks. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: SHAP analysis for XGBoost classifiers across seven major UniProt enzyme classes identifies which knowledge dimensions drive class-level distinctions, revealing characteristic biophysical fingerprints. this insight, we compared the corresponding knowledge radar plots for the two site pairs ( [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Functional discrimination and interpretability of ProtDiS in protein–ligand binding-site analysis. a, Functional consistency analysis across 800,000 sampled residue pairs. For knowledge embeddings, similarity monotonically tracks the probability that two residues share the same functional role, approaching unity at high similarity; in contrast, structure-embedding similarity remains functionally agnostic,… view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of the Common Knowledge (CK) channel variance and intrinsic dimensionality between Highly Annotated and Poorly Annotated CATH folds. pathways maintain strong predictive performance for physical traits (r > 0.8), whereas the CK channel fails to predict these attributes (r < 0.15). Robustness to Structural Noise In addition to annotation sparsity, we investigated whether low-quality structures fo… view at source ↗
Figure 13
Figure 13. Figure 13: Linear probing performance (Pearson r) on Poorly Annotated folds, demonstrating that physical traits are correctly captured by SK pathways rather than the CK channel [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of CK channel variance shift when processing High-Quality versus Low-Quality protein structures. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Inter-channel Distance Correlation (DCC) comparison between the ablated model without the decorrelation penalty and the full ProtDiS model. 2. Necessity of Adversarial Removal (Ladv) The effectiveness of the adversarial removal mechanism was evaluated by training linear probes directly on the residual channel (Common Knowledge, CK). Without the adversarial loss Ladv, the CK channel acts as an unconstraine… view at source ↗
Figure 16
Figure 16. Figure 16: Predictive performance of linear probes trained on the residual (CK) channel with and without adversarial removal. 3. Role of the KL Bottleneck (LKL) The regularization effect of the KL divergence bottleneck was assessed using a non-linear MLP probe on the unseen test set. Removing the KL bottleneck LKL allows the model to overfit by memorizing high-frequency structural noise. The introduction of LKL acts… view at source ↗
Figure 17
Figure 17. Figure 17: Generalization performance on the unseen test set evaluated via non-linear MLP probes, highlighting the regularization effect of the KL bottleneck. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
read the original abstract

Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded and task-relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more specific, independent, and information-efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure-based splits. Protein- and residue-level analyses further show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical. These findings suggest that knowledge-guided decomposition provides a general and interpretable approach for structuring latent spaces in protein structural modeling. The source code and implementation details are publicly available at https://github.com/AI-HPC-Research-Team/ProtDiS.

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

0 major / 4 minor

Summary. The manuscript introduces ProtDiS, a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings via the information bottleneck principle to produce more specific, independent, and information-efficient structural features. It claims consistent performance gains across twelve downstream tasks (largest under structure-based splits), improved differentiation of similar-fold proteins with divergent functions, and capture of fine-grained biophysical signals, with code released publicly.

Significance. If the reported gains hold under the described splits and controls, the work offers a general, interpretable method for disentangling entangled pretrained embeddings in protein modeling. The emphasis on structure-based evaluation and residue-level analysis strengthens the case for biological grounding; public code supports reproducibility.

minor comments (4)
  1. The abstract states 'consistent improvements across twelve downstream tasks' without any numerical values, baselines, or error bars; move a concise quantitative summary (e.g., average Δ and range) into the abstract for immediate clarity.
  2. Section describing the knowledge-guidance term (likely §3.2 or Eq. (3)–(5)) should explicitly state how the auxiliary supervision is constructed and whether it is task-specific or held fixed across the twelve tasks.
  3. Figure 3 or the corresponding results table: clarify whether the structure-based split protocol (sequence identity <30 % or TM-score threshold) is applied uniformly to all twelve tasks or only to a subset.
  4. The information-bottleneck trade-off hyperparameter is listed as a free parameter in the axiom ledger; add a short sensitivity analysis (one paragraph or supplementary table) showing that performance remains above baselines across a reasonable range of the trade-off weight.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of ProtDiS, recognition of the gains under structure-based splits, and recommendation for minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and context describe ProtDiS as a knowledge-guided decomposition framework inspired by the information bottleneck principle, applied to pretrained embeddings for downstream tasks. No equations, self-citations, fitted parameters renamed as predictions, or uniqueness theorems are quoted that reduce the central claim to its inputs by construction. The method is presented as a standard application of existing principles with reported empirical gains under structure-based splits, and code is released, making the derivation self-contained against external benchmarks without load-bearing circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the premise that pretrained embeddings are sufficiently entangled to benefit from explicit decomposition and that the information bottleneck can be applied without loss of critical biophysical signals; no free parameters or invented entities are named in the abstract.

