ProtPainter: Draw or Drag Protein via Topology-guided Diffusion
Authors: Zhengxi Lu
Shizhuo Cheng
Tintin Jiang
Yan Zhang
Min Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: Protein Backbone Generation
Conditional Diffusion
Topology
Protein Editing
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Recent advances in protein backbone generation have achieved promising results under structural, functional, or physical constraints. However, existing methods lack the flexibility for precise topology control, limiting navigation of the backbone space. We present $\textbf{ProtPainter}$, a diffusion-based approach for generating protein backbones conditioned on 3D curves. ProtPainter follows a two-stage process: curve-based sketching and sketch-guided backbone generation. For the first stage, we propose $\textbf{CurveEncoder}$, which predicts secondary structure annotations from a curve to parametrize sketch generation. For the second stage, the sketch guides the generative process in Denoising Diffusion Probabilistic Modeling (DDPM) to generate backbones. During the process, we further introduce a fusion scheduling scheme, Helix-Gating, to control the scaling factors. To evaluate, we propose the first benchmark for topology-conditioned protein generation, introducing Protein Restoration Task and a new metric, self-consistency Topology Fitness (scTF). Experiments demonstrate ProtPainter's ability to generate topology-fit (scTF $>$ 0.8) and designable (scTM $>$ 0.5) backbones, with drawing and dragging tasks showcasing its flexibility and versatility.

Published as a conference paper at ICLR 2025
PROTPAINTER: DRAW OR DRAG PROTEIN TOPOLOGY-GUIDED DIFFUSION
VIA
Zhengxi Lu + Shizhuo Cheng
Yuru Jiang
Yan Zhang to
Min Zhang to
ZZhejanng University
(3200105645, 0A2144112211111
2221133,, Re 32002222202122 (aw.i:2266,,,2nngggo zhang-yan www.yyaam..i...hangg.. jzju edu. cn
ABSTRACT
Recent advances in protein backbone generation have achieved promising results under structural, functional, or physical constraints. However, existing methods lack the flexibility for precise topology control, limiting navigation of the back- bone space. We present ProtPainter. a diffusion- approach for generating protein backbones conditioned on 3D curves. ProtPainter follows a two-stage process: curve-based sketching and sketch-guided backbone generation. For the first stage, we propose CurveEncoder, which predicts secondary structure anno- tations from curve to parametrize sketch generation. For the second stage, the sketch guides the generative process in Denoising Diffusion Probabilistic Model- ing (DDPM) to generate backbones. During this process, further introduce a fusion scheduling scheme, Helix-Gating, to control the scaling factors. To evaluate, propose the first benchmark for topology-conditioned protein gen- eration, introducing Protein Restoration Task and a new metric, self-consistency Topology Fitness (scTF). Experiments demonstrate ProtPainter's ability gen- erate topology-fit (scTF >> 0.8) and designable (scTM >> 0.5) backbones, with drawing and dragging tasks showcasing its flexibility and versatility.
INTRODUCTION
Recently, generative models based on the denoising diffusion framework (Ho et al. et al. 2020) have shown remarkable success in creating realistic protein backbones, inc including RFD- iffusion (Watson et al. 2023), Genie (Lin Alquraishi] (023). FrameDiff (Yim et al.
2020: Song
2023b) and
others. Flow-based models like FrameFloww (Yim et al., 2023a and FOLDFLOW-OT Bose et al.)
2023) incorporate techniques such as flow matching or Riemannian optimal transport, demonstrat- ing unprecedented performance in designability and efficiency (Zheng et al. (024). A follow-up question how users could harness their imagination to guide the generation process in produc- ing proteins with the desired structure and functions, expanding the possibilities beyond what is achievable with those unconditional generation models.
Some models explored non-structural conditions such biochemical properties (Hsu et al. 2024)
and desired functions (Komorowska et al. 2024 Kulyte et al., 2024). Others attempted to generate backbones with structural conditioning like contact maps (Harteveld et al. 2023), partial structural motifs (Watson et al., 2023 Lin et al. 2024: Ingraham et al. 2023). symmetry (Ingraham et al., 2023 Watson al., (2023). and point cloud (Ingraham al. |023 Long et al. 2022). The above methods provide various forms of structural constraints designed for downstream tasks like motif- scaffolding, binder design, and symmetric oligomers design. However, they lack a conditioning mechanism for more detailed and precise structural control, thus posing a challenge for delicate backbone space navigation, which is crucial for tasks like multi-state design (Praetorius et al. 2023) and allosteric protein design (Pillai et al.).
Topology with secondary structure (also referred to as Blueprint or Topo) (Correia) 2024 Harteveld et al. 2023 Zhang et al. Harteveld et al. 2022 Huddy al. 2024 Huang et al. 200 4 2022)
Topology, CG
is a more programmable mechanism. It represents proteins at a higher level
"Equal contribution.
1 "ooresponniig author.

