RiboBridge: protein-conditioned de novo RNA binder structure design

Zhou Zhang; Hanqun Cao; Minghao Sun; Odin Zhang; Yang Zhang; Pheng Ann Heng; Wujun Li; Tianfan Fu

cs.LGOct 22, 2026

RiboBridge: protein-conditioned de novo RNA binder structure design

Authors: Zhou Zhang, Hanqun Cao, Minghao Sun, Odin Zhang, Yang Zhang, Pheng Ann Heng, Wujun Li, Tianfan Fu

Organizations: State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University · The Chinese University of Hong Kong · National University of Singapore

Abstract

Designing RNA molecules that bind to target protein structures requires generating both an RNA conformation and its bound complex geometry, without assuming a predefined binding site. Existing approaches generally model these coupled processes within a unified representation, entangling RNA conformation generation with protein-relative placement. In this work, we introduce RiboBridge, a structure-first framework that separates RNA conformational generation from protein--RNA assembly through cross-frame interaction geometry. RiboBridge first proposes multiple candidate RNA-binding interfaces on the protein surface, generates canonical RNA conformations within a pretrained RNA structural latent space (RiboSphere), and then predicts protein--RNA interaction constraints to bridge the independently defined RNA and protein coordinate frames. The resulting interaction geometry is converted into a geometrically consistent protein-relative pose through rigid placement. We evaluate RiboBridge on a temporally split, sequence-filtered, and redundancy-controlled benchmark of 124 protein--RNA complexes. Under post-hoc interface-correspondence evaluation, where native information is used only after generation to select among already generated predicted-pocket candidates, RiboBridge achieves the highest fNAT and DockQ and the lowest interface and ligand RMSDs among compared methods. Component ablations and stage-wise diagnostics further characterize the contributions of RNA conformational generation, interaction prediction, and rigid placement, identifying the main sources of complex-recovery error.

Figures & tables

Figure 1: Overview of the four-stage RiboBridge framework. (A) Interface hypothesis generation. (B) Pocket-conditioned canonical RNA generation. (C) Cross-frame interaction prediction. (D) Interaction-guided rigid placement.

Method	fNAT $\uparrow$	iRMSD $\downarrow$	lRMSD $\downarrow$	DockQ $\uparrow$	DockQ $\geq 0.23$ / 124 $\uparrow$	ClashScore $\downarrow$
ODesign	0.0481	6.275	37.512	0.1156	15 (12.10%)	10.23
RFDpoly	0.0179	6.982	38.297	0.0883	9 (7.26%)	9.66
RNAFlow	0.0074	7.499	47.132	0.0693	7 (5.65%)	18.14
NACraft	0.0148	7.733	44.510	0.0737	10 (8.06%)	35.78
RiboBridge	0.0586	5.356	31.135	0.1308	20 (16.13%)	14.96

Table 1: Complex generation performance on the test set under method-specific conditioning and post-hoc interface correspondence. Each method follows the conditioning supported by its released pipeline; baseline-specific settings are detailed in Appendix B.3 . RMSD values are reported in Å.

Method	Seq. source	Rec. $\uparrow$	scRMSD (Å) $\downarrow$	scLRMSD (Å) $\downarrow$	scTM $\uparrow$	Des. (%) $\uparrow$
ODesign	Direct output	0.4619	10.76	33.91	0.0343	6.45
RFDpoly	NA-MPNN	0.4655	10.90	42.76	0.0638	8.06
RNAFlow	Direct output	0.3781	9.31	48.24	0.0370	6.45
NACraft	Direct output	0.4733	10.69	41.86	0.0682	7.26
RiboBridge	NA-MPNN	0.4683	10.14	40.98	0.0783	8.06

Table 2: Sequence realizability and AlphaFold 3 self-consistency on the 124-target test set. Rec. denotes sequence recovery. Designability is the percentage of all 124 test targets that yield an AF3-valid design with RNA scRMSD below 2 Å.

