AlphaFold Database Debiasing for Robust Inverse Folding
Authors: Cheng Tan
Zhenxiao Cao
Zhangyang Gao
Siyuan Li
Yufei Huang
Stan Z. Li
Venue: NeurIPS 2025 poster ()
Keywords: protein structure; protein design; AlphaFold
Primary area: machine_learning_for_sciences

Abstract: The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry—such as inverse folding—exposes a critical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geometric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idealized, PDB structures capture the intrinsic variability and physical realism essential for generalization in downstream tasks. To address this discrepancy, we introduce a Debiasing Structure AutoEncoder (DeSAE) that learns to reconstruct native-like conformations from intentionally corrupted backbone geometries. By training the model to recover plausible structural states, DeSAE implicitly captures a more robust and natural structural manifold. At inference, applying DeSAE to AFDB structures produces debiased structures that significantly improve inverse folding performance across multiple benchmarks. This work highlights the critical impact of subtle systematic biases in predicted structures and presents a principled framework for debiasing, significantly boosting the performance of structure-based learning tasks like inverse folding.

AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan Zhenxiao Cao²* Zhangyang Gao Siyuan Li Yufei Huang Stan Z. Li *Shanghai AI Laboratory "Hong Kong University of Science and Technology *AI Lab, Research Center for Industries the Future, Westlake University
Abstract
The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at wear-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry- -such as inverse folding exposes crit- ical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geo- metric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idcalized, PDB structures capture the intrinsic variability and physical realism essential for generalization in downstream tasks. To address this discrepancy, we introduce a Debiasing Structure AutoEncoder (DeSAE) that learns reconstruct native-like like conformations from intentionally corrupted backbone geometries. By training the model to recover plausible structural states, DeSAE implicitly captures
robust and natural structural manifold. At inference, applying DeSAE to AFDB structures produces debiased representations that sig- nificantly improve inverse folding performance across multiple benchmarks, and also enhance other struture-conditionn the critical impact of subtle systematic biases in predicted structures and presents a principled framework for debiasing, significantly boosting the performance of structure-based learning tasks like inverse folding.
modeling tasks. This work highlights
Introduction
The advent of highly accurate protein structure prediction, epitomized by AlphaFold2 1 2 3 has fundamentally reshaped the landscape of molecular biology. The resulting AlphaFold Protein Structure Database (AFDB) 4 6) provides unprecedented repository of structural information, covering vast swathes of the known proteome with near-exp cauue-eeerettalll resolution. This deluge of data promises catalyze breakthroughs in data-driven protein analysis and design, offering fertile ground for deep learning models to decipher the complex Yet, despite their accuracy, AlphaFold prrdiccce structures differ systematically from experimentally determined ones. These differences reflect the inductive biases of the predictive model fiself-bian which, although benign for folding, can impair downstream learning tasks.
relationship.
The challenge posed by this systematic bias becomes particularly salient applications demanding high structural fidelity, such inverse folding-tth prediction of amino acid sequences compatible with a given protein backbone. This task is exquisitely sensitive to the precise geometric and energetic details of the target structure. To empirically demonstrate this, investigated the performance of several representative inverse folding models when trained on different structural datasets and on a consistent, held-out set of experimentally determined PDB structures.
"Equal contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Specifically, models such as StructGNN 77 GraphTrans 77]. GVP 8 and PiFold [9] were trained independently; once using curated dataset of high-quality PDB structures, and again using a compa- rable dataset of high fidelity (pLDDT 70) AFDB structures. Despite the close structural agreement between the two datasets-as indicated by an average RMSD of approximately 0.2 0.2A(Figure) downstream performance on the inverse folding task diverged sharply. As shown in Figure [b), models trained on PDB data achieved recovery rates between 34.1 % and 43.76%, while those trained on AFDB structures performed markedly worse, with recovery rates ranging from 17.16% 27 27.83%. The most extreme degradation was observed for PiFold, which dropped from 43.76% to 17.16% when trained on AFDB data. Intriguingly, we observed consistent trend: models that performed better PDB data suffered more acutely when trained on AFDB data suggesting that stronger models are more prone to overfitting the subtle, non non-physical regularities present in AFDB data.
the
PDB
AFDB
41.26
34.11
33.45
-14.2
-6.13
Recovery rate
26.20
17.16
StrucGGNN GraphTrans
GVP
ProteinMPNN
PiFold
(a)
(b)
Figure I: (a) The RMSD distribution of paired structure data from AFDB and PDB, (b) Recovery rate of representative inverse folding methods trained on PDB and AFDB.
These findings provide direct evidence of a distributional shift between AlphaFold-predittee and experimentally observed structures. This performance gap reveals tangible cost of structural bias: although AFDB structures are highly confident from the model's perspective, they inhabit a displaced region of conformational space-more geometrically idealized, less variable, and ultimately less reflective of the structural diversity sampled in nature. As result, models trained exclusively on AFDB data fail generalize reliably to real-world proteins, underscoring the importance structural realism in downstream design tasks and highlighting the need for principled debiasing strategies.
To intuitively illustrate the divergence between predicted and experimentally determined protein structures, we analyze the distribution of fundamental structural features- as dihedral angles and inter-atomic distances across AFDB and PDB. While AFDB structures generally accu- rate at the fold level, they tend to occupy smoother, more regularized region of conformational space-reflecting the inductive biases of the AlphaFold. To bridge this gap and align AFDB structures more closely with experimentally validated conformations, we introduce the Debiasing Structure Autoencoder (DeSAE), framework trained to reconstruct plausible native conformations from deliberately corrupted backbone geometries. DeSAE learns the structural manifold of experimentally observed proteins, effectively debiasing synthetic structures and improving their downstream utility.
Our main contributions are as follows:
Systematic Characterization of Predictive Bias:
We provide, to our knowledge, the first comprehensive identification and quantification systematic statistical deviations in AFDB when compared against the ensemble of experimental PDB structures.
A Debiasing Framework via Manifold Learning: We introduce DeSAE, a principled framework that learns the manifold of experimentally plausible protein conformations. By training denoising objective, DeSAE learns to project AFDB structures onto more realistic structural space.
We rigorously demonstrate that training inverse folding models on DeS AE-debassed AFDB structures leads to consistent and statistically significant improvements in generalization performance across multiple standard benchmarks, validating the practical utility of our debiasing approach.
Enhanced Generalization for Inverse Folding:

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2 Related Work
2.1 AlphaFold Database and Its Applications
The development of AlphaFold2 [T and its subsequent expansion into the AFDB [4] represents a monumental leap in structural biology [TO [2] AFDB provides open access to millions of high- accuracy predicted protein structures, drastically expanding the known structural proteome far beyond what has been experimentally determined via methods like X-ray crystallography, NMR, or cryo-EM deposited in the PDB [13]. This unprecedented resource has rapidly become foundational for a plethora of applications. Researchers have leveraged AFDB for large-scale functional annotation [14] 15 [6]. understanding protein-protein interactions 17. identifying novel drug targets [18 19]. and
accelerating structural analysis of complex biological systems 20 21). Indeed, structure prediction
models like AlphaFold3 [3] have reportedly incorporated AiphaFold2-predittae structures into their training regimens, albeit often with mixed strategies incorporating experimental data.
2.2 Structure-based Inverse Folding
Inverse protein folding 22 23 24 - the task of predicting an amino acid sequence that will fold into a given dine-dimmnsional backbone structure- is a foundational problem in computational protein design 26 27 28 29 S0] Historically, this problem was approached using physics- physics-based based
energy functions B1, but recent advances in deep learning have enabled substantial improvements in both accuracy and scalability [32
33 34]. Early methods employed multilayer perceptrons (MLPs) to estimate the probability distribution over 20 amino acids for each residue, based on structural 41) Graph-based methods further extend this framework by modeling the protein as a k-nearest neighbor graph 44] 44). StruIGNN and GraphTrans 7 introduced
features [35] 36. 37
38
39
40
a graph encoder coupled with an autoregressive decoder. GVP (8) leveraged geometric vector perceptrons to jointly learn scalar and geometric vector features. GCA 45 employed global attention
mechanisms to capture long-range dependencies. ProteinSolver [4 have addressed partially known
sequences, while models like AlphaDesign 47 ProteinMPNN
48 and ESM-IF 49 have achieved
strong performance by training on large structural datasets. Several recent works [50
51 introduce
protein language models [52 53 54 55 or surface-based representations [56
57 to improve inverse
folding. Furthermore, many structure-based tasks [58, 59
60
61
62 such predicting ligand
binding sites (63
64
65
66
enzyme commission numbers 67
68,
69
70
71
72), protein-protein
interaction interfaces 73
74 75,, post-translational modification 76 77). also critically depend
on precise tertiary structure, often using representations learned by inverse folding models or similar geometric deep learning architectures. Therefore, the fidelity of the structural data used for training these models is paramount. The systematic biases we identify in AFDB could propagate and amplify in such highly sensitive protein structure- based applications, motivating development of a structural debiasing framework to improve robustness and accuracy of these downstream tasks.
Preliminaries
A protein can be represented as sequence of amino acids 5 = (31, $2..,88).. of length L, where 8; E A and A denotes the standard amino acid alphabet. The corresponding 3D structure of the protein is defined by the Cartesian coordinates of its backbone atoms ttpicallp including the nitrogen (N), alpha carbon (Ca), and carbon (C) atoms for each residue. We denote the backbone conformation L:a E BJ, B, {N, Ca, CJ
as X
{ E 3 1 1
Protein Structure Prediction refers the task of inferring the 3D structure X from the amino acid sequence S. This is typically formulated as learning a function / 5 - X=, where the model predicts the atomic coordinates that define the protein's 's conformation. AlphaFold2 (]] as notable example, approximates it with remarkable accuracy, making it a cornerstone of structural biology.
Inverse Folding, also known structure-based sequence design, is the complementary problem. Given a target backbone conformation X, the goal is to recover a sequence S that is likely to fold into X. This task can be expressed probabilistically modeling the conditional distribution p(S) X). or deterministically as learning a function g X + S".
As illustrated in Figure 2, inverse folding relies on structure-to-sequeet tequuenceee reasoning, in contrast to the sequence-to-streee to-structure prediction models like AlphaFold2. Crucially, this directional shift makes inverse folding models vulnerable to distributional artifacts in the structural data they are trained on.
3