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Published as a conference paper at ICLR 2025
of abstraction, focusing on the arrangement and connectivity of secondary structure elements such as a-helices and 8-strands, allowing designers to specify the overall shape and structural organization of a protein. This mechanism is particularly useful for creating proteins with repetitive structural features, such as multi-subunit protein assemblies (Lutz et al., 2023) and nanomaterial (Huddy et al.) 2024). However, current methods are heavily based linear, parametric topology configuration, which defines the topology with a set of parameters that describe the geometric relationships be- Iween Secondary Structure Elements (SSE). While powerful, it suffers from limited coverage of the three-dimensional topology space, potentially constraining the diversity of protein designs that can be generated, not to mention editing the topology of proteins flexibly. Expanding beyond these para- metric constraints could open up possibilities for exploring a broader range of protein topologies and enable more controllable protein design.
Our approach We present the first method that uses 3D curves as topological constraints to de- fine protein folds. The 3D curve representation encompasses critical structural features, including the number of helices, their relative lengths, orientations, positions, and the curvature of helices, providing more detailed and precise topology description.
In analogy super-resolution tasks in imaging (Choi al. 202T), our objective is equal to refine the coarse-grained 3D curves into fully structured protein backbones. In this context, our curve condition corresponds to the reference image y, and the generated backbones correspond to the re- fined images in the DDPM framework. For imaging, reference and generation can be easily aligned and sampled into latent space of the same dimensions. But for protein structures, the alignment could be hard. To address this challenge, we propose CurveEncoder that upsamples the condi- tion curve into sketch, while a filtering operation downsamples the generated backbones into a frame. This ensures that the condition curve and the backbone frames share the same dimension- ality. To enhance the quality of translation generation, we build on the approach from RFDiffu- sion (Watson et al. 2023), utilizing RoseTTAFold estimates p.o for translational guidance based on self-conditioning (Chen et al., 2022). In summary, our method introduces a novel approach that leverages naive sketches bridge curves and backbones, extending the DDPM framework to enable more flexible topology control.
Main Contributions
The main contributions are summarized as follows:
1. We propose ProtPainter, the first method generate protein backbones with specific topology based on 3D curves. For the first stage of ProtPainter, sketches are parametrically created with the assistance of a CurveEncoder, which extracts local geometric features and predicts the SSE of curves. For the second stage, we present a retraining-fres method to guide the generative process in DDPM and generate designable backbones based on a given reference sketch. During this process, a sketch fusion scheduling mechanism, Helix- Gating, used to determine the scaling factor by incorporating helix perceenaee guidance.
2. We provide benchmark to evaluate curve or topology-conditioned backbone generation. 1) We propose a new metric scTF to assess the topological similarity between the generated backbone and curve condition. 2) We offer a method to generate a dataset of curves from protein backbones and implement series of curve-based operations, including jointing, dragging, and drawing. And 3) we also develop a Protein Restoration Task to compose the benchmark.
3. We demonstrate that this new modality can be well applied to downstream tasks such as binder design, motif scaffolding, and dragging. Notably, an empirical example demon- strates that for multi-state designs like hinge proteins. ProtPainter provides an easy way to create preliminary scaffolds with good quality for further design.
BACKGROUND AND RELATED WORK
2.1 DIFFUSION PROBABILISTIC MODELING
The Diffusion Probabilistic Modeling DSchl-Dickstein al.; 2015) formulates the model training as follows. Given a forward diffusion process, the model predicts the noise added to the original sample time f. For sample from the training set &o. the forward process is defined iteratively
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adding a small amount of Gaussian noise to the sample in T steps, which produces sequence of noisy samples 20.T such that the final sample 2T (N 1) to a good approximation. Within the framework of Denoising Diffusion Probabilistic Modeling (DDPM) (Ho et al. 2020), the noise magnitude at each step is defined by a variance schedule 3,, f. E [0 7) such that
pr (r(2t/a111 = Na / B, 3,1)
(1)
The above transition defines a Markov process in which the original data transformed into a standard normal distribution. It is possible to write the density of xa given an in closed form
(r(2)/ro) N( Varso. (1 - ā.) s.l. 2t - Varoo v1 ārEt,
where a₂ IT,a and 0; 3, and 6 ~ (0,
Transforming a sample 2T into sample Tu is done in several updates that reverse the process of adding destructive noising, given by reverse sampling scheme
v1 -aaviiaaaaa 1 ā₁
to(m1,t) + (1 aa)e.
2t
(3)
Vai
where e NV(0,), 1). The neural network Eo (the denoiser) is trained to predict noise added to zo-
2.2
GUIDED DIFFUSION FOR BACKBONE GENERATION.
Guided sampling has been applied diffusion models to generate samples with human instructions, formulating the sampling process conditional and unconditional terms. Considering conditioning variable y. classifier-guice
methods like Chroma (Ingraham et al., 2023), train a time-dependent classifier model pt (yla:) the noised structures St pr( (r)) and adjust the sampling posterior
V. log Pt (x by Bayesian inference. Instead of training a classifier to estimate pt Pr(y (z),, classifier- free methods approximate the conditional term heuristically. [Wang et al. (2024) and Komorowskal et al. (2024) introduce the physical force to extend an unconditional model to dynamic conformation sampling. Force guidance is also applied generate antibody (Kulyté et al. 2024) with lower energy.
2.3 TOPOLOGY-BASED BACKBONE CONTROL
Controlling protein topology has been a long-standing challenge to go beyond the linear configu- ration of SSE (also known as Blueprint (Xu Zhang 2018). Common blueprint-basd methods describe the number and approximate locations well the overall orientation of the secondary
2022; 2023 Westhead et al.) 1999), which is preliminary but essential for de novo protein design Kortemme 2024), binder de-
structure elements parametrically (Kortemme) 2024 Harteveld et al.
sign (Levy et al. 2004). and membrane protein design (von Heijne]
2006). These parameterized
representations are powerful for repetitive assembly but limited by their degree of freedom, tough- ening general users to control more complex 3D topology flexibly.
Topology-basee Diffusion Models for Backbone Generation To gain more control like tradi- tional blueprint- based methods, some diffusion-basee models have taken topology condition: Topodiff (Zhang et al.) encodes the global topology in the latent space with VAE, enabling topo- logical control by giving querying protein, but not supporting more detailed topology editing. Dif (Correia 2024) employs the diffusion model to generate the sketch from a predefined SSE sequence and then inputs it to RFDiffusion for a realistic backbone. However, its topology defini- tion follows linear configuration and lacks control of faree-dimensione topology. In conclusion, current topology-bant diffusion models do not support more flexible and detailed topology control.
3 METHOD
The generation process is split into two stages: curve sketching (introduced in Section 3.1 and sketch-guided sampling (introduced in Section 3.22. To improve the guided sampling process, Helix- Gating, fusion scheduling mechanism, is detailed in Section 3.3
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SSE
predict
SSE...
Free Paint (wtt contwer)
Topo Ediable Dave
CurveEncoder
Sketching
Sketch
Tradeoff
%g
RF
HG
x
Backbone
Guided
Phase Stop
Sampling
phase
phase on
Phose., Step
Goting
Interpolation
Figure 1: Architecture. Sketching: given 3D curve, SSEcurve is predicted by CurveEncoder. Then naive sketch is generated parametrically. Guided Sampling: the sketch fused into a diffusion sampling process with the guidance of RoseTTAFold and Helix-Gating interpolation.
Fusion Schedule
Helix-Gating
Gruezian noloe
Gancttion
Helix%()
(yö(;)) +1²² phase
Cankddetel pluss
Dasoediiional
++ Via
Cantrolacle prana
phase an
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Helix%(
Figure 2:
Sketch Fusion Scheduling with Helix-Gating, a. Helix-Gating splits the sampling process into two phases by comparing the helix percentage of & and y. enabling the scheduling of fusion. The curve space trajectories of different diffusion sampling processes.
3.1 SKETCHING CURVES
This stage begins by defining forward process that abstracts backbone topology into repre- sentations. Next, we introduce the CurveEncoder, which annotates curves with SSE labels, denoted SSEcurve. The final sketch is then generated parametrically, guided by SSEcurve.
Topology Represented by Curves To represent the topology of protein C. backbones as curves, employ downsampling method detailed in Appendix A. Specifically, a-helices and 3-sheets are abstracted to their central axes, while loop regions retain their original coordinates. The resulting curve coordinates are re-sampled, smoothed, and annotated with SSE labels by averaging the labels of their nearest backbone atoms.
CurveEncoder This module is designed to predict the SSE annotation for curves SSEcurves as reverse process for curve SSE assignment. Inspired by Greener & Jamali (202)), three-layer EGNN (Satorras et al. 2021) applied to extract connectivity features of curve coordinates, and one-dimension CNN to extract the curvature feature as a complement. Then multi-head attention layer integrates the features and predicts the secondary structure element annotation for SSEcurve- SSEcurve can be customized by user input. Given node embeddings h'' and coordinate embeddings x' of layer I, and edge information & (esy), the Equivariant Graph Convolutional Layer (EGCL) written h1+1, T1+1 EGCL E). The algorithm is shown in Algorithm and details shown in Appendix D The parametric approach to generate the naive sketch with curve coordinates and SSE shown in Appendix
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3.2 SKETCH- SKETCH-GUIDED BACKBONE SAMPLING
Our model uses the frame representation following (Watson al. 2023), which comprises the trans- lation 2 (Ca coordinates) and rotation r (N-Ca-6 rigid orientation) for each residue. Consider X₁ [2e. are the residue frames at diffusion step where E Ig x3 are the coordinates of Ca (translation part) and r₁ E (3)3) Neea the rotation matrix (rotation part).
The translation part is generated from the 3D Gaussian noise by DDPM.
1 ai-1
p(21-122, 20) N(z= 20), with
B,
Be Be, Ore
(4)
where
V 00
1
Var(1 (1- a4-1)
(zzz 20)
20 +
24:
(5)
1
at
1 ae
and can be estimated by RoseTTAFold prediction in with masked sequence input, inspired by RFDiffusion.
For residue orientations, Brownian motion is used on the manifold of rotation matrices. The frames are equivariant with respect to rotation.
po( Po Ro(R= x z x2- R* mz),
where R# 24 = [Rz, Rr).
(6)
Given reference naive sketch y IR [R- define conditional distribution of guidance term 9t-1:
9t-1 ( y, 26).
Choi et al. (2021) refines the generation conditioned the reference y by
Po ( y) R= po (2 (())-)) = o(y--1)-
(8)
where á is a linear low-pass filtering operation to ensure the low-pass features of the reference image and the generation images remain the same. We adopt this idea of aligning generation reference at low dimensions with sketch being the bridge. The proposed CurveEncoder upsamples the condition curve to a sketch and & filters the generation backbones into a frame. The generated backbone frames zo now be guided by reference sketch through the filtered frame part Φx (2a). Here define the frame filter operation as Φix (X₂) = Az(X,) where 2 (X,) extracts the Co coordinates of frames X, and X (between 0 and is factor for tradeoff between diversity and guidance.
We approximately treat rotation (rx) and translation (z₂) as independently distributed variables under general translation condition CT.
po De, 0r) Pa (2 |ae, or
Pa( = po (T).
(9)
Combining equations 8 and we have
p y) se p0(20-1/01,
= (
(10)
We only need to update 2
24-1 = (()+-1) +2/-1 ((rk_1)
(11)
where r is sampled from the unconditional distribution proposed by Tt, x/-1
and
={1 is the translation part of x/ 1- Set operation on the equation we have
Var(1 x.-1)
x0(2).
(12)
1 1- C2
So the conditional probability approximation is
Von.-180 2/
varll - k(-1)
2t-1
(1 A) 24 + X. 1-
(13)
1 ae
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3.3 HELIX-GATING: CONTROLLING SCALING FACTORS
We propose Helix-Gating, a two-stage fusion scheduling scheme to enhance the guided sampling process with sketch y. The transition timing between (1) the confidential phase and (2) the con- trollable phase is determined by comparing the helix percentage (operator denoted as O) between RF predicted 2 and sketch y. In the confidential phase, the guidance is limited and scaled using the difference in helix percentages between predicted and target proteins, ensuring a constant fi- delity increase with limited guidance from sketch y. In the controllable phase, the guidance fully provided:
vor 01-1)
Vt-1 7. ((((())) + n, if ((8) O3) (Confidential Phase)
F(y, (8) y 1-a
(14)
Fly, 20)
(15)
if ( 2 O (Controllable Phase)
where y and 7) are hyper-parameters, 8 the difference function. We set À 2/3, 7 0.2, 17 0.7, according to the ablation study G.5 Figure 2b illustrates this process: once the helix percentage reaches a threshold, the controllable phase begins, and more condition information integrated, refining the generation to align closely with the sketch. Ablation 3tudy3Jand Appendix 12 demon- strate the effectiveness of Helix- GaiinGG
3.4 DRAWING BINDER AND DRAGGING PROTHIN
Dragging protein is formulated as curve-conditioned motif scaffolding. In this process, the curve of the dragged protein serves as the scaffold to be generated, conditioned on its updated shape, while the fixed part is treated as the motif condition. For structure with length L, let M and S be the index set of motif and scaffold, respectively, that is M MSS = {1. [1., L}. So let the structure of motif and scaffold be M and 2.S. respectively. The whole un-noised structure is zo [=g ..y.. The x0 backbone and sidechain structure are input as fixed templates of Rose to predict 2 which influences the translation part, with the motif fixed masked, the denoising process becomes pe pe(z l M cT). ). We approximate that
2.M po po 20" cr) = po(o2 er (oo
(16)
To draw binders, we first calculate the distance between the curve and the target protein to identify "interface hotspots" z4 the target. The complex design is then conditioned both the curve and the target protein, incorporating these hotspot residues.
4 EXPERIMENTS
4.1 PROTEIN RESTORATION
We evaluate the conditioning effectiveness and generation quality of ProtPainter by our proposed Protein Restoration task, which is defined as given the condition representing an existing protein target, a deep learning method generates designable backbones to the curve topologically (details in Figure 7). We conduct experiments in two aspects: (I) preliminary topology fitness before re- folding" and (2) topology fitness and designability after refolding with comparison. Considering the curve as a novel condition, the goal is to demonstrate that our method achieves more precise and fine-grained control over topology while maintaining high generation quality.
Metrics We evaluate the restoration capability of methods mainly on the three metrics:
Designability. Designability quantified through backbone TM-score before and after refolding (scTM, higher is better). In this work, each curve generates No. backbones and refolding pipeline uses ProteinMPNN (Dauparas et al. [2022) at temperature 0.1 to generate Nseq sequences for Omegafold (Wu et al. 2022) to predict;
Design sequence and predict all-atom structure.