free parameters (1)
  • informativeness-compression trade-off
    The method explicitly balances informativeness and compression, implying at least one tunable hyperparameter whose value is not reported in the abstract.
axioms (1)
  • domain assumption Pretrained protein micro-environment embeddings contain entangled representations that can be decomposed into independent biologically grounded dimensions
    This premise is required for the decomposition step to be meaningful.

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Reference graph

Works this paper leans on

9 extracted references · 3 canonical work pages

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    Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G

    URL https://proceedings.mlr.press/ v267/adams25a.html. Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. Accurate prediction of pro- tein structures and interactions using a three-track neural network.Science, 373(6557):871–876, 2021. Barrio-Hernandez, I., Yeo, J., ...

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    OpenReview

    URL https://openreview.net/forum? id=to3qCB3tOh9. OpenReview. Zhao, S., Song, J., and Ermon, S. Infovae: Balancing learn- ing and inference in variational autoencoders.Proceed- ings of the AAAI Conference on Artificial Intelligence, 33(01):5885–5892, Jul. 2019. doi: 10.1609/aaai.v33i01. 33015885. URL https://ojs.aaai.org/index. php/AAAI/article/view/4538....

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  4. [4]

    This design deliberately avoids scale-mismatch issues that would arise from integrating macroscopic (e.g., global fold classes) or microscopic (e.g., quantum mechanical) labels

    Scale Consistency and Functional Relevance:The chosen dimensions are strictly constrained to a unified, residue-level scale, encompassing local geometric and physicochemical properties. This design deliberately avoids scale-mismatch issues that would arise from integrating macroscopic (e.g., global fold classes) or microscopic (e.g., quantum mechanical) l...

  5. [5]

    As illustrated in Figure 6, the off-diagonal MI values remain predominantly low (for instance, the MI between Secondary Structure and other labels is 0.012∼0.210 )

    Relative Independence:To ensure that the selected dimensions provide distinct supervisory signals, we evaluated their relative independence by computing the Mutual Information (MI) among all eight labels. As illustrated in Figure 6, the off-diagonal MI values remain predominantly low (for instance, the MI between Secondary Structure and other labels is 0....

  6. [6]

    common knowledge

    Computational Feasibility:Given the massive scale of the requisite pretraining datasets (encompassing structures from both the PDB and AlphaFoldDB), the empirical viability of our framework heavily relies on highly scalable supervision. The selected eight dimensions can be deterministically and rapidly computed using standardized algorithms (e.g., DSSP) w...

    2023

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    As shown in Figure 15, in the absence of Lred, the decoupled channels suffer from severe non-linear feature collapse

    Necessity of Cross-Channel Decorrelation ( Lred)We evaluated the impact of the decorrelation penalty using the inter-channel Distance Correlation (DCC). As shown in Figure 15, in the absence of Lred, the decoupled channels suffer from severe non-linear feature collapse. For instance, the DCC between Local Packing and Hydrophobicity surges to 0.4688. Conve...

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    Necessity of Adversarial Removal ( Ladv)The effectiveness of the adversarial removal mechanism was evaluated by training linear probes directly on the residual channel (Common Knowledge, CK). Without the adversarial lossLadv, the CK channel acts as an unconstrained reservoir, accurately predicting predefined attributes (e.g., ASA achieves 0.513 (R2), Seco...

  9. [9]

    Removing the KL bottleneck LKL allows the model to overfit by memorizing high-frequency structural noise

    Role of the KL Bottleneck ( LKL)The regularization effect of the KL divergence bottleneck was assessed using a non-linear MLP probe on the unseen test set. Removing the KL bottleneck LKL allows the model to overfit by memorizing high-frequency structural noise. The introduction of LKL acts as a crucial manifold regularizer, compelling the model to learn a...