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Protein Sequence
Protein Structure
Structure Prediction
ESMADEAATRRWWSEIPVLLTTWAAA RDRELWWORLKKEECSSSIRRVWY
WAADNDWFRLEESNEETTRRVRKKC
WYIHDDLIVVEEDIEFIIIITTPTTAA
HIKPOLWARRV/KFGGLAALLALLO
Figure 2: Conceptual comparison between protein structure prediction (sequence-to-structurg and inverse folding PWLAVEEPOLIOKGGYHHWKKKKC
Inverse Folding
Why Inverse Folding Reveals Structural Bias? We adopt inverse folding as the primary lens through which to study bias predicted structures. Unlike structure prediction- where small geometric inaccuracies may be tolerable-inee inverse folding requires a high-fidelity representation of the structural manifold. Any systematic bias in the input structure distribution can distort the learned mapping from structure to sequence. Learning the conditional distribution p(S|X) requires the model to capture fine-grained and context-specific geometric feature.
The Structural Dehiasing Task The core challenge motivating this work stems from the observation that while models trained on predicted structures Xped (e.g., from AFDB) may perform well on similar held-out predicted data, their generalization to experimental data Xexp (e.g. from PDB) can be suboptimal. We define the structural debiasing task as learning a transformation T that debias X xreed into structure X pred = T(X pred). ). The primary objective is not merely to alter X pred, but to ensure that downstream models trained or evaluated using X pred exhibit improved generalization.
In the specific context of inverse folding, let F denote an inverse folding model trained on a dataset of structures. When F is trained debiased structures X' X'pred, its effectiveness is evaluated based on its ability to recover amino acid sequences for a held-out set of experimental structures Xexp. Let (-) be performance metric for the downstream task- e.g., the sequence recovery rate. The transformation T is considered beneficial it satisfies the following condition:
M(F(Xpred) M (F((Xred))
(1)
with the ideal objective being to approach:
M M(F(Xrra))) + M(((a))))))( ((F(ee)))
(2)
That is, the model trained on debiased predicted structures should generalize as well as, or nearly as well model trained high-quality experimental structures.
4 Uncovering Systematic Structural Bias in AFDB
4.1 Manifestation of Bias: Degraded Performance in Inverse Folding
To empirically evaluate the impact of training exclusively on predicted structures, we constructed rigorously curated dataset of paired PDB and AFDB entries (see Appendix [.. We adopt the validation and test splits from CATH 4.2 [78). removing any entries with high sequence similarity with our paired dataset. It important to note that only the training partition is altered. As previously shown in Figure [ although the predicted AFDB structures exhibit close agreement with experimental structures, the inverse folding performance of models trained on AFDB structures is markedly worse than those trained on PDB data. This phenomenon is further elucidated by examining the learning dynamies when trained and validated across PDB and AFDB data, as depicted in Figure 3 While models trained on either PDB or AFDB data exhibit superficially similar decreases in training loss (Figures 33 and 3D), a stark divergence emerges in their validation performance. Notably, models trained on AFDB data demonstrate a pronounced difficulty in generalizing, as evidenced by significantly higher or more erratic validation losses on held-out PDB structures (Figure compared to the robust generalization observed when training and validating on PDB data (Figure 3p).

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StruKGNNN
GraphTrans
ProNeiMMPNN
PiFold
14
1085
0
2.2
16
10-
10
(5) Training loss curve trained on PDB
(b) Validation loss curve trained on PDB
nw wwm
15
65"
60
(c) Training loss curve trained on AFDB (d) Validation loss curve trained on AFDB
Figure 3: Training and validation loss curves for inverse folding models on PDB and AFDB datasets.
4.2 Statistical Analysis of Key Structure Features
We performed comparative statistical analysis of fundamental structural features. We focused dihedral angles o, tb, and bond lengths (C-Ca and N-Ca) in the main text, as these parameters critically define local protein conformation. More detailed analysis provided in Appendix
(a) Ramachandran plot (PDB)
(b) Ramachandian plot (AFDB)
(c) Mixed Ramachandran plot
10606
10500
2000
300
(4) distribution comparison
(e) distribution comparison
(f) distribution comparisoo
270
130
B.
C-Ca distance Comparison
N-Ca distance Comparison
C-N distance Comparison
Figure 4: Statistical comparison of key structural features between paired PDB and AFDB data.
5

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The Ramachandran plot for PDB (Figure 4)) displays well-defined clusters corresponding to canonical secondary structures (a-helices, 3-sheets), but also significant dispersion of points into less popu- lated and even classically "disallowed" regions. In stark contrast, AFDB structures (Figure Fp) shows considerably tighter, more concentrated clusters within the allowed regions, with a notably sparser population in the disallowed areas. The mixed plot (Figure 4+) vividly demonstrates this: AFDB conformations predominantly occupy the core the allowed regions, while PDB conformations form broader envelope and populate peripheral regions more extensively.
Direct comparison of the Φ v and dihedral angle distributions further substantiates these observa- tions. For both ø and & angles, the AFDB distributions exhibit sharper, higher peaks and narrower spreads compared the PDB distributions. The w angle distribution, predominantly centered around radians, also shows more constrained, needle-like peak for AFDB compared to the slightly broader peak observed for PDB, which can accommodate subtle deviations and cis- cis-peptide bonds. Analysis of backbone bond lengths reinforces this trend of reduced variability in AFDB. The PDB bond length distributions exhibit broader tails, indicative of greater range of bond distortions reflecting real physical variations experimental factors.
5 Method
The observed "idealization" or regularization bias within AFDB data leads to narrower distribution of local geometric parameters compared to experimental data. This discrepancy can hinder the development of robust deep learning models for tasks that critically depend precise atomic details. To address this issue, we propose simple yet effective Debiasing Structure Autoencoder (DeSAE), illustrated in Figure 5 DeSAE is designed to reconstruct native-like conformations from biased or corrupted structural inputs, thereby learning generalizable structural manifold.
Structure Corruption Strategy We introduce localized perturbations to the backbone coordinates. Specifically, we randomly select a subset of residues and choose one of their backbone atoms. The coordinates of the chosen atom a" is replaced by the centroid the remaining three atoms:
2/aa- 1/3
Ti.a:
dEBA((a)
This strategy forces the model to learn local geometric integrity based on contextual information from the rest of the structure, using the uncorrupted PDB structure as the ground truth for reconstruction.
DeSAE Architecture In DeSAE, each residue i is associated with local frame T;(R; [R; 4;), where R. E SO(3) is rotation matrix and R is translation vector (typically centered Co). Node features are denoted h2 E R2 and edge features between node and are hij E RR In the SE(3) encoder, we employ only the frame aggregation layers capture local geometric interactions. In contrast, the SE(3) decoder utilizes both b aggregation and frame update layers, allowing the model iteratively refine local frames and recover physically consistent backbone geometries.
Structure Corruption Stralogy
Frame Aggregation
Frame Updating
PDB
residue embedding
N E aaxo
SE(3) Encoder
SE(3) Decoder
X' gixta
Structural consistency
Figure 5: Overview of the DeSAE. The encoder utilizes Frame Aggregation layers build infor- mative residue and edge representations invariant to global pose. The decoder employs both Frame Aggregation/Updaal layers refine residue frames and reconstruct the debiased protein structure.

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Frame Aggregation This module updates node and edge embeddings by processing information in manner invariant to global rigid transformations. For pair of nodes (i. and their respective frames T; T at layer I, the update process can be summarized as:
(1+1)
hig ((+1) FrameAgg(h, !1) lb Tú, T;),
Ri
(4)
The aggregation involves several steps:
1. Projection to latent geometric space: Node and edge features projected into latent 3D space, yielding sets of "virtual atom" " coordinates associated with each node and edge:
(1) = (1 ()), Pij 2g(1)))
(5)
where Z+(+) RD mm is learnable function typically parameterized by MLP followed by reshaping, represents the number of virtual points.
2. Augmented edge embedding with relative frame geometry: To incorporate geometric con- text, the edge feature is augmented using the relative pose between local frames. The relative transformation is given by Tij = T Tj, and applied to the edge projection:
Pij (Tip 2ig ||=
(1) (1)
3. Inter-node geometric interaction: To capture the geometric relationship between the nodes themselves, their latent point clouds 2.") and z/" compared after aligning them within common reference frame, typically frame i. The points 2,) are transformed using the relative (I)
rotation R. R, before caleulating the geometric dot product with 24
Ki 22 (2) (R, )0
(I+1) is aggregated by integrating the augmented 4. Edge aggregating: The final edge embedding hij
(1) (t) geometric features (Pij 9ig and explicit relative frame information:
h (1+1) = MTPP(p;; dis, vec R.y); I|t, - fell);
(8)
5. Node aggregating: The node embedding is aggregated by weighted neighboring nodes:
n (++1 (L.
Σ
aightt
JEM,
where N₁ denotes the set of neighboring nodes, and øij is the attention weight.
Frame Updating The Frame Updating module, employed exclusively within the decoder, is responsible for refining the residue-specific local frames T:") = (R. ((0)) at layer to produce updated frames T.((+1) (R((+)) (((-1) for the subsequent layer.
1. Rotation updating: The updated rotation R. is predicted based on an attention-weighted aggregation of the relative orientations of neighboring frames:
(oo(R
Σ at (eo(RR)))
JEN.
(10)
R((+1) Quat2Rot vec (R)
where of; is the learnable attention weight, W, E Rx99 projects the vectorized matrix into the 4D space. Quat2Rot(-) maps a quaternion to its corresponding 3 x 3 rotation matrix, detailed in Appendix
2. Translation updating: The updated translation + (1+1) is determined by an weightion aggregation relative positional information derived from neighboring residues:
(+11
(1)
(11)
JEA:
where at, is the learnable attention weight. The coordinates are determined by 2; T,(0) h
7