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Confidence. Confidence in refolding is measured the pLDDT of the predicted structures to test the Docaa-Distance Difference;
Similarity. To measure this, we propose self-consistency Topology Fitness (scTF), which is calculated as the Procrustes similarity between the refolded (or un-refolded) backbone curve and the curve guidance. The threshold for scTF is chosen from the experience of topological similarity (shown in Figure 6 and the proportional relationship with scTM (shown in Figure
Preliminary Topology Fitness For preliminary evaluation of the topology control ability, choose representatives six topology families, three architectures in Mainly Alpha class from In this experiment, ProtPainter attempts store them conditioned on their curve representations (no refolding needed). In Table | we find that ProtPainter is capable of generating structures barely similar (scTF 0.7) to more than 60 percent of diverse topologies in Mainly Alpha class (CATH ID = I). And it performs well especially on topologies in the Up-down Bundle (CATH ID 1.20) with the portion of scTF 0.7 near 0.8. How- ever, the portion goes down to 0.249 for DNA polymerase (CATH ID = 1. 1.10.150) for its topology complexity.
CATH (Orengo et al. 1997, Sillitoe et al. 2021 {
Table Protein Restoration Task on CATH without refolding.
CATH ID
1,10.150
1.10,287
1.20.5
1,20.58
1.20.120
1.25.40)
count
976
299
428
scTF > 0.7
0.249
0.642
0.849
0.797
0.816
0.615
scTF > 0.8
0,0759
0.470
0,716
0.633
0.626
0.375
0.394
Topology Fitness with Designability To evaluate the topology fitness and designability, we select 10 monomer structures cases. These cases are restored by ProtPainter conditioned on curves and refolded to evaluate their designability (shown Table [ We can restore most topologies and generate both similar and designable structures but there some exceptions like 1AV1, 7KUW, and 4DB8. We also display some cases in Figure 3 including binder design and motif scaffolding.
Table 2: Cases of Protein Restoration Task with refolding.
ID
IP68
6891
ITQG
7KUW
4D88
2N81
ITJI.
IAVI
014842
P30968
length
82
249
93
55
220
84
205
284
319
scTM
0.9655
0.9093
0.9588
0.5149
0.7318
0.7071
0.6862
0.3292
0,9508
0.9396
sCTF
0.944
0.956
0.980
0.854
0.771
0.628
0.614
0.920
0.938
0.942
PLDDT
94.883
86.986
86.788
48.34
80.809
85.826
79.044
88.566
94.754
86.894
We compare our method with trateoffthhe-rt unconditional diffusion model RFDiffu- sion (Watson et al. (203) and conditional diffusion models Chroma (Ingraham et al. 2023). and TopoDiff (Zhang et al.). The modalities of conditions are as follows,
Baselines
RFDiffusion is conditioned sequence lengths.
Chroma is conditioned on secondary structure annotations.
Chroma incorporates point clouds as the condition, instead secondary structure anno- tations. These point clouds generated by constructing curved cylinder using 3D curve as the central axis, with a radius varying from 0 to 4 A. The point cloud is uniformly distributed within the cylinder's volume.
TopoDiff is conditioned the topology latent, such latent-based linear interpolation between two structures, which is the most relevant to our method. To compare it with ours, curves preprocessed by our CurveEncoder and transformed into sketches. Then the sketches are mapped into the latent space as TopoDiff's s DDIM conditions.
2AATH ID is composed of Class, Architecture, Topology and Homologous Superfamily which sepa-
rated by "*
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denovo
binder
motif scaffolding
original
JARARA
condition
NAAAP
restored
original target
0.9588
5 h
ScRMSD
context
PLDDT
PLDDT
ScTF 0.966
ScTF 0.980
restored target
condition
Figure 3: Examples of ProtPainter on de novo protein design, binder design, and motif scaffolding.
From left to right, the original structures are 6s91, ltqg. 7f4d_MR, 7f4d_GB, and 1031. Curves visualized in 3D space. Other examples are shown in Figure 14
Data For the evaluation dataset, We select three representative protein clusters ordered by increas- ing length and topological complexity: HHH ems laOb la0b_clusti clobsclster] and GPCR Each comprises 50 backbones restore (refold needed). Proteins the same dataset share similar topologies but exhibit subtle structural differences (as shown in Figure 8 a).
Table 3: Comparison of Similarity and Designability Protein Restoration Task. Confident des- ignability (CD) is the portion of proteins with scTM >> 0.5 and PLDDT 70, and designability (FD) is the portion of proteins with scTM 0.5, pLDDT 70, and scTF 0.7. Each metrie is evaluated with 500 backbones selected from 4000 sequences
Condition
HHHl
med
cRMSIJ
s:TM?
x/FF1
C37
F12
scRMSD
s/TM
x/TP
(1)
F1)
sIRMSD
s:TM
s:TF
(D)
FI
MFIXifimion
0.753
0.910
01021
0.980
0.540
1.064
0.962
0.212
0.224
0.040
2.402
0.905
[1262
0.886
0.000
SSE
Chacma(rmd)
0.742
Chremayy=))
4.011
0.333
0.542
0.784
0.144
10.900
0.60
0.259
0.544
0.042
20.405
0:474
0.229
0.083
0000
PoinCloud
Chromatr-2)
0.744
8:54
0.220
0.000
Checmatr=d]
17,497
Chremalmm4)
4.048
0.723
0.574
0.840
0.000
7.138
0.717
0.215
0.583
0.000
17.368
0.477
0.227
0.166
0.000
Tepolggy
TewDbif
0.711
0.997
0.350
0.58
0.160
1.62
0.911
0.247
0.992
0.070
11.420
0.53
0.179
0.340
0.000
Curve
ProtPaineerOus)
0.792
Comparison results The results shown in Table
First, compared state-of-the-art meth- ods, ProtPainter demonstrates convincing performance in confident designability (CD), reflecting its capability to generate high-confidence designs. Second, ProtPainter excels understanding human-defined topologies and achieves state-of-the-art control over them, as evidenced by its supe- rior values and trends in FD and scTF metrics. In contrast, other conditional methods are limited
A cluster of simple proteins consisting of 3-helix bundles connected by loops, with lengths between 50 and
60
structurally similar to la0b,pdb using PDBefold (Dietmann et al. 2001). with lengths ranging from 100 250 and redundant structures
'Top 50 proteins in PDB (Burley et al. 2023 Berman al. 2003)
'The largest superfamily of cell surface membrane receptors, encoded by approximately 1000 genes, char- acterized by conserved seven-transmenn transmembrane (7TM) helices connected by three intra- and three extracellular
lnops (Rosenbaum et al. 2209 Fichel & von 7astrow 2018 Katritch et al. 2012 Zhang et al.] 2024), with lengths hetween 280 and 400
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in the precise topological control. Point clouds utilized by Chroma only offer vague spatial condi- tioning and lack sufficient internal topological information for precise control. Moreover, protein latent space used by TopoDiff reveals an ambiguity in understanding sketches, thus struggling to store the overall topology defined as human-drawn curves. Finally, ProtPainter slightly outperforms RFDiffusion in FD for shorter proteins (length < 60), likely due to the simplicity and similarity of protein folds at this length. In summary. ProtPainter offers significantly more precise and detailed topological control over the backbone while maintaining high design quality.
4.2 USER STUDY: CURVE FROM SCRATCH
In addition to generating sketches from existing protein structures, we introduce three approaches enabling users to create novel 3D curves:
1. Users modify an existing 3D curve by dragging anchor points to reshape it, generating a new curve.
2. A 2D curve can be drawn from scratch on a sketchpad for convenience. This curve is then converted into 3D by assigning random, smoothly varying depths to its points. Experimen- tal results are presented in Table 4
3. By installing a ChimeraX plugin, users can draw curves directly on a protein surface, defin- ing binder conditions. Secondary structure elements for the generated binder can also be as- signed. The plugin installation code available at hhtpp:: / /ggtt/ttttbbb com/ 111e1111111 /gg/ ChimeraX_pllue biiluiinnideerr
For Method 2. we begin by generating 100 -wo-dimensional curves from scratch. Among these, 80 created using a 2D curve generator, while the remaining 20 are drawn manually by humans. Depth values are then assigned to the points, varying randomly yet smoothly. The low-frequency component is modeled as sinusoidal function, with random noise added as high-frequency vari- ations. Notably, all curves have lengths of less than 100, and each contains fewer than six helix bundles. Some of the cases are shown in Figure 15
Table Results conditioned on curves from scratch.
Source
scRMSD,
ScTM
scTFt
CD
Human
3.278
0.782
0.745
0.75
0.65
Curve Generator
3.565
0.768
0.696
0.8875
0.575
All
3.508
0.771
0.706
0.86
0.59
Additionally, to simulate the user drawing process, we introduced noise by randomly perturbing each point within sphere centered at its original position. The results in demonsttate our ability to understand and address potential challenges faced by users.
4.3 NAVIGATION OF PROTEIN TOPOLOGY SPACE WITH PROTPAINTER
To demonstrate that curves as the prerequisite condition enable flexible topology editing while re- laining designability, we show empirical cases such as dragging, rotating, joining, and local SSE editing. Novelty calculated as the maximum TM score (maxPDBTM) relative to PDB (Burley
et al. 2023 Berman et al. 2003) with Foldseek (Van Kempen et al. 2024). As shown in Figure 4 dragging proteins enables topology transition from the three-helix bundle (2) to (3) and finally to the two-helix scaffold (4), all retaining designable (scTM > 0.5). Local SSE editing enables drastic backbone change while the topology remains unchanged (TF = 0.983). comprehensive exam- ple of hinge protein (Practorius et al.) 2023) design (6,7,8) demonstrates that ProfPainter-generate helix bundles can transit between different topologies while retaining designability. The topology space trajectory of the jointing domains (9, 10) at different angles (points in purple) indicates that the novel topology (11) I) can be obtained in this way.
In conclusion, with the help of topo-editable curves, ProtPainter achieves unprecedented structural control in diffusion- biffusion-based backbone generation, enabling more natural, flexible, and precise topol- ogy space navigation compared to traditional structure editing.