Absolute performance
Variant	Atom11 RMSD (Å) $\downarrow$	iRMSD (Å) $\downarrow$	fNAT $\uparrow$	DockQ $\uparrow$
Full (pred. matched)	7.972	4.134	0.4584	0.4691
No self-correction	8.054	4.163	0.4500	0.4659
Length-only	9.459	4.984	0.3231	0.3622
Shuffled pocket	9.420	4.798	0.3296	0.3745
Target-paired 95% CI of Full advantage

Table 3: Ablation of pocket-conditioned canonical RNA generation. All variants are evaluated on the same 54-target formally matched subset. The lower block reports target-paired bootstrap 95% confidence intervals for the advantage of the full model, with positive values favoring Full for all metrics.

Figure 2: Error decomposition of RiboBridge. Results are computed on the 54-target diagnostic subset satisfying the formal interface-matching criterion. (a) Stage-wise failure-aware DockQ and pose observability. (b) Paired DockQ changes between consecutive stages. Filled markers include unobservable failures; open markers compare targets that are observable at both stages.

Variant	F1@5 Å $\uparrow$	F1@8 Å $\uparrow$	Dist. MAE (Å) $\downarrow$	Pose RMSD (Å) $\downarrow$	DockQ $\uparrow$
Full	0.272	0.388	3.571	21.92	0.253
$-$ local geometry	0.235	0.331	3.793	22.10	0.241
$-$ triangle reasoning	0.263	0.348	3.683	24.58	0.250
CE-only	0.227	0.353	3.573	24.66	0.246

Table 4: Ablation of cross-frame interaction prediction. Contact F1 and distogram MAE evaluate predictions before rigid placement; pose RMSD and DockQ evaluate the resulting complexes. CE-only removes the explicit 5 Å and 8 Å contact objectives. Pose RMSD and DockQ are computed using the fixed reference RBL solver without nonlinear fallback.

Figure 3: Predicted-pocket-conditioned RNA generation. (A–C) Distinct RNA binding solutions generated from two predicted pockets. (D) Length-conditioned RNA generation at a fixed predicted pocket on a separate target.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Setting	Value
Protein encoder width / GVP layers	$768/4$
Protein graph neighbors	$30$
Interface queries / query-decoder layers	$8/3$
Proposal refinement layers	$2$
Interface probability threshold	$0.60$
Interface support size	$6$ – $128$

Appendix

Table 5: Architecture and inference settings for interface hypothesis generation and canonical RNA generation.

Setting	Value
Per-chain hidden dimension	$128$
Per-chain message-passing layers	$4$
Triangle reasoning stages	$20$
Protein context anchors	at most $16$
Local shape neighborhood size	$8$
Distogram classes / maximum finite range	$37/20$ Å

Appendix

Table 6: Architecture and inference settings for cross-frame interaction modeling and rigid placement.

Method	Protein input	Site information	RNA information	Candidate selection
ODesign	Structure	Native-derived hotspot	Requested length	Single output
RFDpoly	Structure	No explicit hotspot	Requested length	Single output
RNAFlow	Structure crop	Native-RNA-guided crop	Requested length	Single output
NACraft	Sequence	No explicit hotspot	Requested length	Highest model confidence
RiboBridge	Structure	Predicted interface	Requested length	Matched hypothesis

Appendix

Table 7: Conditioning information used by each method in the main comparison. For RiboBridge, “Matched hypothesis” means that predicted-interface candidates are generated independently before a correspondence with the native interface is established for evaluation.

Method	RNA sequence source
ODesign	Direct model output
RFDpoly	NA-MPNN
RNAFlow	Direct model output
NACraft	Direct model output
RiboBridge	NA-MPNN

Appendix

Table 8: RNA sequence sources used in the AlphaFold 3 self-consistency evaluation.

Quantity	Value
Raw learned queries / target	8
Test targets	124
Total retained proposals	202
Mean proposals / target	1.629
Median proposals / target	2
Range	1–3

Appendix

Table 9: Retained interface hypotheses in the blind 124-target evaluation.

Interface budget	Matched targets	Recall
Top-1	45/124	36.29%
Top-2	53/124	42.74%
All retained (Top-3)	54/124	43.55%

Appendix

Table 10: Formal native-interface coverage as the retained proposal budget increases.

Selection	Mean Dice	Median Dice	Median center dist. (Å)
Rank-1 proposal	0.503	0.619	10.72
Best-overlap retained proposal	0.573	0.647	8.42
Formal matched proposal	0.835	0.862	2.34

Appendix

Table 11: Correspondence between predicted interfaces and the expanded native interaction region.