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Structural consistency We employ the structure loss inspired by Chroma [79]. which aims to directly minimize the deviation between the predicted and reference backbone conformations. The detailed loss function is provided in the Appendix A.1
6 Experiments
We begin by curating paired dataset of experimentally determined structures from PDB and their corresponding AFDB data (see Appendix [B.. We first pretrain DeSAE with the proposed structure conuptiin strategy that corrupt randomly 10% residues. The trained DeSAE AFDB structures to produce a "Debiased AFDB" dataset. Due to its simple design, DeSAE has only 5.9M parameters and is capable of processing process about 20k AFDB structures in 3 minutes on a single NVIDIA A A100 GPU. To assess the impact of this debiasing, we evaluated these datasets downstream inverse folding task. Specifically, the inverse folding model was trained independently using three distinct datasets: (1) PDB, (2) AFDB, and (3) Debiased AFDB. Further specifics on model architectures, and experimental parameters are provided Appendix C
then applied to the
6.1
Does Dehiasing Work?
We train models on three datasets and evaluate them using the validation and test sets of CATH 4.2 778. A critical aspect of structural debiasing is the preservation of the overall protein fold. Figure D a) presents the RMSD distribution remain concentrated at low values. and even seems unchanged compared to Figure [a), indicating that the debiasing process effectively debias AFDB without introducing significant global distortions. More importantly, the Debiased AFDB enables substantial improvements in sequence recovery across five inverse folding models (Figure 6(b)).
43.76
AFBB
40
Deblloed ATDO
36,46
34:11
30
6,20
27:33
266
Recovery mie
23.5
20
10
0.0
0.2
0.4
StauckiNN
GraghTrans
GVP
ProtcinMPNN
PiFold
(8)
Figure 6: (a) The RMSD distribution of paired structure data from Debiased AFDB and PDB. (b) Recovery rate of inverse folding methods trained on three datasets and evaluated on CATH 4.2.
Training dynamics on the Debiased AFDB more favorable than the original AFDB. As depicted in Figure 7 the validation loss exhibits a decay thrend over training epochs. While remains less stable than training on PDB, it is significantly more stable and lower in magnitude than that observed for AFDB-trained models (see Figure 3}. These results collectively affirm that our DSAE not only preserves structural accuracy but also enhances the learnability of inverse folding models.
StruetGNN
GraphTrans
GVP
ProteinMPNN
PiFold
(b) Validation loss curve trained on PDB Figure 7: Training and validation loss curves for inverse folding models on Debiased AFDB.
(a) Training Joss curve trained on PDB
8

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6.2 Does Debiasing Generalizable?
To assess the generalization capability of our Debiased AFDB, we use the trained model further evaluate on TS50 and TS500 test sets, which represent diverse and difficult inverse folding challenges. As shown in Figure 8 the performance advantages of Debiased AFDB persist on both TS50 and TS500 benchmarks. Similarly, results on the CATH 4.3 test set reproted Table reinforce this trend. Across all five inverse folding models training on Debiased AFDB leads to consistent improvements in sequence recovery compared to training on original AFDB structures. These results strongly indicate that our debiasing methodology effectively enhances the structural realism of AFDB data, enabling models to learn more generalizable sequence-structuue relationships.
50
PD3
40.66
37.36
37.39
43.44
42.18
40
35.55
32,46
31.13
35,
34,29
335.10g
16.3
4:29
30
24,30
20.80
$7.00
29,78
19
Recovery
20
20
10
10
StrucGGNN
GraphTrans
StmeIGNN
GraphTarn
GVP
ProteiaMPNN
PiFold
Figure 8: Recovery rate of inverse folding methods trained on PDB, AFDB, and Debiased AFDB. (a) Test on TS50 benchmark. (b) Test on TS500 benchmark.
6.3 Does Larger Scale Pretraining DeSAE Benefit Downstream Task?
The default DeSAE model is pretrained the PDB subset of our paired dataset, which comprises approximately 20,000 structures. To explore the benefits of larger-scale pretraining, we further pretrain DeSAE on the full PDB dataset and apply our debiasing pipeline to the AFDB data of the paired dataset. We refer to this extended dataset as Debiased AFDB-XL. As shown in Table [ Debiased AFDB-XL yields modest improvements over the Debiased AFDB. However, the overall gains are limited. A possible reason is that DeSAE primarily captures local geometric features, and thus may not fully benefit from additional global or large-scale scale structural diversity in pretraining.
Table 1: Recovery rate (%) of inverse folding methods trained on different data and test on the CATH 4.3 test set. The footnote colored in blue indicates the improvement of Debiased AFDB over the AFDB, colored in green indicates the improvement of Debiased AFDB-XL over the Debiased AFDB.
Method
PDB
AFDB
Debiased AFDB
Debiased AFDB-XI.
StructGNN
32.62
26.04
29. 29.99 (+3.95)
(43.5)
30.57 (+0.58)
GraphTrans
32.84
27.05
30.62
30.96, (0.96(+0.34)
GVP
34.81
24.35
31.15 (+6.80)
31.66 (+0.51)
ProteinMPNN
42.33
26.48
35.22
35.55 (10.33)
PiFold
43.74
17.74
35.38(+17.64)
35.76
More detailed experimental analysis and statistics are provided in Appendix D
7 Conclusion and Limitation
In this work, we identified and addressed critical challenge in leveraging the vast AFDB for training deep learning models sensitive to precise atomic details, particularly for the task of inverse folding. We demonstrated that systematic bias exists within AFDB structures. To mitigate this, we propose DeSAE to reconstruct native-like experimental structures from corrupted inputs. Our extensive experiments consistently showed that our debiasing pipeline effectively debiases AFDB without introducing significant global distortions. One limitation of our approach is focus on backbone geometries, potentially overlooking higher-order structural details such as sidechain orientations.

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Acknowledgement
This work was supported by 2025 Local Supporting Project (PI-PRU24DATAQ01) by Shanghai Artifi- cial Intelligence Laboratory, National Science and Technology Major Project (No. 2022ZD01151011 National Natural Science Foundation of China Project (No. 624B2115, No. U21A20427), Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of West- lake University. Project (No. WU2023C019) from the Westlake University Industries of the Future Research Funding.
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A Technical Details
A.1 Structure Consisteney Loss
Inspired by Chroma 79 we supervise our model with five complementary loss terms:
C gioobal + fragment + Lasr + Eneighhor + Ldistance
(12)
To define these terms, let X E RR be the ground-truth backbone coordinates of n residues and X E nxx their predictions. We first introduce the basic aligned RMSD loss:
Caligned (X, X) ||Align(X, X) - XX
(13)
where Align(X X, X) rigidly aligns the prediction to the target before measuring deviation. Thus, the five loss terms defined based the aligned RMSD loss as follows:
Global Loss (Cglobat): Apply Loligned to the full backbone of each residue, treating X as [n, 4, (four atoms per residue).
Fragment Loss (Lrggmentt): For earch residue, consider its sequential fragments of residues centered around each residue; X has shape [n,, c, 4, 3].
Pair Loss (Caair): For each of the K 30 nearest-neighbor residue pairs, measure alignment error over fragments on both residues, i.e. shape [n, K, c 2,
Neighbor Loss (Lneighbor): Compute RMSD over the four backbone atoms of the K nearest neighbors for each residue, with X shaped [n, K, 4, 3).
Finally, the Distance Loss (Edistance) enforces correct inter-residue distances using an MSE objec- tive:
Edistance [Dist( ( Dist(X)ll
(14)
where (X) E R=*xn is the matrix of pairwise C, CC. distances derived from the coordinates X.
We compute each of these five losses at every decoder layer and average them over the L layers. Empirically, this multi-scale supervision crucial to achieve good global structural fidelity.
A.2
Quat2Rot Function
Let (w,r, be the quaternion, then:
(w² + +x² y² -
2(xy wz)
2(xx + wy)
R(q)= = 2(xy + wz)
w² x² + y2² 22
2 - wx)
(15)
2(xz wy)
2(yz wx)
w² - x² - y² 22
Equivalently, each component of the rotation matrix is given by:
Roo = w² + -² 1² 2
Roi 2 2)
Roz 2 2 wy)
Ri = 2 2(x + 2).
Rn = w² - x² + y22 22
(16)
R12 = 2(y w I),
R2o 2(x
v),
R21 = 2 z + w w))
R22 = 2 - 2 y2 +
16

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B Dataset Details
This section details the curation of datasets used for training the DeSAE and for evaluating its efficacy in downstream tasks, particularly inverse folding. Rigorous procedures were implemented to ensure data quality and prevent information leakage between training and test sets.
B.1 DeSAE Training Dataset: Paired AFDB-PDB Structures
The DeSAE model is trained to learn a mapping from potentially biased predicted structures to conformations more representative of experimental observations. To facilitate this, constructed dataset paired protein structures, where each pair consists of:
1. An geghaaFol--prdicceee structure sourced from the AFDB 4m.
2. Its corresponding experimentally determined structure from the PDB
13
The curation process for this paired dataset involved several steps:
1. Initial Pairing: We identified all PDB entries that have a corresponding prediction available in the
AFDB based on UniProt accession numbers.
2. Quality and Consistency Filtering: To ensure meaningful structural correspondence and high- quality predictions, we applied the following filters:
The AlphaFold prediction must exhibit a mean predicted Local Distance Difference Test (pLDDT) score greater than 70. The choice of pLDDT score 70 is widely adopted convention in the field for filtering tigh-confidence predictions from AlphaFold [14] By setting the threshold at >70, we ensure that the AFDB structures used in our study are of high quality while maximizing dataset coverage across the proteome.
The sequence lengths the AFDB AFDB-predicted structure and the PDB experimental structure must be identical.
3. Residue-Level Matching: By enforcing identical sequence lengths and originating from the same protein, we ensure direct residue-to-residue mapping between the predicted and experimental structures within each pair.
This curation process yielded high-quality dataset of 19,392 AFDB-PDB paired structures.
B.2 Downstream Task Evaluation: Inverse Folding Datasets
To evaluate the impact of DeSAE-debiasing on inverse folding performance, we prepared three distinct structural datasets derived from curated pairs:
1. PDB Dataset: The experimental structures from the 19,392 PDB entries.
2. AFDB Dataset: The corresponding AlphaFold predictions from the AFDB.
3. Debiased AFDB Dataset: The AFDB structures after being processed by trained DeSAE model.
Inverse folding models are trained and evaluated separately on these three structural datasets to quantify the effect of debiasing.
B.3 Benchmark Test Sets and Data Leakage Prevention
To assess the generalization capabilities of inverse folding models trained on the aforementioned datasets, we utilized several established benchmark test sets: CATH 4.2 [78). TS50, TS500, and CATH 4.3 [78. To ensure that our benchmark evaluations are not compromised by data leakage from the DeSAE training set or the inverse folding training sets, we implemented strict sequence similarity filtering protocol. Using MMseqs2 88 8I] we removed any protein structure from these benchmark test sets if its sequence exhibited more than 90% sequence identity to any sequence present in our comprehensive 19.392-structure paired dataset used for DeSAB training. Following this filtering, our final benchmark test sets comprise: 893 structures from CATH 4.2, 38 from TS50, 382 from TS500, and 1575 from CATH 4.3. Performance on these carefully curated, non-overlapping test sets provides robust measure of model generalization.
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C Experiment Details
Our experimental methodology involves two-stage process, followed by downstream task evaluation, as illustrated in Figure ( First, DeSAE is trained using experimental PDB structures. Second, the trained DeSAE is employed to process and debias AFDB structures, Finally, inverse folding models trained the original PDB, original AFDB, and the DeSAB-debiased AFDB structures to evaluate the impact our debiasing approach.
Stage 1: Pretraining DeSAE on PDB. To initialize our debiasing autoencoder, trained DeSAE exclusively on the PDB portion of our paired dataset. Training spanned 60 epochs with an initial learning rate of 10-3, batch size of 16, and a CosineAnnealingL.. scheduler anneal the learning rate. The SE(3) encoder comprised eight equivariant layers, and the decoder comprised six layers, each with hidden dimensionality of 128. In each epoch, we randomly corrupted 10% of the residues in every structure to enforce robustness against atomic perturbations.
Stage 2: Generating a Debiased AFDB. After pretraining, we applied the learned DeSAE as preprocessing step to our AFDB structures. Specifically, we passed the AFDB entries through DeSAE to mitigate systematic biases in the predicted coordinates, producing a "Debiased AFDB" dataset. Together with the original AFDB and PDB sets, this yielded three distinct paired training datasets for downstream evaluation: (1) PDB, (2) AFDB, and (3) Debiased AFDB.
Stage 3: Inverse Folding Evaluation. To assess the impact of debiasing inverse folding performance, we trained separate inverse-folding models on each of the three datasets. Each model was trained for 50 epochs with learning rate of x 10-3 and batch size of 32. The performance of these three differently trained inverse folding models was then compared on the independent benchmark test sets detailed in Appendix B
Paired AFDB-PDB Structure
SE(3) Encoder
SE(3) Decoder
Three training datasets for the inverse folding task
Stage 1: Train DeSAE with PDB data.
PDB
AFDB
Stage Debias AFDB with trained DeSAE.
PDB
SE(3)
SE(3)
Encoder
Decoder
AFDB
Debiased AFDB
Figure 9: Overview of our experimental pipeline. Stage pretrains DeSAE on PDB data; Stage 2 applics DeSAE to AFDB to obtain Debiased AFDB; Stage 3 trains and evaluates inverse-folding models on PDB, AFDB, and Debiased AFDB datasets for comparison.
Baseline To rigorously evaluate our approach, we compare against five state-of-the-ate inverse- folding methods: StructGNN 77 GraphTrans (2). GVP [.. ProteinMPNN (48) and PiFold [9]. Each baseline is trained under identical conditions on three distinct datasets: (1) the original AFDB structures, (2) experimentally determined PDB structures, and (3) our debiased AFDB set.
As shown in the main text, models with higher expressive capacity-such as PiFold perform better on PDB dataset, but are dramatically underperformed on AFDB dataset. These models are indeed designed to capture subtle geometric patterns and fine-grained correlations within structural data. When trained on high-fidelity experimental structures from the PDB, these models benefit from the
18