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8
Maxdmum score reletive PDB 0,5
Siart Topo
Drag
PDG Rep
Drag
Edit SSE Hinge
ScTM 0.7321
0.739
0.8673
0.983
MDS Dimension
Rotate
Joint
Rotale
ScTM 0.9888
ScTM 0.9593 maxPDBTM 0.4919
MDS Dimension
SCTF 0.949
Figure 4: Draw and edit process. (a) Structures are visualized the MDS topology space, with their colors corresponding to respective operations. Novelty is measured the maximum TM score relative to PDB (Burley et al. 2023 Berman et al.) (003) in the upper left corner. (b) Case study for actions of dragging, SSE editing, comprehensive tasks like hinge protein, and jointing.
4.4 ABLATION STUDY
We perform experiments on key components of ProtPainter. The parameters set Neurve = 50 per dataset, Nos- = 5, and Naa = 8. We define fit-designability scTM > 0.5, PLDDT > 70, and scTF > 0.8. The results are shown in Table I2 We conduct the If no Helix Deatng scheme is applied, the Two-
Table 5: ProtPainter ablations.
Fitdesignability (1)
Curve Encoder
Helix-
Twn-
Helix- Gating
GPCR
HHH_ems
Guiding
Phase
0.56
0.656
0.53
Phase transition occurs at timestep 10. If no CurveEncoder is used, secondary struc- ture elements (SSE) are randomly labeled based the target portion. Additional ab-
0.588
0.48
0.668
0.392
0.388
0.20
0.112
0.196
lations are provided in Appendix
0.296
0.18
CONCLUSION
In this work, we propose ProtPainter, which applies a new representation structural condition to generate designable protein backbones with topological control, enabling powerful topology space navigation through curve-based operations, enriching current protein design meth- ods. We already tested method on many downstream tasks with amazing results. Considering the sampling time, the sketching process is efficient, but backbone generation and refolding are cine-consuming, taking between 10 seconds and minutes on single NVIDIA. To enable real- time protein design, more optimizations are needed to reduce inference time. Furthermore, we plan work on beta sheet in future work, which briefly discussed in Appendix
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6 ACKNOWLEDGMENTS
This work is supported in part by the National Science and Technology Major Project Grant No. 2022ZD01 17000 M.Z., the National Nature Science Foundation of China Grant No. 62202426 to M.Z., the National Natural Science Foundation of China Grant No. 92353303/3214 117732211104//1920200000 Y.Z., and the 'Pioneer' and 'Leading Goose' R&D Program of Zhejiang Grant No. 2024C03147 to Y.Z..
REFERENCES
Helen Berman, Kim Henrick, and Haruki Nakamura. Announcing the worldwide protein data bank. Nature structural & molecular biology, 10(12):980-99) 2003.
Avishek Joey Bose, Tara Akhound-Sadegh. Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, and Alexan- der Tong. Se stochhasi flow matching for protein backbone generation. arXiv preprint 1rXiv:2310.02391, 2023.
Stephen Burley, Charmi Bhikadiya, Chunxiao Bi, Sebastian Bittrich, Henry Chao, Chen, Paul A Craig, Gregg V Crichlow, Kenneth Dalenberg, Jose M Duarte, et al. Resb protein data bank (resh. org): delivery of apperimentlllyddeermiiin pdb structures alongside one million computed structure models of proteins from artificial Intelligence/machine learning. Nucleic acids research, 51(D1):D48-DD50, 2023.
Ting Chen, Ruixiang Zhang, and Geoffrey Hinton. Analog bits: Generating discrete data using diffusion models with self-conditioning. arXiv preprint arXiv:2208.04202. 2022.
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon. Ilvr: Con- ditioning method for denoising diffusion probabilistic models. In Proceedings the IEEE/CVF International Conference on Computer Vision, pp. 14367-14376, 2021.
Bruno Correia. Difftopo: Fold exploration using coarse grained protein topology representations. bioRxiv, pp. 2024-02, 2024.
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al. Robust deep learning- based protein sequence design using proteinmpon. Science, 378(6615):49 2022.
Sabine Dietmann, Jong Park, Cedric Notredame, Andreas Heger, Michael Lappe, and Liisa Holm. A fully automatic evolutionary classification of protein folds: Dali domain dictionary version 3. Nucleic acids research, 29(1):55-555 2001.
Kelsie Eichel and Mark von Zastrow, Subcellular organization of gper signaling. Trends in Phar- macological Sciences, 39(2):200- 200-088, 2018.
Joe G Greener and Kiarash Jamali. Fast protein structure searching using structure graph embed- dings. bioRxiv, pp. 2022-11, 2022.
Jiaqi Han, Yu Rong, Tingyang Xu, and Wenbing Huang. Geometrically equivariant graph neural networks: survey, arXiv preprint 07X2:22002.072300 2022.
Zander Harteveld, Jaume Bonet, Stéphane Rosset, Che Yang. Fabian Sesterhenn, and Bruno E Cor- reia. A generie framework for hierarchical de novo protein design. Proceedings of the National Academy of Sciences, 119(43):c22061 111119, 2022.
Zander Harteveld, Alexandra Van Hall-Beauvais, Irina Morozova, Joshua Southern, Casper Goverde, Sandrine Georgeon, Stéphane Rosset, Michëal Defferrard, Andreas Loukas, Pierre Van- dergheynst, et al. Exploring "dark matter" protein folds using deep learning. bioRxiv, pp. 2023- 08, 2023.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.