Stage	RNA source	Interaction	Placement	Oracle input
Native/native	Native	Native	Native	Full complex
GT-token recon.	GT-token recon.	–	Oracle pose	Tokens + pose
Generated canonical	Generated	–	Oracle pose	Pose
Exact dist. + RBL	Generated	Exact distances	RBL	Distances
Oracle 37-bin + RBL	Generated	Oracle bins	RBL	Distance bins
Predicted map	Generated	Predicted	RBL	None

Appendix

Table 12: Stages used for error decomposition. Consecutive stages replace one source of oracle information with its predicted counterpart.

Stage	Obs.	DockQ	fNAT	iRMSD	lRMSD	Pose RMSD (mean/med.)
Native/native	54/54	1.000	1.000	$\approx 0$	0	0/0
GT-token recon.	54/54	0.854	0.834	0.858	1.319	0/0
Generated RNA	54/54	0.464	0.461	4.183	7.728	0/0
Exact dist. + RBL	53/54	0.446	0.441	4.261	7.804	0.011/0.007
Oracle bins + RBL	53/54	0.445	0.453	4.261	8.217	0.888/0.100
Predicted map	44/54	0.253	0.232	4.206	22.869	21.920/21.021

Appendix

Table 13: Stage-wise diagnostics on the 54-target formally matched subset. DockQ is failure-aware; all RMSDs are in Å.

Transition	Isolated source	$\Delta$ DockQ loss (95% CI)	$\Delta$ observable
Native $\rightarrow$ GT-token recon.	RiboSphere reconstruction	0.1465 $[0.1193,0.1764]$	0
GT-token $\rightarrow$ generated canonical	Canonical generation	0.3893 $[0.3290,0.4487]$	0
Oracle pose $\rightarrow$ exact-distance RBL	Rigid placement	0.0181 $[0.0000,0.0537]$	$-1$
Exact distance $\rightarrow$ oracle 37-bin	Distance discretization	0.0016 $[-0.0056,0.0101]$	0
Oracle 37-bin $\rightarrow$ predicted map	Interaction prediction	0.1920 $[0.1502,0.2335]$	$-9$
Reference RBL $\rightarrow$ fallback	Nonlinear recovery	$-0.0018$ $[-0.0043,0.0000]$	$+3$

Appendix

Table 14: Target-paired changes between consecutive stages. Positive

\Delta

DockQ denotes degradation from the upstream to downstream stage.

Figure 4: Qualitative examples of complex recovery and conformation–placement errors. (a–c) High-recovery complexes. (d–f) Examples with different degrees of RNA conformational and protein-relative pose deviation. Generated RNA is purple, native RNA is gray, and the local protein environment is pale green. RMSD measures RNA conformational deviation after alignment; placement error measures deviation of the recovered pose without an additional RNA superposition. DockQ evaluates recovery of the native complex.

Component	Trainable params.	Hardware	Training budget	Training time
Interface predictor	102.66M	8 GPUs	20 epochs	$\sim$ 4 h
Canonical generator	38.5M	8 GPUs	40 epochs	$\sim$ 4 h
Interaction predictor	13.3M	8 GPUs	50 epochs	$\sim$ 13 h

Appendix

Table 15: Training cost of the three trainable components in RiboBridge.

Quantity	Runtime
Full 124-target run	1872.8 s
Mean / target	15.10 s
Median / target	11.01 s
10th percentile	6.06 s
90th percentile	28.33 s
Median, 1 retained pocket	6.47 s

Appendix

Table 16: End-to-end inference cost on the blind 124-target evaluation. Runtime includes pocket prediction and downstream generation for all retained interface hypotheses.

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RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-design via geometry-latent flow matching. La-Ribo retains a sparse phosphate-sugar--base scaffold and encodes nucleotide identity and local conformation in residue-wise latents. A shared flow network generates both jointly, and an RNA-specific decoder then reconstructs all heavy atoms. To expand supervision, we construct a quality-controlled corpus of 168,561 RNA structures, integrating experimental data with predictions from three folding models, including 10,631 MSA-supported structures generated in this work. La-Ribo improves designability and codesignability over the evaluated baselines across sampling budgets and two refolding models, and the same prior supports scaffold-conditioned inverse folding without additional training.

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