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natural variability and physical realism inherent in the data, leading to strong generalization.
However, AFDB structures, while accurate at the fold level, exhibit systematic geometric regularities due AlphaFold's inductive local structural noise. High-capacity models can inadvertently latch onto these superficial statistical cues, treating them as meaningful signals. This leads distribution-speeffic overfitting: the model internalizes patterns that are predictive only within the synthetic AFDB manifold, rather than learning robust, physically grounded eequence-structure mappings. contrast, lower-capccity models may act as a natural form of regularization, failing to fully absorb these subtle biases and thus generalizing marginally better.
smoother dihedral angle distributions and reduced
Metric We primarily employed the sequence recovery rate that measures the residue-wise accuracy:
1
Recovery Rate
Σ ( T,
(17)
where Spred ($1, $2.....s$) is the output sequence of inverse folding methods. In addition to recovery rate, we also considered perplexity as a complementary metric. Perplexity is widely used in sequence modeling tasks:
Perplexity = exp
Σ
log P(s:1X))
(18)
D Additional Experiments
D.1 Evaluating Generalization to AlphaFold Structures
To further investigate the distinct characteristics learned by models trained on different structural data sources, conducted auxiliary experiment. In this scenario. evaluated the performance of inverse folding models trained on PDB, AFDB, and Debiased AFDB structures, but this time, the test set constructed using Aphaeold-predicted structures corresponding to the CATH4.2 benchmark (termed " "FFDB-vrrsoon CATH4.2 test set"). This setup allows us to assess how well models generalize to the specific structural distribution of AlphaFold predictions themselves.
The results, presented in Table 2 reveal several interesting trends. Unsurprisingly, models trained directly on raw AFDB data consistently achieve the highest recovery rates on this AFDB-version test set. This is expected, as the training and testing distributions are perfectly matched, and the models have effectively learned the idiosyncratic features and potential biases inherent in AlphaFold's predictions. Interestingly. models trained on Debiased AFDB structures achieve the second-best performance. While the debiasing process aims to shift AFDB structures towards the PDB distribution, these structures evidently retain sufficient AFDB-like characteristics to perform well when tested on AFDB predictions, outperforming models trained solely on PDB data for most architectures.
Notably, models trained exclusively on experimental PDB structures, despite having no exposure to AlphaFoldds specific predictive patterns or biases during training, still demonstrate commendable generalization to the AFDB-version test set. For instance, PiFold trained on PDB achieves a recovery rate of 44.31 %. This suggests that experimental structures encapsulate fundamental sequence- structure relationships that possess inherent generalizability, even to predicted structures that might deviate subtly from experimental reality. This observation reinforees the concept that PDB structures, while potentially "noisier," represent a more foundational and broadly applicable structural truth. The strong performance of AFDB-trainee models on AFDB test data further underscores the existence of a distinct "AFDB manifold" which models can readily lear,, but which may not perfectly align with the manifold of experimental structures.
D.2 Differential Impact of Large-Scale DeSAE Pretraining Recovery and Perplexity
Our previous analysis Table D indicated that pretraining DeSAE on an expanded PDB dataset provided Debiased AFDB-XL which yielded only marginal improvements in the sequence recovery
19

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Table 2: Recovery rate (%) of inverse folding methods trained different data and test on the AFDB- version CATH4.2 test set.
Method
PDB
AFDB
Debiased AFDB
StructGNN
34.21
37.83
36.16
GraphTrans
33.74
43,45
36,64
GVP
35.60
45.22
37.63
ProteinMPNN
41.87
76.83
45.34
PiFold
44.31
62.87
46.35
rate. To gain a more nuanced understanding of the effects of this larger-scale pretraining, we further evaluated the models using the perplexity metric, where lower values signify better performance by indicating higher confidence in the predicted amino acid probabilities.
The results, presented in Table 3, reveal more pronounced benefit of using Debiased AFDB-XL when assessed by perplexity. Across all inverse folding architectures tested on the CATH 4.3 set, models trained Debiased AFDB -XL consistently achieved lower (i.e., better) perplexity scores compared to those trained on the standard Debiased AFDB. This divergence suggests that while the additional structural information learned by DSAE-XL from the larger PDB corpus may not substantially alter the single best amino acid prediction at each position (thus leading to minor changes in recovery rate), it does refine the overall probability distribution possible amino acids. The model becomes more confident in its correct predictions and less confident in incorrect ones, leading to lower perplexity.
Table 3: Perpelxity metric of inverse folding methods trained on different data and test on the CATH
4.3 test
Method
PDB
AFDB
Debiased AFDB
Debiased AFDB-XI.
StructGNN
8.81
11.90
10.64
9.67
GraphTrans
8.75
14.09
10.55
9.56
GVP
8.07
16.59
10.14
9,25
ProteinMPNN
7.07
11.70
10.19
8.77
PiFold
6.42
13.83
10.39
8.43
D.3
Experimental sensitivity to the mask ratio
In the training stage of DeSAE, we specified the mask ratio to 0.1. However, we did not elaborate here on the rationale behind selecting this parameter or on the sensitivity of downstream tasks to To empirically validate the robustness of our approach, we conducted an ablation study to assess how performance changes with different corruption rates. We trained DeSAE using rates of 10%, 20%, and 30% and then evaluated the performance of downstream inverse folding models trained on the resulting debiased datasets.
Table 4: Performance under different mask ratios. Values in parentheses indicate change relative to mask=10%.
Model
Mask=10%
Mask=20%
Mask=30%
StructGNN
30.50
30.45 (-0.05)
32.06 (+1.56)
GraphTrans
31.29
32.53 (+1.24)
32.68 (+1.39)
GVP
31.44
34.27 (+2.83)
34.14 (+2.70)
ProteinMPNN
36.46
38.64 (+2.18)
38.55 (42.09)
PiFold
35.68
35.66 (-0.02)
38.41 (42.73)
As result shown in 4 the results clearly demonstrate that the performance is not critically sensitive to the 10% rate. For most models, performance is stable or even improves with higher corruption rates. This indicates that method is robust and that forcing the model to solve more challenging
20

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reconstruction task may lead it to learn an even more generalizable representation of the structural manifold. The 10% rate used in the paper is a conservative but effective choice, and the method's strong performance across a range of rates confirms its stability.
E Structure Features Analysis
Quantitive Analysis
Bond length To quantitatively substantiate our hypothesis regarding systematic differences between AFDB and PDB structural ensembles, we performed detailed statistical analysis of canonical backbone bond lengths: Ca-N, C-Ca, O-C, and N-C. The comparative statistics are summarized in Table 5 While the mean values for these bond lengths are highly comparable between AFDB and PDB structures, their variances differ substantially. Across all four analyzed bond types, PDB structures consistently exhibit significantly larger variances.
Table 5: Comparison of bond length statistics between AFDB and PDB structures.
Ca-N
C-Cor
O-C
N-C
AFDB
1.465413.68x 1 1.5323+4.11xx0 5 1.2305=1.40x
10 5
1.3351 10
PDB 1.4110=16.95x0 10-3 - 1.5248 10 - 1.2336=114.4xx00 10 1.3308=9.57x00
This pattern of significantly tighter bond length distributions in AFDB structures suggests higher degree of geometric regularity and uniformity compared to experimental structures. It implies that AFDB predictions may not fully capture the natural conformational fluctuations and slight deviations present in the PDB, potentially reflecting an "overrregularization" or a confinement to narrower, more idealized region of the conformational landscape. This quantitative finding lends strong support to our premise that AFDB structures possess distinct statistical properties that can contribute to the observed generalization gap in downstream tasks sensitive to such fine- ane-grained structural details.
Angle distribution To quantitatively assess the geometric differences between predicted and experimental structures, we analyzed the distributions key backbone angles within our paired dataset, comparing structures from AFDB against their corresponding PDB entries. We focused on the distributions of three backbone dihedral angles (ø, v., w) and three backbone bond angles (a, 3, 7) defined as follows for residue é:
Dihedral angles:
Φi, angle defined by atoms C C3-I-N--Ca,-C.
th angle defined by atoms N,-Cay-C,-NHHt
wi. angle defined by atoms
Bond angles:
ag, bond angle N.-Ca,.-C..
Br bond angle -N,---;-Coa;:
wi. bond angle Ca,-C,NN441
The distributions of these six angles were computed separately for the AFDB and PDB structure sets. To quantify the divergence between these distributions, employed several distance and similarity metrics: Hellback-Leiblee (KL) divergence, Wasserstein distance (Earth Mover's Distance), Euclidean distance, and Cosine similarity.
Table these comparison metrics. The results indicate noticeable differences between the AFDB and PDB angle distributions. For instance, the dihedral angles té and 6, which define the Ramachandran plot, exhibit relatively larger KL divergences and Euclidean distances, and lower cosine similarities, compared to the bond angles (o,, 3, 7). This suggests that AFDB predictions, while generally accurate, may possess subtly different conformational preferences or a more restricted sampling of these crucial dihedral spaces compared to the ensemble of experimental structures.
21