---

Published as a conference paper at ICLR 2025
Chloe Hsu, Clara Fannjiang, and Jennifer Listgarten. Generative models for protein structures and sequences. nature biotechnology. 42(2))199-193 199, 2024.
Bin Huang, Yang Xu, Xiuhong Hu, Yongrui Liu, Shanhui Liao, Jiahai Zhang, Chengdong Huang, Jingjun Hong, Quan Chen, and Haiyan Liu. A backbone-centred energy function of neural net- works for protein design. Nature, 602(7897) February 2022. ISSN 0028-0836, 1476-
4687. doi: 10. 10.10088441566-021-4433333 URL https: www. nature. com artp/iettiicce/// 541586-021-043833
Po-Ssu Huang, Gustav Oberdorfer, Chunfu Xu, Xue Y. Pei, Brent L. Nannenga, Joseph M. Rogers, Frank DiMaio, Tamir Gonen, Ben Luisi, and David Baker. High thermodynamic stability of
parametrically designed helical bundles. Science, (1666208)448144535 October 2014. ISSN 0036-8075, 1095-9203.
doi: 10.1 (0.1126/6 16.1126//ee 1257481.. URL https: /www sci ence. org
doii 1121111266100 sww./ce.eecccce/eee 1257481
Timothy F. Huddy, Yang Hsia, Ryan D. Kibler, Jinwei Xu, Neville Bethel, Deepesh Nagarajan, Rachel Redler, Philip J. Y. Leung, Connor Weidle, Alexis Courbet, Erin C. Yang, Asim K. Bera, Nicolas Coudray, S. John Calise, Fatima A. Davila-Hernaaddz, Hannah L. Han, Kenneth D. Car,, Zhe Li, Ryan McHugh, Gabriella Reggiano, Alex Kang, Banumathi Sankaran, Miles S. Dickin- son, Brian Coventry, T. J. Brunette, Yulai Liu, Justas Dauparas, Andrew J. Borst, Damian Ekiert, Justin M. Kollman, Gira Bhabha, and David Baker. Blueprinting extendable nanomaterials with standardized protein blocks. Nature, 627(8005):888-944, March 2024. ISSN 0028-0836, 1476- 4687. doi: 000100088441188--24007771111 641586-024-0788-4
URL httphttp//// www nat ure. wwt ctt.erticcc//////////cccccc
John Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent M Lord, Christopher Ng-Thow-Hing Erik R Van Vlack, et al. Illuminating protein 2023.
Frappier. Dana M
space with a programmable generative model. Nature, 623(7989)
Vsevolod Katritch, Vadim Cherezov, and Raymond C Stevens. Diversity and modularity of protein-coupled receptor structures. Trends in pharmacological sciences, 33(1):17-27, 2012.
Urszula Julia Komorowska, Simon V Mathis, Kieran Didi, Francisco Vargas, Pietro Lio, and Mateja Jamnik. Synamics-informee protein design with structure conditioning. In The Twelfth Interna- fional Conference on Learning Representations, 2024.
Tanja Kortemme. De novo protein design from new structures to programmable functions. Cell, 187(3):526554) 544, 2024.
Paulina Kulyté, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández- Lobato, and Pietro Liò. Improving antibody design with force- prree-gided sampling in diffusion mod- els. arXiv preprint 2xXiv:2406.08822 2024.
Yaakov Levy, Peter G Wolynes, and José N Onuchic. Protein topology determines binding mecha- nism. Proceedings of the National Academy of Sciences, 101(2):511-11 2004.
Yeqing Lin and Mohammed Alquraishi. Generating novel, designable, and diverse protein struc- tures by equivariantly diffusing oriented residue clouds. In International Conference on Machine Learning, pp. 20978-22 20978-21002. PMLR, 2023.
Yeqing Lin, Minji Lee, Zhao Zhang, and Mohammed AlQuraishi. Out of many, one: Design- ing and scaffolding proteins the scale of the structural universe with genie 2. arXiv preprint 2rXiv:2405.15489 2024.
Siyu Long, Yi Zhou, Xinyu Dai, and Hao Zhou. Zero-shot 3d drug design by sketching and gener- ating. Advances Neural Information Processing Systems, 11-23894-23990 2022.
Isaac D. Lutz, Shunzhi Wang, Christoffer Norn, Alexis Courbet, Andrew J. Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Jinwei Xu, Elizabeth M. Leaf, Catherine Treichel, Patrisia Litvicov, Zhe Li, Alexander D. Goodson, Paula Livera-Sáncher Ana-Maria Bratovianu, Minkyung Baek, Neil P. King, Hannele Ruohola-Baker and David Baker. Top-down design of protein architectures with reinforcement learning. Science, 380 273, April 2023. doi: 10. 126/science. ence. ad ad6691
adf6591. URI. hhttps: www.:w.wcieeee.. wttp://wwwwwweeeeeee/////ooo///
12

---

Published as a conference paper at ICLR 2025
Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thortto. Cath-a hierarchic classification of protein domain structures. Structure, 5(8):1093- 1109, 1997.
Pedro R Peres-Nete and Donald A Jackson. How well do multivariate data sets match? the advan- tages of procrustean superimposition approach over the mantel test. Oecologia, 129:169-178, 2001.
Arvind Pillai, Abbas Idris, Annika Philomin, Connor Weidle, Rebecca Skotheim, Philip J. Y. Leung, Adam Broerman, Cullen Demakis, Andrew J. Borst, Florian Practorius, and David Baker. De novo design of allosterically switchable protein assemblies. ISSN 0028-0836, 1476- 4687. doi: 10. 17788/41586-004-0788888 0145866024-078111
http:tt:: /www nature. www.ureee....rreeeeeeeeeeeeeeeee awoom/atiiclss///
Florian Praetorius, Philip JY Leung. Maxx H Tessmer, Adam Broerman, Cullen Demakis, Acacia Dishman, Arvind Pillai, Abbas Idris, David Juergens, Justas Dauparas, et al. Design of stimulus- responsive two-state hinge proteins. Science, 381 (6659):544-76, 2023.
Daniel M Rosenbaum, Søren GF Rasmussen, and Brian K Kobilka. The structure and function of g-protein-coupled receptors. Nature, 459(7245):356-336 2009.
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E (n) equivariant graph neural net- works. In International conference on machine learning, pp. 9332.9332. PMLR, 2021.
Ian Sillitoe, Nicola Bordin, Natalie Dawson, Vaishali Waman, Paul Ashford, Harry M Scholes, Camilla SM Pang. Laurel Woodridge, Clemens Rauer, Necladri Sen, et al. Cath: increased struc- tural coverage of functional space. Nucleic acids research, (9(D)):D266-D27, 2021.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learn- ing. pp. 2256-2266 PMLR, 2015.
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXhv:2011.13455 2020.
Michel Van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger. Fast and accurate protein struc- ture search with foldseck. Nature biotechnology, 12(2):243-244 2024.
Gunnar von Heijne. Membrane-protein topology. Nature reviews Molecular cell biology, 7(12): 909-918, 2006.
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, and Quanquan Gu. Protein conformation generation via force-guided se (3) diffusion models. arXiv preprint 1.Xi::2403.14083 2024.
Joseph L. Watson, David Juergens, Nathaniel Bennett, Brian L. Trippe, Jason Yim, Helen E Eise- nach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al. De novo design of protein structure and function with ridiffusion. Nature, 62097976)1089--1000 2023.
David R Westhead, Timothy WF Slidel, Tomas PJ Flores, and Janet M Thorton. Protein structural topology: Automated analysis and diagrammatic representation. Protein Science, 8(4):897-904, 1999.
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al. High-resolution de novo structure prediction from primary sequence. BioRxiv, pp. 2022-07, 2022.
Lianjie Xu and Wen-Bin Bin Zhang. Topology: unique dimension in protein engineering. Science China Chemistry, 61:3-16, 2018.
13

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Jason Yim, Andrew Campbell, Andrew YK Foong, Michael Gastegger, José Jiménez-Luma, Sarah Lewis, Victor Garcia Satorras, Bastiaan Veeling, Regina Barzilay, Tommi Jaakkola, et al. Fast protein backbone generation with se (3) flow matching, arXiv preprint arXfv:2310.05297, 2023a
Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola. Se (3) diffusion model with application to protein backbone generation. arXiv preprint wwx.i::332.022111 2023b.
Mingyang Zhang, Ting Chen, Xun Lu, Xiaobing Lan, Ziqiang Chen, and Shaoyong Lu. G protein- coupled receptors (gpers): advances structures, mechanisms, and drug discovery. Signal Trans- duction and Targeted Therapy, 9(1):88, 2024.
Yuyang Zhang, Zinnia Ma, and Haipeng Gong. Topodiff: Improving protein backbone generation with topology-awane latent encoding. In NeurlPS 2023 Generative Al and Biology (GenBio) Workshop.
Renxin Zhao, Jie Feng, Jie Liu, Wenjie Fu, Xiaoyan Li, and Bing Li. Deciphering of microbial com- munity and antibiotic resistance genes in activated sludge reactors under high selective pressure of different antibiotics. Water Research, 151:388-400 4028 2019.
Zhuoqi Zheng, Bo Zhang, Bozitao Zhong, Kexin Liu, Zhengxin Li, Junjie Zhu, Jinyu Yu, Ting Wei, and Hai-Feng Chen. Scaffold-lab: Critical evaluation and ranking of protein backbone generation methods in unified framework. bioRxiv, pp. 2024-02, 2024.
A TOPOLOGY FITNESS
A.1 CURVE AS TOPOLOGY REPRESENTATION
Given the Ca coordinates X of protein, we define its topology by abstracting its secondary struc- ture elements. For the alpha helix and beta sheet, the Ca coordinates are replaced by series of points along the central axis of that segment. Loop regions retain their original Ca coordinates. A resampling procedure is then applied to reduce the number of coordinates and average the point dis- tance along the curve, yielding the curve coordinates C. The curve coordinates then categorized as a secondary structure by averaging the nearest neighboring Co secondary structure, resulting in a final curve representation Y.
A.2 DEFINITION OF TOPOLOGY FITNESS
To compare the topological structures of two proteins, we introduce Topology Fitness (TF), coarser measure than TM-score. First, the down-sampling transformation is applied to both proteins, gen- erating simplified 3D curves. The curves are then sampled to have the same number of points and aligned using Procrustes analysis. It involves normalizing two curves to achieve optimal overlap by minimizing the Procrustes distance, which quantifies the difference in shape between objects. Using Procrustes superposition, an approach that translates, rotates, and scales the objects, the position and size of the curves can be adjusted maximize their alignment. This process ensures that the curves are as close as possible in both position and scale. Finally, disparsity is calculated, representing the degree of alignment, based the sum of distances between corresponding points on the two curves(Peres- Netoo & Jackson 2001 Zhao et al. 2019).
Mathematically, given two coordinate sets Y, (12,1 32.2. V2,m .22m disparsity is computed via Procrustes analysis:
W1. 31 and Y2
disparsity min || Y Y2RlIrr
where R is orthogonal matrix aligning Y, Y,, and || denotes the Frobenius norm. TF is
written as
TF disparsity.
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A.3 SCTF WITH SCTM AND SCRMSD
The scTF is defined as self-consistency Topology Fitness, measuring the topology fitness between structures and condition curves. Here scTF is computed the TF between designed structures (after refolding) and conditions (scTF_2 __2 Figure 7). From the joint distribution Figure S scT' has close linear relationship with scTM and scRMSD, with positive correlation with the former and negative correlation with the latter. scTF can also be used to measure the designability of curve (whether it describes the topology of a designable backbone). scTFF's value changes more in line with scTM as its length changes, because it is more similar to scTM,, paying more attention to global similarity, is less sensitive to length, and is not strict as scRMSD in long structures.
1.0
1.0
0.3
0.5
96TF
0.4
044
0.4
100
0.0
0.0
0.0
0.50
0.75
1.00
0.6
0.9
1.0
0.9
1.0
60
1.0
1.0
0.8
0.6
0.5
0.2
0.2
0.2
0.0
0.0
Figure 5: scTF scTM and scRMSD. Figures from left to right show the test results datasets HHH ems, med, and GPCR respectively. The first and second rows show the relationship between scTM and scT,, and the relationship between ScRMSD and scTF, respectively. The results have not been filtered by selection. Neurve 50, Nob 10, Noeq = 8.
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A.4
CUTOFF VISUALIZATION
Not similar
Generally similar
Similar
TF
(0,0.7)
(0.7,0.8)
(0.8, 1)
Example
Figure 6: Topology similarity example between two different structures (green and orange).
B CURVE-PROTEIN RESTORE TASK
B. PIPELINE
scTM,scRMSD
Generate Curve
EKERELRVL...
N₂ Backbone
N,.. Sequence
Ground Iruth
Atom
ProtPainter
ProteinMPNN
OmegaFold
ScTF
ScTF 2
Figure 7: Protein Restoration Task and designability test. Using ProtPainter, we sample Nbs back- bones conditioned on curve sampled from ground truth. Then we proceed to sample multiple (Nseg) sequences with ProteinMPNN (Dauparas et al. 2022). Each sequence is then folded with OmegaFold (Wu et al.) 2022) to obtain the predicted backbone, which scored against the sam- pled backbone with RMSD (scRMSD) TM-score (scTM). This framework also gives a method for evaluating topology similarity between structures using scTF. scTF_1 and scTF_2 represent the degree of topology agreement between the generated(before refolding) or designed(after refolding) structures and the condition curve. They have a certain relationship but also distinction. ScTF is always larger than 1.T.. We use scTF.1 in Table T and scTF.2 in other places.
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B.2 DATASET VISUALIZATION
b
Similarity 1
0.8
RCD0fu
Noee
acTF
0.4-
Proifsn
55
64
6.3
MDS
HHH_ams
GPCR
Species
Y MAX DUU
jitt
oper
Ground Truth Dataset
MDS Dimension
Figure 8: Datasets visualization protein space. We calculate scTM between structures and plot
the MDS plot. b. The mean scTF in Figure 3
C CURVE SPACE VISUALIZATION
We visualize the generation results compared to RFDiffusion and ProtPainter space to demonstrate that we exert more detailed control.
REDiffusion
ProtPainter Conditin
Figure 9: Conditional Generation Visualization. We sample 500 backbones conditioned on 1tqg for each method. RFDiffusion is conditioned on sequence length. Protein Generator is conditioned on secondary structure lists. ProtPainter conditioned on the curve. The generated backbones are shown curve space with MDS measured by TF to each other.
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D CURVEENCODER
The CurveEncoder aims to extract spatial geometric features from protein topologies to predict the SSE for consequent sketching and aligning.
D.1 CURVATURE
This process starts with curve interpolation to form chain, effectively describing the shape of curves in computational geometry while preserving local properties. Protein curves are interpolated using splines, providing smooth and continuous representation of the protein backbone.
To capture the geometric characteristics, the curvature defined along the interpolated curves. Cur- vature quantifies how sharply a curve bends at given point, offering insight into the protein 's local geometric features. Curvature xc() is calculated as:
<(t) = [1'(t) x (0)l [F)S
where rt) and (t) are the first and second derivatives of the spline curve with respect to the parameter i, respectively. This formulation captures both the magnitude and direction of bending each point along the curve.
D.2 ARCHITECTURE
Building on this geometrie representation, utilize a combination of EGNN (Equivariant Graph Neural Network) and a curvature-based CNN. For the 3D structural data, each node in the 3D graph has scalar features and contains 3D coordinates. Equivariant graph neural networks are proposed to incorporate geometric symmetry into model building (Han et al. (022). Set Equivariant Graph Convolutional Layer (EGCL) incorporates node embeddings h² = hb 1 coordinate em- beddings x¹ = {=h,- .... 1 edge information & = (Cig), h441, T141 = EGCT[ 31, e). The
message passing and node updates are
maj = de (hh, M1y |u - =2l1 ais)
(17)
o = of C Σ (o) xdt)) (mir))
(18)
$$1
my Σ mij
(19)
6++1 = 6n m.)
(20)
where my are vector messages. de, dr, and dr, are functions commonly approximated by Multilayer Perceptrons (MLPs) for edge or node operations. Edge values aij eúj without additional edge information. Compared with traditional 3D CNNs, geometrically equivariant GNNs do not require voxelization of input data while still maintaining the desirable equivariance.
The model workflow is as follows:
D.3 ABLATION
In order verify the accuracy and generalization of the model prediction, we processed 1000 protein structures in the HHH ems dataset to generate two different granularity datasets with (curve. SSE) pairs. The two datasets are detailed and have no details, where the number of curve points 40% and 120% of the Ca atomic coordinates, respectively. We trained and evaluated models 1 to in Table 6 on these two datasets. Training is done 100 epochs with cross-entropy loss, Adam optimization, and a learning rate of 0.01. Results shown in Figure I0 Method 1, curvature CNN, performs the best in accuracy on the training dataset, but its limited ability to extract spatial information from its curvature results in poor generalization. Method combining the advantages of curvature CNN and EGNN, performs well in both accuracy and generalization. And we select Method 5 in our approach.
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Algorithm 1 Curveenncoder
1: Input: Curve coordinates f0o,
2: x',t< InterpolationSampleaa)
LLennhh of and are N (N > M)
3: xt()) <- r' (f))))"
D Curvature with length N
[1 if - =1
4: eig 0 otherwise
for 0 S i,j N
5: hg < EGCL,(h;-1,
for to to hgg = x(t))
6: <- CNN(K(())
7: Linear( Linear (MuliiHeaddAttentoon/// 1)))
8: Output: o
D Prediction of Secondary Structure
Table 6: CurveEncoders for Ablation
Method
Models
Curvature CNN with kernel 15
GCN connected by chain with 3D-coord feature
EGNN with random feature EGNN with curvature feature EGNN with curvature feature + curvature CNN
Train on Nodetall
Train an Detailed
Valiacionn
Validatton
Accuracy
Accuracy
Figure 10: CurveEncoder Ablation Result.
D.4 TRAIN
We processed 15000 data samples consisting of PDB (Burley al., 2023 Berman al. 2003) and scaffolds, generating three different granularity curve datasets with sampling rates of 40%, 80%, and 120%. Then the curves smoothly interpolated and the true labels masked partially and randomly to enhance the robustness. Then the dataset is split into a training set and test set in the ratio of 8:2. For EGNN, set tokens 100, dim 32, and depth 3. For training, set the learning rate at 0.0001, batch size at 1, and run 2000 epochs with CrossEntropyl Adam optimization, and learning rate of 0.0001.
E SKETCHING
Inspired by (Harteveld et al. 2022), we place predicted SSEs at their respective relative positions as specified by given curve, creating 3D backbone object containing only the SSEs, which we refer to as a naive sketch. For a-helix, we generate the sketch coordinates near the curve such that every 3.6 amino acid residues make turn of the helix, and the upward translation is 0.54mm. Hence, the pitch is 0.54nm, and the distance between two amino acid residues is 0.15mm. We sample coordinates the curve with some distance for loop and f-sheet. The naive sketch contains
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topological information and more detailed structural information, such curve curvature, and a- helical packing information. The naive sketch is aimed to align the curve prompts and generate backbones. Then it is plugged into conditional generation to guide the design.
F COMPARISON
The comparison RFDiffusion in designability and scTM is shown in Figure 11
Designability 1
Confidence
1.0
0.0
80-
pLDDT
0.6
eb-
RFDitusion ProfPsinter
RFDiftusion ProfPainter
0.4
HHH Lmm
med
GPCR
HHH_eme
med
GPCR
Figure 11: Designability and sc TM compared to RFDiffusion.
G OTHER ABLATION
G.1 CONDITION
The condition ablation on protein restore task similarity is shown in Figure 7
Table 7: Condition ablation protein restore task similarity. Metrics are measured without refold- ing. *curve with SSE means that users set the ground truth SSE on curves. scTF scTF.1.
Dataset
Method
Condition
SSE_percent (f)
scTF (f)
RMSD (4)
RFDiffusion
seqlen
0.905+0.059
0.364=0.164
10.453+3.700
HHH_ems
ProtPainter
curve
0.94810.045
0.813=0.115
6.416:10.853
ProtPainter
curve with SSE
0.952+0.045
0.109
6.686++0.979
RFDiffusion
seqlen
0.771:00.088
0.196=0.092
16.077+42.68
med
ProtPainter
curve
0.837+-0.11
0.298
11.174+3.456
ProtPainter
curve with SSE
0.85110.121
0.749=0.217
13.81143.767
RFDiffusion
seqlen curve
0.778+-0.10
0.122-0.034
24.746+
GPCR
ProtPainter
0.761
0.892=0.075
16.63315.516
ProtPainter
curve with SSE
0.794
0.817=0.104
22.597:14.251
20