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Table 6: Comparison of different dihedral angle distributions using various distance metries.
KL. (x100)
Wasserstein (x (x10=*)
Euclidean Distance
Cosine Similarity
1,5663 Ø
5,0828
333,0086
0,9764
4/
3.6680
2.5720
871.7690
0,9349
3
2.6013
5.2714
355.6580
0.9894
0
0.0057
3,5109
86.5567
0,9950
B
0.0043
2.7487
75.5303
0,9964
7
0.0012
1.8125
40.1267
0,9991
E.2
Qualitative Analysis
01
(a) Ramachandrun plot (PDB)
(b) Ramachamdran plot (AFDB)
(6) Mixed Ramaccanndrm plot
20-02
20w
20100
(d) distribution comparison
y distribution comparison
distribution comparison
3007
2.90
1.3
(g) distribution comparison
y distribution comparison
distribution comparison
Figure 10: Global feature visualization of dihedral angles and bond angles.
22

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AFDB-C.C
1-00000
2W0
1896
5903
50000
140
144
1.52
1.5x
134
3.50
3:50
1.54
1.0
146
140
1.50
1.6
1.5
(a) C-Cor distance on AFDB
C-Ca distance PDB
C-Ca distance Comparison
AF30.2.
708-2.1
/ED0 + 983-6.1
133.00
20002
2000
11000
20600
18883
200300
135
1.30
3:30
3.34
136
1:8
Lo
150
130
330
1.6
134
140
124
L20
L3b
132
1.66
135
1x
1
(d) N-C distunce on AFDB
(c) N-C distance on PDB
(f) N-C distance Comparison
2560
30K
(g) N-Ca distance on AFDB
(h) N-Ca distance on PDB
(i) N-Ca distance Comparison
0500
30652
1003
500.0
135
3:24
Lit
1.54
136
170
13x 159 186
3:2 154
106
180
134
314
139 130 120
130
(i) O-C distance on AFDB
(k) O-C distance on PDB
(1) O-C distance Comparison
Figure 11: Bond length visualization.
23

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Visualization
To provide qualitative perspective the structural modifications introduced by DeSAF, visualized representative protein structures from PDB, their corresponding AFDB predictions, and the DSAE-processed Debiased AFDB versions. Figure 12 presents several such examples, with structures superimposed for comparison. At global level, visual inspection reveals that both the raw AFDB predictions and the Debiased AFDB structures maintain high fidelity the experimental PDB structures, exhibiting similar overall folds and tertiary arrangements.
These visualizations underscore key aspect of findings: the systematic biases in AFDB that impede inverse folding performance are often not readily apparent through casual visual inspection of global structure. The differences, while quantitatively significant for downstream deep learning models sensitive to local geometric details, can be quite subtle.
PDB
AFDB
Debiased AFDB
1ku
4htg
RMSD=0.303
RMSD=0.266
RMSD=0.277
RMSD=0.266
2b3k
2c02
RMSD 0.191
RMSD-0.1 0.177
RMSD-0.224
RMSD=0.199
2pwq
2yd4
DROR
RRER
RMSD=0.559
RMSD=0.503
RMSD=00962
RMSD=0.865
3jyp
Ixku
RMSD=0.439
RMSD=0.408
RMSD=0.275
RMSD=0.294
Figure 12: The visualization of samples in PDB, AFDB, and Debiased AFDB.
24