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Published as a conference paper at ICLR 2025
G.2
USER CONTROLLABILITY TRADEOFF RESULTS
ghublo counRou 2000
PeanacTF
3.
Graun Truis
Condition
0.50
Examc
32
Curee Poanta
Seve
Food Thme
Hallo Gobed Trring
080
Number of Inversso stpps
Figure 12: Ablation. À control tradeoff between diversity and similarity. b. Example samples of different λ. c. Selection method ablation; Score is computed as (scTF + scTM)/2. d. Two-Phase timing ablation.
Users control the generation between diversity and similarity mainly by adjusting the down- sampling factor À between 0 and (not included). If X = O, then it becomes an unconditional generation process. In Figure 12 a. we set Neurve = 0,1 3/4, also written by (1 (1-X) A)- curves of three protein datasets and evaluate by scTF (scTFF in Figure 7 without refolding) be- tween the generated backbones and curves. We find that all three curve datasets formed from existing protein datasets, the similarity increases and diversity decreases when À increases. When = 0, the designs similar to the condition curve are almost but dataset HHH_ems is exception for the folds of short proteins (50-60) are limited. And we set À = 2/3 in the following experiments.
and sample 1, 4, generate backbones conditioned on
50/dataser Nob 5, Nesq
Figure 12 shows the cases how the results vary by A.
G.3 CURVE POINTS
In this study, sample protein with amino acid length of 54 using point counts of 10, 20, 30, 40, 50, and 60 to represent its topological structure. These representations are then utilized conditions for ProtPainter generation. Our findings reveal that the conditions become more detailed, the topological similarity to the original protein increases, with a notable plateau observed between 30 and 40 points. Given this trend, we adopt a sampling rate of 40%. which effectively hints at the protein's 's topology while optimizing the number of representations required.
G.4 TWO-PHASE TIMING
We conduct an ablation study to elucidate the impact of varying (i.e., the number inversion steps) during the Conditional Diffusion steps datasets HHH eems (Neurve 50, No. = 2, Nosq 8). We run our approach on two phases split by fixed timing steps (t = 0, 10, 20, 30, 40, 50) or varying helix-gated gated timing obtain results (sampling from 50 corresponds to the pure noisy latent to t 0 corresponds to the denoised results). We evaluate the result by computing the mean average score (0.5 x scTM 0.5 x scTF) of the generated backbones. The results are shown in Figure 12
21

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Published as a conference paper at ICLR 2025
G.5 HYPERPARAMETER
Table 8: Hyperparameter Ablation.NNarve 50 and Nob = 2 for Protein Restoration Task on dataset HHH ems with selection method by score. scTF_ and scTF are shown in Figure 7 average (scTF_1 scTM + sc_23,,3 and CD is the confident designability (scTM >>
0.5 and pLDDT >
70).
Params
scTF I(f)
scTM((1)
scTF 2(1)
average(T)
CD(T)
7=0, 100.6
0.805
0.746
0.765
0.772
0,58
-=0, 0=0.
0.806
0.716
0.775
0.766
0.54
7=0, y=0.8
0.800
0.733
0.783
0.772
0.59
7=0, .y00.9
0.795
0.705
0.739
0.746
0.49
1=0, 1p=
0.796
0.730
0.783
0.770
0.54
y=0.1, 1/0.66
0.814
0.720
0.777
0.770
0.58
7/0.1,
0.801
0.711
0.753
0.755
0.50
0=0.1,
0.807
0.725
0.765
0.765
0.53
700.9
0.793
0.721
0.749
0.754
0.52
7=0.1.
0.819
0.720
0.768
0.769
0.55
y=0.2, 1=0.6
0.814
0.736
0.788
0.779
0.60
>=0.2, 1/0.7
0.811
0.731
0.804
0.782
0.61
y=0.2, 100.8
0.809
0.707
0.746
0.754
0.49
>=0.2,
0.808
0.721
0.775
0.768
0.58
-=0.2 1|=
0.796
0.696
0.748
0.747
0.49
>=0.3, 000.6
0.803
0.745
0.779
0.774
0.62
>=0.3, m/0.7
0.816
0.727
0.757
0.766
0.51
>=0.3, 00
0.802
0.736
0.766
0.768
0.61
-=033, 009
0.801
0.718
0.753
0.757
0.53
0.3, y=1
0.798
0.676
0.734
0.736
0.48
With A 3. 3, we evaluate hyper parameters 7 ranging from 0.3 and ry from 0.6 to 1, as shown in
Table G.5
We select 0.2 and 17 0.7 to balance between designability and restoration similarity.
G.6 SELECT METHOD
We test the selected method of selecting designed structures by score or scT.. We find that select- ing by score is more suitable in our task, which seeks compromise between controllability and designability. From the edge distribution of the results and the case in Figure 13] corresponding to the proportion of scMM50.5, we chose threshold of scTF>0.8 illustrate the similarity between structure and a condition.
70
18
40
30
577M
5CTM
Figure 13: Selection Method Ablation. The score is computed as (sTTFtscTM)/2
22

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Published as a conference paper at ICLR 2025
H EXAMPLE GALLERY
ID
ground truth
condition
designed example
1p68
7kuw
4db8
2n8i
11j1
1av1:A
014842
P30968
motif scaffolding
1tjl
draw binder
6wpw_RP
Figure 14: Gallery.
23

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Published as a conference paper at ICLR 2025
I ADDING NOISE TO THE RESTORATION TASK
We simulate the user's drawing process by introducing noise, randomly perturbing each point within sphere centered at its original position, with radius (representing noise level) ranging from 0 to 5A. The results, summarized in Table 9 reveal that designability (FD) initially increases before declining. This trend demonstrates the robustness of our method, which tolerate a certain level of user error while accurately interpreting the intended design. The subsequent decrease in FD, coupled with consistently high levels of confident designability (CD), highlights our method's ability to refine the input condition and generate designable and more reasonable outcomes.
Table Adding noise to the curve condition in Protein Restoration Task.
Noise Level
IаااII ems
med
GPCR
CD
FD
CD
FD
CD
FD
0.832
0.654
0.870
0.734
0.936
0.792
0.768
0.602
0.924
0.802
0,902
0.694
0.882
0.540
0.668
0.428
0,510
0.326
3
0.802
0.414
0.432
0.290
0.104
0.062
4
0.714
0.428
0.232
0.102
0.188
0.126
5
0.784
0.392
0.286
0.166
0.298
0.192
ADDITIONAL EXAMPLE GALLERY
condition
ARRAS CARA
result
www
M www
satisfied
X
Figure 15: User cases gallery.
K BETA SHEET EXAMPLES
We have achieved controlling beta sheet percentage by increasing diversity, inspired by RFDiffusion. The cases are presented in Figure 16 The measures are as follows,
1. partial diffusion (lower self-confidence to increase diversity)
2. increasing lambda and decreasing gamma (lower condition and increase diversity)
3. early stop (fewer diffusion steps lower the condition and self-confidence)
24

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Published as a conference paper at ICLR 2025
The cases indicate that it can be extended to beta sheets through sketch features, the same as the helix.
structures (without refolding)
PLDDT
0.9288799
0.9278597
0.9304725
0.7333212
Delix_perctttt_ Beta_perce
0.8858845
0.7687688 0.0570570
0.7777778 0.0870871
0.3663664
0.1051051
Measures
Partial Diffusion,
Increase X, decrease
Figure 16: Beta sheet examples.
25