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31


Reviews:
[{"review_id": "hzN8ql845s", "reviewer": "Reviewer_nKri", "summary": "This paper identifies a systematic bias in AlphaFold-predicted structures (AFDB) compared to experimentally determined ones (PDB), and demonstrates that this bias impairs model generalization in structure-based tasks such as inverse folding. To address this, the authors propose DeSAE, a lightweight SE(3)-equivariant autoencoder trained on experimental structures, which learns to reduce these discrepancies. Experimental results show that training on DeSAE-debiased structures leads to consistent performance gains across multiple benchmarks. The study highlights both the risks of directly using AFDB data and the potential of learned debiasing to improve downstream modeling.", "questions": "While I find the problem addressed in this work both interesting and important, I remain hesitant to assign a higher score due to some limitations in the proposed solution and experimental scope. I would like to raise the following questions to help clarify the strength and applicability of the contribution:\n\n1) The paper frames its contribution as a debiasing method for inverse folding. However, the proposed method seems to offer more general improvements to structural representations. Could the authors clarify whether the observed benefits are specific to inverse folding, or whether similar gains might be expected in other downstream tasks, such as graph-level property prediction or node-level classification?\n\n2) Prior works like ProteinMPNN have employed random perturbations or noise-based augmentations on AFDB structures. Have the authors considered comparing DeSAE with these simpler augmentation baselines to better isolate the added value of learned debiasing?\n\n3) It is difficult to believe that a lightweight autoencoder can uniformly improve the structural quality of all AlphaFold-predicted structures. Have the authors explored which subsets of AFDB (e.g., grouped by pLDDT confidence, secondary structure type, or disorder region) benefit most from debiasing?\nProviding a more granular analysis of when and where DeSAE is most effective would help strengthen the practical value of this work.\nOf course, such universality is not expected, but a clearer characterization of the method’s scope would help better contextualize its contributions.", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1) The paper addresses an interesting and practically relevant issue: the systematic distributional bias in AlphaFold-predicted structures (AFDB) compared to experimental structures (PDB). This is increasingly important given the growing reliance on AFDB in structure-based learning pipelines.\n\n2) Through quantitative evaluation across multiple inverse folding models, the authors convincingly show that models trained on AFDB suffer significant generalization drops when evaluated on experimental data, clearly revealing the underlying distributional mismatch.\n\n3) The proposed DeSAE is a conceptually simple yet effective SE(3)-equivariant autoencoder trained on PDB structures. It is capable of adjusting AFDB data toward more realistic geometries, while remaining lightweight and efficient enough for large-scale application.\n\nWeaknesses:\n\n1) While the paper provides strong results on sequence recovery in inverse folding, it lacks broader evaluation metrics such as diversity or perplexity. It remains unclear whether the observed improvements reflect better convergence dynamics or genuine gains in generalization capacity.\n\n2) The paper does not analyze whether the performance gains result from broad, consistent improvements across the dataset or are driven by a small subset of easy cases. While Figure 6(a) offers a useful visualization of RMSD distributions before and after debiasing, it does not reveal how improvements in recovery rate are distributed with respect to structure quality or AFDB confidence levels.\n\n3) While not central to the technical contribution, some figures (e.g., loss curves and histograms) would benefit from clearer styling and labeling to improve readability.", "was_revised": true, "final_justification": "I initially gave a borderline score due to the narrow task scope and limited evaluation in the main paper. The rebuttal addressed these concerns to a reasonable extent, showing some generalization beyond inverse folding and clarifying existing analyses like perplexity. While the method and presentation are still somewhat basic, the problem is relevant and the results are solid enough to justify a higher score. I’ve updated my rating accordingly.", "rebuttal": "Dear Reviewer nKri,\n\nWe thank you for your thoughtful and constructive feedback. We are encouraged that they found the problem of AFDB bias to be interesting and practically relevant and our demonstration of its impact to be convincing. Your suggestions have helped us strengthen the paper, and we are pleased to provide clarifications for the concerns:\n\n**Q1** It lacks broader evaluation metrics like perplexity.\n\n**A1** We appreciate the reviewer’s point and agree that broader evaluation metrics provide a more nuanced understanding of generalization beyond sequence recovery alone. In fact, we included perplexity analysis in Appendix D.2 (Table 3, page 19), where we observed that models trained on Debiased AFDB consistently achieve lower perplexity than those trained on the original AFDB.\n\nFurthermore, while Table 1 shows that expanding the DeSAE pretraining set (Debiased AFDB-XL) results in only modest improvements in sequence recovery, it yields significantly better perplexity, suggesting improved confidence, even when accuracy changes marginally.\n\nPrior work (e.g., [1,2]) has shown that when recovery rates are in the sub-60% regime—as is typical for structure-conditioned models—recovery rate and perplexity are strongly correlated. Thus, we focused on recovery for simplicity and interpretability, while providing perplexity in the appendix to complement the analysis.\n\n[1] Hsu et al. Learning inverse folding from millions of predicted structures. ICML 2022.\n\n[2] Gao et al. KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement. ICLR, 2023.\n\n**Q2** While not central to the technical contribution, some figures would benefit from clearer styling and labeling to improve readability.\n\n**A2** We appreciate your kind suggestion. While we are unable to make changes to the manuscript during the rebuttal phase, we will change them in the revised manuscript.\n\n**Q3** Whether similar gains might be expected in other downstream tasks?\n\n**A3** Thank you for raising this insightful question about the generality of DeSAE's benefits. **We framed our contribution around inverse folding because this task is exquisitely sensitive to fine-grained local geometry, making it an ideal setting to reveal and evaluate the subtle structural biases present in AFDB.**\n\nTo explore whether these benefits extend to broader structure-based tasks, we conducted additional experiments on four widely studied protein function prediction tasks, following the protocol of CDConv [3], a strong and classical baseline that integrates both sequence and structure in a simple but effective model. \n\nThe tasks we examined are: Fold Classification (FC), Enzyme Reaction Classification (ER), Gene Ontology (GO) Term Prediction, and Enzyme Commission (EC) Number Prediction. For GO, we report the biological process subset for convenience. The results are as follows:\n\n(Note: Due to the lack of a provided pre-processing script in the original work, we conservatively removed a small number of misaligned entries, ensuring a fair and high-quality comparison.)\n\n||FC(%)|ER(%)|GO($F_{\\max}$)|EC($F_{\\max}$)|\n|-|-|-|-|-|\n|PDB|50.6|84.8|0.415|0.780|\n|AFDB|40.7|76.3|0.384|0.756|\n|Debiased AFDB|48.6|81.4|0.392|0.771|\n\nThe performance improvement is most dramatic for tasks that are highly dependent on precise structural information. Debiased AFDB obtains significant improvements on FC and ER, which are structure-dominant tasks. In contrast, for GO and EC, which are largely sequence-dominated tasks, the gains from debiasing are more modest. \n\nIn summary, these experiments provide encouraging evidence that DeSAE improves performance in structure-sensitive tasks beyond inverse folding, and that its utility is most pronounced in settings where accurate backbone geometry directly informs functional or structural classification.\n\n[3] Fan et al. Continuous-discrete convolution for geometry-sequence modeling in proteins. ICLR, 2023.\n\n**Q4** Have the authors considered comparing DeSAE with these simpler augmentation baselines to better isolate the added value of learned debiasing?\n\n**A4** We appreciate the reviewer's suggestion. It's important to emphasize that DeSAE and random noise-based augmentations serve fundamentally different purposes. Noise augmentation—as used in prior works like ProteinMPNN—is primarily a regularization technique. **It improves model robustness by exposing the model to slight perturbations but does not correct the systematic directional bias inherent in AFDB.**\n\nIn contrast, **DeSAE is a distributional correction mechanism trained to explicitly map biased AFDB geometries toward the structural manifold of experimentally determined PDB structures. It learns to reconstruct native-like conformations and thus aims to restore realism, not just encourage noise tolerance.**\n\nFollowing ProteinMPNN[4], we followed the augmentation protocol used in ProteinMPNN and added Gaussian noise (std = 0.02 Å) to backbone coordinates in AFDB structures.\n\n||AFDB w/o aug|AFDB w/ aug|Debiased AFDB|PDB|\n|-|-|-|-|-|\n|StructGNN|26.26|28.11|30.50|34.11| \n|GraphTrans|27.32|28.05|31.29|33.45|\n|GVP|23.57|22.78|31.44|34.90|\n|ProteinMPNN|26.44|31.93|36.46|41.26|\n|PiFold|17.16|31.62|35.68|43.76|\n\nThese results underscore the distinction: DeSAE is not merely a noise injector, but a model that learns a principled and structured denoising transformation aligned with experimental conformational space. We believe this is key to its stronger performance and generalization ability.\n\n[4] Dauparas et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science, 2022.\n\n**Q5** Have the authors explored which subsets of AFDB (e.g., grouped by pLDDT confidence, secondary structure type, or disorder region) benefit most from debiasing? Providing a more granular analysis of when and where DeSAE is most effective would help strengthen the practical value of this work. \n\n**A5** Thank you for this insightful suggestion. We agree that characterizing the conditions under which DeSAE offers the most benefit is important for understanding its practical utility. In response, we conducted two stratified evaluations: one based on structure quality and the other on secondary structure composition.\n\n**1. Structure Quality Stratification (by RMSD)**\n\nWe grouped the AFDB structures into three subsets based on the backbone RMSD between the original AFDB structure and its corresponding experimental PDB counterpart: (1) RMSD < 0.5 Å; (2) 0.5 ≤ RMSD ≤ 2.0 Å; (3) RMSD > 2.0 Å.\n\nWe then trained inverse folding models on each subset of debiased AFDB structures. Recovery rates on the test set are shown below:\n\n|Model| RMSD<0.5 | 0.5$\\leq$RMSD$\\leq$2.0 | RMSD>2.0 | Full  |\n|-|-|-|-|-|\n| StructGNN   | 24.31 | 27.70  | 28.02 | 30.50 |\n| GraphTrans  | 24.87 | 28.39  | 28.56 | 31.29 |\n| GVP         | 25.58 | 29.08  | 28.66 | 31.44 |\n| ProteinMPNN | 28.50 | 32.50  | 33.07 | 36.46 |\n| PiFold      | 29.17 | 30.28  | 32.58 | 35.68 |\n\nModels trained on the full debiased dataset consistently outperform their counterparts trained on the subsets. This is likely due to greater data diversity and size in the full set. Notably, the lowest RMSD subset (RMSD < 0.5 Å) performs the worst across all models—suggesting that highly idealized AFDB structures, even after debiasing, may not introduce enough structural variability to support generalizable learning. \n\n**2. Secondary Structure Stratification**\n\nWe further categorized proteins based on their predominant secondary structure, using DSSP annotations: (1) Alpha-helix dominant: >50% of residues are in α-helices; (2) Beta-sheet dominant: >50% of residues are in β-sheets; (3) Coil: the other structures.\n\nInverse folding models trained on each subset of debiased AFDB structures yielded the following results:\n\n|Model| alpha helix | beta sheet | coil  | Full  |\n|-|-|-|-|-|\n| StructGNN   | 27.67 | 25.69 | 27.49 | 30.50 |\n| GraphTrans  | 28.02 | 25.53 | 28.17 | 31.29 |\n| GVP         | 28.40 | 25.73 | 28.54 | 31.44 |\n| ProteinMPNN | 32.41 | 30.05 | 32.45 | 36.46 |\n| PiFold      | 30.29 | 27.29 | 30.27 | 35.68 |\n\nAgain, the full dataset yields the best performance. However, we observe that alpha-helical and coil-rich proteins benefit more consistently from debiasing compared to beta-sheet–dominant proteins [5]. This may be because helices and coils exhibit greater conformational flexibility in experimental structures and are more susceptible to over-idealization in AFDB predictions—making them more amenable to DeSAE’s correction. In contrast, beta-sheets are typically more geometrically constrained, and thus may already be well-modeled by AlphaFold, leaving less room for structural improvement.\n\nThese stratified evaluations confirm that DeSAE offers the most benefit when trained on a diverse, full dataset, but also suggest that its corrective effect is stronger in more flexible or initially over-idealized regions, such as coils and helices, or AFDB structures with moderate-to-high RMSD. \n\nWe truly appreciate the reviewer’s suggestion, as this analysis adds important nuance to the interpretation of our results. We will incorporate further granular stratifications in future work.\n\n[5] Galpern et al. Inferring protein folding mechanisms from natural sequence diversity. Biophysical Journal, 2025."}, {"review_id": "k6w3dyNV7M", "reviewer": "Reviewer_nyVZ", "summary": "This paper identifies a systematic geometric bias in the AlphaFold Protein Structure Database (AFDB) that degrades the performance of inverse folding models. To address this, the authors propose a Debiasing Structure AutoEncoder (DeSAE) to reconstruct more native-like conformations from these predicted structures. Experiments show that training on the resulting debiased dataset substantially and consistently improves sequence recovery rates across multiple inverse folding models and benchmarks.", "questions": "1. Does the paper provide any visualizations of the statistical distribution of the debiased AFDB dataset?\n2. What is the effect of this debiasing method on structure prediction and design tasks?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- This paper provides strong empirical evidence that a distributional shift exists between AlphaFold-predicted structures (AFDB) and experimentally determined structures (PDB). This is a practical and broadly relevant obstacle for the protein design community, which increasingly relies on millions of AFDB structures for design tasks.\n- DeSAE is a first end-to-end attempt at learning a geometric debiasing operator for AFDB, with a fast computational framework.\n- The authors evaluate their debiasing method on five representative inverse folding models on multiple standard benchmarks. This supports the central claim that the proposed debiasing method works.\n- This paper is generally well organised; figures effectively illustrate the bias and gains.\n\nWeaknesses\n\nMajor\n- The study lacks a comparison to trivial baselines. For instance, would simply injecting random noise to perturb AFDB structures achieve a similar debiasing effect?\n- The paper notes that PiFold's performance drops dramatically when trained on AFDB data and speculates that \"stronger models are more prone to overfitting\". This is a weak explanation that lacks a deep technical analysis.\n- The result for ProteinMPNN in Table 2 is counterintuitive. The table shows that when tested on the AFDB dataset, the model trained on raw AFDB performs worse (30.48% recovery) than the one trained on PDB (41.87% recovery). This is the opposite of the trend seen for four other models. This requires further explanation.\n\nMinor\n- In line 113, the description of backbone atoms is missing Oxygen (O).\n- Table 3 title: Perpelxity -> Perplexity\n- Line 249:  decay thrend -> decay trend", "was_revised": true, "final_justification": "Good paper.", "rebuttal": "Dear Reviewer nyVZ,\n\nWe sincerely thank you for your thorough and constructive feedback and for your positive evaluation of our work's significance, clarity, and originality. We are very encouraged by your assessment that the paper is technically solid with high impact. The reviewer's insightful comments have provided a clear path to significantly strengthen our manuscript.\n\nWe are particularly grateful for the suggestions to include a trivial baseline for comparison and to provide deeper analysis of specific results. We agree with all the points raised and will address them in the revised version of our paper as detailed below:\n\n**Q1** Would simply injecting random noise to perturb AFDB structures achieve a similar debiasing effect?\n\n**A1** We appreciate the reviewer's suggestion. It's important to distinguish between random perturbation and learned debiasing. Noise-based augmentation, as used in prior works like ProteinMPNN [1], is a regularization strategy that encourages model robustness by adding stochastic perturbations during training. However, it does not address the systematic, directional biases present in AlphaFold-generated structures—biases that result from the inductive priors of the predictive model and manifest as over-idealized geometries.\n\nDeSAE is designed as a learned structural correction mechanism. To empirically clarify this distinction, we added Gaussian noise (std = 0.02 Å) to backbone coordinates of AFDB structures (the baseline \"AFDB w/ aug\"), following the augmentation protocol in ProteinMPNN [1]:\n\n||AFDB w/o aug|AFDB w/ aug|Debiased AFDB|PDB|\n|-|-|-|-|-|\n|StructGNN|26.26|28.11|30.50|34.11| \n|GraphTrans|27.32|28.05|31.29|33.45|\n|GVP|23.57|22.78|31.44|34.90|\n|ProteinMPNN|26.44|31.93|36.46|41.26|\n|PiFold|17.16|31.62|35.68|43.76|\n\nThese results clearly show that while noise augmentation yields marginal improvements, DeSAE consistently outperforms both the unaugmented and augmented AFDB baselines across all models. This suggests that DeSAE learns a meaningful correction that enhances the alignment of AFDB structures with experimentally validated geometries, rather than merely increasing model robustness to local perturbations.\n\nThank you once again for your valuable feedback and for highlighting the broader implications of our approach.\n\n[1] Dauparas et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science, 2022.\n\n**Q2** PiFold's performance drops dramatically when trained on AFDB data and speculates that \"stronger models are more prone to overfitting\". This is a weak explanation that lacks a deep technical analysis.\n\n**A2** We thank the reviewer for encouraging a more rigorous explanation.\n\nModels with higher expressive capacity—such as PiFold—are indeed designed to capture subtle geometric patterns and fine-grained correlations within structural data. When trained on high-fidelity experimental structures from the PDB, these models benefit from the natural variability and physical realism inherent in the data, leading to strong generalization.\n\nHowever, AFDB structures, while accurate at the fold level, exhibit systematic geometric regularities due to AlphaFold’s inductive biases—such as smoother dihedral angle distributions and reduced local structural noise. High-capacity models can inadvertently latch onto these superficial statistical cues, treating them as meaningful signals. This leads to **distribution-specific overfitting: the model internalizes patterns that are predictive only within the synthetic AFDB manifold, rather than learning robust, physically grounded sequence-structure mappings**.\n\n**In contrast, lower-capacity models may act as a natural form of regularization, failing to fully absorb these subtle biases and thus generalizing marginally better.** This phenomenon is consistent with the sharp degradation in PiFold’s performance observed in Figure 1, which we now interpret as a manifestation of capacity-driven sensitivity to dataset bias.\n\nWe will incorporate this more detailed technical explanation in the revised manuscript.\n\n**Q3** The result for ProteinMPNN in Table 2 is counterintuitive. \n\n**A3** Thank you for your careful observation and for pointing out the counterintuitive result for ProteinMPNN in Table 2. Your intuition was entirely correct. The original value was indeed an error, which we have now rectified. We appreciate your keen eye, which has helped us improve the clarity and accuracy of our paper.\n\nWe double-checked our experimental logs and reran the experiment to ensure accuracy. The performance of the model trained on PDB (41.87% recovery) is reproducible and correct as reported. The correct recovery rate for ProteinMPNN trained on AFDB and evaluated on the AFDB-version CATH4.2 test set is 76.83%, not 30.48%. It is consistent with expectations—since the training and test sets are both drawn from AFDB distributions, the model is able to fit the idiosyncrasies of AlphaFold-predicted structures and perform well under distributional alignment.\n\nWe will correct this value in the final version of the table. We truly appreciate your attention to this detail—it helped us catch a misreported result that would otherwise have distorted the interpretation of this experiment.\n\n**Q4** Does the paper provide any visualizations of the statistical distribution of the debiased AFDB dataset?\n\n**A4** We are sorry for ignoring the visualization of the Debiased AFDB. Although we are unable to include figures during the rebuttal phase, we will incorporate comprehensive visual comparisons for the Debiased AFDB in the revised manuscript. \n\nOur figures show that the Debiased AFDB occupies a broader and more physically realistic structural distribution, with data points less tightly clustered than those of AFDB—reflecting a closer alignment with experimentally observed variability in PDB. We believe these additions will offer clearer insight into how DeSAE shifts predicted structures toward the natural conformational manifold.\n\n**Q5** What is the effect of this debiasing method on structure prediction and design tasks?\n\n**A5** Thank you for this insightful question—we greatly appreciate your suggestion. As stated in Section 3, we focus on inverse folding as our primary evaluation task because it is highly sensitive to atomic-level geometric fidelity. \n\nTo explore the broader applicability of our debiasing approach, we additionally evaluated its impact on protein function prediction tasks, following the protocol of CDConv [1], chosen for its simplicity and strong baseline performance. The tasks we examined are: Fold Classification (FC), Enzyme Reaction Classification (ER), Gene Ontology (GO) Term Prediction, and Enzyme Commission (EC) Number Prediction. For GO, we report the biological process subset for convenience. The results are as follows:\n\n(Note: Due to the lack of a provided pre-processing script in the original work, we conservatively removed a small number of misaligned entries, ensuring a fair and high-quality comparison.)\n\n||FC(%)|ER(%)|GO($F_{\\max}$)|EC($F_{\\max}$)|\n|-|-|-|-|-|\n|PDB|50.6|84.8|0.415|0.780|\n|AFDB|40.7|76.3|0.384|0.756|\n|Debiased AFDB|48.6|81.4|0.392|0.771|\n\nThese results suggest that Debiased AFDB structures consistently outperform raw AFDB structures, especially on tasks more sensitive to geometric features (e.g., FC, ER). While improvements on sequence-dominant tasks (GO, EC) are less pronounced.\n\nWe will expand our evaluation to include structure prediction and design tasks as the future work. We believe this would further clarify the role of structural debiasing across a broader spectrum.\n\n[1] Fan et al. Continuous-discrete convolution for geometry-sequence modeling in proteins. ICLR, 2023.\n\n**Q6** Minor typos.\n\n**A6** We appreciate your careful reading of the manuscript. We will thoroughly proofread the text and correct these typos in the revised version."}, {"review_id": "Ek5OsTsrdX", "reviewer": "Reviewer_YoZM", "summary": "The AlphaFold Protein Structure Database (AFDB) is a valuable large-scale synthetic dataset for training data-driven protein design models. However, a significant domain gap exists between synthetic and real data—particularly in the fine-grained distribution of side-chain atomic structures—leading to dataset bias in model training. To address this, this work proposes DeSAE (Debiasing Structure AutoEncoder), a novel approach to reduce bias in the AFDB dataset. DeSAE is first trained to reconstruct ground-truth structures from randomly corrupted Protein Data Bank (PDB) data. Once trained, it is applied directly to AFDB to generate a debiased version, AFDB(-XL). Comparative analysis reveals that training on different datasets can cause notable performance drops on real validation data. Extensive experiments demonstrate that the debiased AFDB significantly outperforms the original dataset in downstream tasks.", "questions": "Addressing the following points—previously raised in my “Weaknesses” section—would help me better assess the strength of your work and could lead to a higher score. My current evaluation is primarily limited by significant concerns regarding the experimental results.\n\n1. How about inverse folding model performance on settings (1) PDB + raw AFDB (2) PDB + debiased AFDB (3)PDB + debiased AFDB-XL ? There is no evidence in this paper showing that debiased-AFDB can better help with models trained with the real PDB dataset,  I think this limitation is so critical that prevents me from directly voting for accepting this paper though the paper quality is overall good.\n\n2. Could DeSAE’s corruption strategy inadvertently remove beneficial structural regularities? How sensitive is performance to the corruption rate (10%)?\n\nI would be happy to improve my rating if my main concerns can be well addressed.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "I found several strengths of this paper:\n\n1. **Clarity and motivation**: The paper is well-written, and the technical motivation is clearly presented. It addresses an important limitation of AFDB, with strong potential for real-world impact.\n2. **Strong empirical results**: Extensive experiments demonstrate that DeSAE effectively improves the performance of several inverse folding models by debiasing AFDB. The results are promising, and the method appears reproducible.\n\nHowever, there are two main weaknesses:\n\n1. The necessity of debiasing AFDB remains unclear without proper discussion of recent works like Protina and DPLM-2 that successfully combine AFDB and PDB data. A more thorough contextualization of how this work relates to and potentially benefits for existing mixed-data approaches would substantially strengthen the motivation for the proposed debiasing methodology.\n\n2. While Table 1 compares models trained exclusively on PDB versus debiased-AFDB data, this experimental design fails to reflect practical usage scenarios where AFDB data would typically augment rather than replace experimental structures. To properly validate the utility of debiasing, the authors should include hybrid training experiments comparing: (1) PDB + raw AFDB (2) PDB + debiased AFDB (3)PDB + debiased AFDB-XL. Such comparisons would more convincingly demonstrate whether debiasing actually improves model robustness when AFDB data is used as a supplement to limited PDB data - the more realistic application scenario. The current binary comparison cannot answer whether debiasing meaningfully enhances inverse folding performance in practical settings where both data sources are available.", "was_revised": true, "final_justification": "My initial major concerns have been adequately addressed in the rebuttal. Therefore, I recommend accepting this paper, as it presents a solid framework to debiasing synthetic datasets such as AFDB, which could significantly benefit the protein prediction and design community.", "rebuttal": "Dear Reviewer YoZM,\n\nWe sincerely thank you for your thorough and insightful feedback. We are encouraged that you found our paper well-written, clearly motivated, and supported by strong empirical results. We especially appreciate the constructive suggestions for improvement, and would like to response to the concerns below:\n\n**Q1** How about inverse folding model performance on settings (1) PDB + raw AFDB (2) PDB + debiased AFDB (3)PDB + debiased AFDB-XL?