Reviews:
[{"review_id": "dp8u6o7Zbi", "reviewer": "Reviewer_WjYS", "summary": "This work proposes ProtPainter, a diffusion-based method for generating protein backbones with topological constraints. The proposed CurveEncoder enables sketching, bridging the gap between a conditional curve and backbone modality. The sketch guided sampling based on Helix-Gating scheduling enables efficient feature fusion and retains the most generation ability of RFDiffusion. The method enables powerful topology space navigation through curve-based operations, enriching current protein design methods.", "questions": "1.\tThe main experiments are based on Protein Restoration task. Is it difficult for a general user to draw a reasonable curve?\n2.\tTo draw a good curve requires both drawing skills and knowledge about protein topology, if the drawn curve is not so good (for example, the drawn global topology is not physically stable), will the generation performance drop?\n3.\tDo the authors have some ideas about correcting the draw error mentioned in the the Q2?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe author provides a novel and meaningful task as Curve and topology-based protein design, which offers relatively precise and flexible global topology control. The point is the authors connect curves and SSE, which serves as a blueprint and a bridge between human-draw, maybe not so physically reasonable curves and designable protein backbones\n2.\tThe paper proposes a simple and elegant method for curve-guided protein backbone sampling, including curve sketching and Sketch-guided backbone sampling\n3.\tThis paper presents a simple, training-free sketch-guided backbone sampling method based on interpolation between zt and scaled y (yt-1), without the need for expensive retraining of large protein structure generative models.\n4.\tThe author proposes Helix-Gating to improve the training-free guided sampling performance further.", "weaknesses": "1.\tThe proposed method underperforms when being conditioned on complex interlocking topologies, and this framework has not supported SSE annotation with beta sheets or others so far.\n2.\tFor binder design, ProtPainter just provides an empirical conformation estimation. Further optimization and validation are required.\n3.\tFor users facing real-world tasks, a more user-friendly, responsive interface that allows users to draw or drag proteins with ease.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y3QsDLyueU", "reviewer": "Reviewer_Rep2", "summary": "This paper tackles controllable protein structure generation. Specifically, it proposes a method to extract an editable \"curve representation\" that describes the chain together with some secondary structure annotations from protein backbones. Given that curve, new proteins with a similar topology can be synthesized by conditioning a protein backbone generator on the curve. Modifying the curve representation enables to edit the protein structure in a controllable manner. The curve representation is extracted from given proteins using a CurveEncoder module. The method is validated on protein restoration tasks where the goal is to reconstruct proteins with similar topology as the input proteins from which the curve representation was extracted. Additionally, some editing tasks are shown that topologically and spatially modify given proteins.", "questions": "I do not have any further questions to the authors.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "**Originality**: The proposed method is novel, to the best of my knowledge, building on the conditioning framework from Choi et al. (ILVR). Some related works exist, however, and the method is quite related to DiffTopo.\n\n**Experiments**: The paper successfully demonstrates that the curve representation can encode spatial protein layout and topology and that it can be used to recreate proteins of similar topologies. The qualitative protein editing experiments are also interesting.\n\n**Clarity**: While it is overall possible to generally follow the paper, the paper writing is of poor quality and some things remain somewhat unclear (see weaknesses below).\n\n**Quality and Significance**: Due to the paper's weaknesses (see below), I think the significance and quality of the paper is low.", "weaknesses": "I see several weaknesses:\n\n- The method can only generate alpha helices or coil/loop regions, but not beta sheets. This is a major weakness and severely reduces the method's broad applicability and significance. This is also not discussed in detail and it remains unclear why exactly that is the case. Why can beta sheets not be treated similarly like alpha helices?\n- The only experiment where ProtPainter is compared to existing baselines is in Table 3. However, I think these results are misleading. These baseline models (RFDiffusion, Chroma, and ProteinGenerator) are not conditioned on topological, spatial layouts of the proteins, it seems, as is the case for ProtPainter. Hence, we cannot expect those models to spatially and topologically recreate any given proteins and it is not surprising that those baselines achieve low scTF/FD scores (and for designability [CD], ProtPainter isn't actually outperforming the baselines in all cases). In other words, the baselines are not chosen appropriately.\n  - Chroma has a method to constrain protein generation on shapes. I think it could be appropriate to extract shapes from the input proteins (analogous to the curves) and condition Chroma on those, if possible.\n  - I also think comparisons to TopoDiff and/or DiffTopo would be appropriate, if possible. They may not offer fine editing control, but these methods can also encode protein topologies and spatially reconstruct, I think. These works are actually discussed in the paper and are arguably most related.\n- The designability definition used in this paper relies on pLDDT > 70 and scTM > 0.5. However, most works in the literature these days use a stricter designability definition of scRMSD < 2 Angstrom. I would suggest the authors to use this instead.\n- In Section 3.2, the paper writes that ProtPainter leverages RoseTTAFold to predict $\\hat{z}_0$ (also in figure 1). I would have thought that the authors use the diffusion model RFDiffusion (which is initialized from RoseTTAFold, but itself is not a folding model anymore after training). This also isn't well explained in the very short \"Train\" section in the appendix. It is not entirely clear how the model is trained.\n- The model only shows protein restoration and protein editing tasks, but no de novo generation from scratch. It would be great if the authors could also demonstrate the generation of designable proteins from curves drawn entirely from scratch or generated by another curve generator (the very related TopoDiff and DiffTopo do such things, too).\n- As mentioned previously, the writing quality is generally low, with various grammar errors. This negatively affects the quality of the paper.\n- The ILVR method is cited, but without looking at the Choi paper the ProtPainter method is hard to follow in detail. More details would be helpful and a more critical discussion and motivation of the chosen frame filter operation would be useful, among other things, for example.\n\nIn conclusion, while ProtPainter's curve representation is interesting and the protein editing capabilities could be useful, due to the above weaknesses I do not think that the paper meets the bar for publication.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AgSxJyQrml", "reviewer": "Reviewer_VUTJ", "summary": "This paper presents a conditional diffusion model that can generate proteins corresponding to 3D topological designs provided by the user.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "This paper defines a new task, which is a novel attempt, even though I think the significance and novelty of this task may be questionable. The paper introduces CurveEncoder and Helix-Gating for this task, presenting certain innovativeness in its methodology.", "weaknesses": "1. The definition of this task may be highly similar to the definition of a problem in chroma [1], although not exactly the same. Here, I first point out where chroma mentions the corresponding task. Figure 3 in the main text of chroma indicates that they have implemented shape conditioning to enable geometric molecular programming. Section R of chroma's supplementary materials, titled \"Programmability: Shape,\" also discusses this. My perspective is that while the objectives of this part of chroma and ProtPainter are not perfectly aligned, the desired effects are very similar. Furthermore, it might be possible to simply adjust chroma's Conditioners to transform chroma's Shape Conditioners into topological Conditioners. My question is: How does ProtPainter's topological conditioning differ from or improve upon Chroma's shape conditioning? Are there specific advantages or capabilities that ProtPainter offers that cannot be achieved by adapting Chroma's approach?\n\n\n2. A major difference between ProtPainter and chroma's Shape Conditioner is that ProtPainter can additionally specify the proportion of secondary structures, such as the percentage of Helices. However, I think that in many cases when designing the protein scaffold, we want more helical-like secondary structures, as they can improve the thermal stability of the protein. Therefore, specifying the proportion of Helices is unnecessary. Because we don't need low proportion of Helices. It is only when designing motifs or some flexible regions, or under special circumstances, that one would want to avoid Helices and thus need to specify their proportion. But these special circumstances are usually quite complex, and merely specifying topology may be insufficient and unnecessary. Thus, I do not fully understand the significance of this aspect. Of course, I understand that protein design is a very complex issue, and the scenario I mentioned above might only represent one type of design problem. There may be other types of design problems where the user needs to specify the percentage of helices; however, I do not understand the importance of this issue. The paper does not seem to particularly discuss this matter either. Please have the author indicate whether specifying the percentage of helices is important. If it is important, could you explain why and provide applications along with relevant papers?\n\n\n3. Whether users can provide a correct 3D topological blueprint for successfully generating a protein is questionable. The sketches provided to the diffusion model in this paper are all derived from real proteins. However, in real application scenarios, it is doubtful whether researchers can provide such accurate sketches. If researchers cannot provide very valid sketches, it is uncertain whether ProtPainter can function properly. Moreover, if researchers cannot provide very valid sketches, then chroma's Shape Conditioner might be a more reasonable approach. I suggest that the author provide dedicated experiments to analyze this issue. For example, 1) conduct a user study where a person sketches a protein based on their understanding and professional knowledge of proteins, and then check how successfully ProtPainter can design the protein according to this sketch. 2) In the paper, sketches of proteins are generated from real proteins. During inference, adding noise to the actual sketches to simulate user errors and randomness, see if ProtPainter can still smoothly generate proteins. Additionally, analyze the maximum level of noise that ProtPainter can tolerate.\n\nMy understanding may have some inaccuracies, so please feel free to point them out.\n\n[1] Illuminating protein space with a programmable generative model", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)