\n\n**A1** We thank the reviewer for pointing out the need to better contextualize our work with mixed-data approaches.\n\nWhile recent methods such as DPLM-2 and Proteina demonstrate that mixing PDB and AFDB data can improve performance, such combinations inherently mix structurally **\"realistic data\"** with **\"biased synthetic structures\"**. **Our proposed contribution is orthogonal: we aim to first reduce this domain gap by debiasing AFDB structures, thereby enabling more effective and consistent use of synthetic data alongside experimental structures.**\n\nTo empirically investigate this question, we conducted additional experiments where we combined 25% PDB and 75% AFDB-derived data (either raw or debiased) following the mix ratio in AlphaFold2 [1].\n\nThe results on the CATH 4.2 test set are summarized below:\n\n|             | PDB   | PDB + raw AFDB |  PDB + Debiased AFDB | PDB + Debiased AFDB-XL | AFDB  |\n|-------------|-------|----------------|----------------------|------------------------|-------|\n| StructGNN   | 34.11 | 31.06          | 31.24                | 31.83                  | 26.26 |\n| GraphTrans  | 33.45 | 31.68          | 31.86                | 32.22                  | 27.32 |\n| GVP         | 34.90 | 32.37          | 32.78                | 32.64                  | 23.57 |\n| ProteinMPNN | 41.26 | 36.40          | 37.95                | 38.82                  | 26.44 |\n| PiFold      | 43.76 | 36.82          | 37.61                | 38.67                  | 17.16 |\n\nIt can be seen that adding raw AFDB to PDB data generally hurts performance of inverse folding, especially for stronger models like PiFold and ProteinMPNN. Interestingly, the improvements from adding debiased AFDB are most significant for stronger models, like PiFold and ProteinMPNN, suggesting that these models are more sensitive to subtle structural inaccuracies and benefit more from structurally consistent training data. In contrast, weaker models like StructGNN and GraphTrans show smaller gains, likely due to their limited capacity to leverage fine-grained structural corrections.\n\nThese findings reinforce our central claim: debiasing enables predicted structures to act as a more reliable and effective complement to experimental data.\n\n[1] Jumper et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021.\n\n**Q2** Could DeSAE’s corruption strategy inadvertently remove beneficial structural regularities? How sensitive is performance to the corruption rate (10%)?\n\n**A2** Thank you for this excellent and insightful question which gives us a welcome opportunity to clarify the design principles of our method and demonstrate its robustness.\n\nOur corruption strategy is specifically designed to reinforce, not remove, beneficial structural regularities. The key lies in the objective of the denoising task. We introduce localized, non-physical perturbations by replacing a backbone atom's coordinates with the centroid of its neighbors. This breaks the local geometric integrity at specific sites. \n\nThe model's task is to reconstruct the original, experimentally determined PDB structure from this corrupted input. To do so, it must learn the underlying rules of plausible protein geometry from the surrounding, uncorrupted parts of the structure. In essence, the model learns to \"heal\" the structure by restoring it to a state consistent with the natural conformational manifold defined by the PDB. Therefore, the training process explicitly forces DeSAE to learn and internalize the very native-like regularities we aim to preserve, making it an effective method for projecting idealized AFDB structures onto a more realistic manifold.\n\nTo empirically validate the robustness of our approach, we conducted an ablation study to assess how performance changes with different corruption rates. We trained DeSAE using rates of 10%, 20%, and 30% and then evaluated the performance of downstream inverse folding models trained on the resulting debiased datasets. The results on the CATH 4.2 test set are presented below.\n\n| Model | mask=10% | mask=20% | mask=30% |\n|-------------|----------|----------|----------|\n| StructGNN   | 30.50 | 30.45 (-0.05) | 32.06 (+1.56)|\n| GraphTrans  | 31.29 | 32.53 (+1.24) | 32.68 (+1.39)|\n| GVP         | 31.44 | 34.27 (+2.83) | 34.14 (+2.70)|\n| ProteinMPNN | 36.46 | 38.64 (+2.18) | 38.55 (+2.09)|\n| PiFold      | 35.68 | 35.66 (-0.02) | 38.41 (+2.73)|\n\nThe results clearly demonstrate that the performance is not critically sensitive to the 10% rate. For most models, performance is stable or even improves with higher corruption rates. This indicates that our method is robust and that forcing the model to solve a more challenging reconstruction task may lead it to learn an even more generalizable representation of the structural manifold. \n\nThe 10% rate used in the paper is a conservative but effective choice, and the method's strong performance across a range of rates confirms its stability. We will include this study in the Appendix of our revised manuscript."}, {"review_id": "RII58Qsh7c", "reviewer": "Reviewer_nZFv", "summary": "Although AFDB provides a high-precision and large-scale protein structure database, it has certain limitations when training deep models that are sensitive to fine atomic geometry (such as inverse folding). By comparing the distribution of structural features, this study found that AFDB exhibits systematic geometric biases. Therefore, it attempts to use a Debiasing Structure AutoEncoder (DeSAE) to restore reasonable structural states in the data, and employs the debiased AFDB data to evaluate the performance of inverse folding tasks across multiple benchmarks, achieving improvements. Given that the deep utilization of high-quality data generated by large models is one of the current research hotspots, the ideas and tasks of this study are simple but inspiring.", "questions": "1.\tThe paper mentions \"using a comparable dataset of high fidelity (pLDDT > 70) AFDB structures.\" Why is pLDDT > 70 used rather than other metrics or other numerical thresholds?\n2.\tAlthough the paper provides comprehensive validation for inverse folding tasks and even includes generalization capability verification in Figure 8, it lacks broader generalization/universality validation for other tasks. How can it demonstrate that systematic geometric bias corresponds to fine-grained atomic geometry when only one task has been validated?\n3.\tThe baselines mentioned in the paper include StructGNN, GraphTrans, GVP, ProteinMPNN, and PiFold. Are there larger-scale performance comparisons available for inverse folding baselines?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "This study represents a simple yet inspiring research work. Its strengths lie in the transferability of this framework to a broader range of data generated by large models, though it may require model fine-tuning. The formulas and model descriptions are clear and easy to understand.\nHowever, the limitations include that the validation tasks primarily focus on inverse folding. After removing geometric biases, there should be a more comprehensive validation framework to assess generalization across diverse tasks, as relying on a single task may lack sufficient persuasive power.", "was_revised": true, "final_justification": "All of my concerns have been addressed, so I have decided to raise the score to 5.", "rebuttal": "Dear Reviewer nZFV,\n\nWe sincerely thank you for your positive and encouraging assessment of our work. We are delighted that you found our study simple but inspiring and recognized its potential for broader impact. Your feedback is valuable, and we appreciate the opportunity to clarify the scope of our validation and our methodological choices. We believe our responses below will address the questions raised and reinforce the significance of our findings:\n\n**Q1** The paper mentions \"using a comparable dataset of high fidelity (pLDDT > 70) AFDB structures.\" Why is pLDDT > 70 used rather than other metrics or other numerical thresholds?\n\n**A1** The choice of a pLDDT score > 70 is a widely adopted convention in the field for filtering high-confidence predictions from AlphaFold's group [1]. According to the official AlphaFold and numerous subsequent studies, structures with a pLDDT score between 70 and 90 are considered to have a good backbone prediction, while scores above 90 are considered high accuracy. By setting the threshold at >70, we ensure that **the AFDB structures used in our study are of high quality while maximizing dataset coverage across the proteome**.\n\nWe will add an explanation to clarify this convention and its justification in Appendix B.1 of the revised manuscript.\n\n[1] Tunyasuvunakool et al. Highly accurate protein structure prediction for the human proteome. Nature, 2021.\n\n**Q2** Although the paper provides comprehensive validation for inverse folding tasks and even includes generalization capability verification in Figure 8, it lacks broader generalization/universality validation for other tasks. How can it demonstrate that systematic geometric bias corresponds to fine-grained atomic geometry when only one task has been validated?\n\n**A2** Thank you for this important and insightful comment. While our paper focuses primarily on inverse folding as a representative task sensitive to local atomic geometry, we agree that broader validation across structurally grounded downstream tasks is essential to support claims about the general impact of geometric bias. While reviewers nKri and nyVZ also raised similar questions, we sincerely appreciate your professionalism and expertise.\n\nThe tasks we conducted are: Fold Classification (FC), Enzyme Reaction Classification (ER), Gene Ontology (GO) Term Prediction, and Enzyme Commission (EC) Number Prediction. For GO, we report the biological process subset for convenience. For consistency and fairness, we used the same backbone-only representations across datasets (PDB, AFDB, Debiased AFDB) and removed a small number of misaligned or corrupted entries, due to the absence of official preprocessing scripts from the original CDConv repository.\n\nThe results are summarized below:\n\n||FC(%)|ER(%)|GO($F_{\\max}$)|EC($F_{\\max}$)|\n|-|-|-|-|-|\n|PDB|50.6|84.8|0.415|0.780|\n|AFDB|40.7|76.3|0.384|0.756|\n|Debiased AFDB|48.6|81.4|0.392|0.771|\n\nAcross all tasks, models trained on Debiased AFDB outperform those trained on raw AFDB, confirming that DeSAE improves generalization even beyond inverse folding. Structure-sensitive tasks like Fold Classification and Enzyme Reaction Classification exhibit the most pronounced gains, reinforcing the notion that the learned debiasing targets systematic geometric distortions.\n\nTogether, these results indicate that DeSAE is not narrowly tuned for inverse folding, but rather enhances structural realism in a way that generalizes across diverse structure-based learning tasks.\n\nWe appreciate your suggestion and fully agree that expanding the range of validated tasks is valuable. As part of future work, we plan to evaluate the impact of debiased structures on additional tasks to further establish the universality and limits of our framework.\n\n[2] Fan et al. Continuous-discrete convolution for geometry-sequence modeling in proteins. ICLR, 2023.\n\n**Q3** Are there larger-scale performance comparisons available for inverse folding baselines?\n\n**A3** We thank the reviewer for this excellent and highly relevant question. Our initial selection of five baselines was intended to provide a diverse and representative sample of established inverse folding models, spanning different architectural paradigms.\n\nOur primary goal was not to establish a new state-of-the-art in inverse folding, but rather to demonstrate that **the performance degradation on raw AFDB data and the subsequent improvement from our debiasing method are consistent and model-agnostic phenomena.**\n\nWe acknowledge the value of comparing against larger-scale and more recent models, particularly those leveraging pretrained protein language models (PLMs), which have become increasingly prominent in inverse folding. To this end, we evaluated our debiasing pipeline using KW-Design [3], a recent state-of-the-art method that **incorporates a frozen ESM-2 (650M) language model along with a frozen pretrained PiFold model**. While KW-Design's reliance on pretrained components makes it partially insulated from AFDB bias (**as the pretrained PiFold model was pretrained on PDB structures**), we still observe a notable performance drop on raw AFDB data, and a partial recovery after applying our debiasing procedure:\n\n|          | PDB | AFDB | Debiased AFDB |\n|----------|-----|------|---------------|\n| KWDesign |57.34|51.60 | 54.71 |\n\nThese results reaffirm that AFDB bias negatively impacts performance, even for large pretrained models, and that DeSAE debiasing provides measurable benefit even in this setting. While KW-Design is already highly capable due to its pretrained components, the consistent trend holds: Debiased AFDB structures bring the performance closer to the PDB-trained upper bound.\n\nWe believe this further supports the model-agnostic nature of the structural bias problem and demonstrates the general utility of DeSAE across small and large-scale models alike.\n\n[3] Gao et al. KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement. ICLR, 2023."}]

Author rebuttal:

Meta-review: (4,5,5,5) This paper identifies and quantifies systematic geometric bias in AlphaFold Protein Structure Database (AFDB) structures, demonstrating that these biases degrade downstream model performance, especially in inverse folding tasks. The authors introduce DeSAE, a lightweight SE(3)-equivariant autoencoder that debiases AFDB structures, resulting in consistent improvements across multiple structure-based benchmarks. All reviewers were positive, noting the importance of the problem and the rebuttal processed addressed initial concerns about evaluation scope and baselines.
Decision: Accept (poster)