Joint Discrete and SE(3) Diffusion for Protein Interface Design

Zhenqiao Song; Jianwen Xie; Lei Li

cs.AIOct 10, 2026

Joint Discrete and SE(3) Diffusion for Protein Interface Design

Authors: Zhenqiao Song, Jianwen Xie, Lei Li

Organizations: Language Technologies Institute, Carnegie Mellon University · Lambda Inc.

Abstract

The design of high-affinity, target-specific protein binders is central to the development of modern therapeutics. However, existing protein-generation methods remain limited in this setting, as many generated designs fail to simultaneously achieve geometric and physicochemical complementarity at binding interfaces. To address this challenge, we introduce PIDiff, a physicochemically enhanced multimodal diffusion framework for protein-protein complex design. PIDiff jointly designs target-conditioned protein sequences and structures by coupling diffusion in discrete sequence space with diffusion on the continuous (SE(3)) structural manifold. At its core, PIDiff employs PIFormer, a novel architecture that interleaves geometry-aware invariant point attention with global sequence self-attention. We further curate PIBench, a protein-protein complex dataset with diverse target scaffolds derived from the Protein Data Bank (PDB). We first pretrain PIDiff on PIBench and subsequently fine-tune it for two therapeutically relevant applications: minibinder design and antibody design. PPFrame consistently outperforms baseline methods across sequence design success rate, structure binding energy, and sequence-structure co-designability on all tasks.

Figures & tables

Figure 1: Overview of the proposed PIDiff architecture. At each diffusion time step, the target protein structure is rendered in green, while the binder protein is shown in pink.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.523	81.61	21.73%	37.90%	73.01%	-45.97	50.00%	50.00%
APM	0.252	77.66	2.17%	31.26%	53.27%	-45.12	23.91%	34.78%
Complexa	0.521	84.61	30.43%	53.77%	82.69%	-56.57	56.52%	54.34%
PIDiff	0.592	87.18	47.83%	33.22%	57.49%	-67.80	65.22%	65.22%

Table 1: Model performance on general protein-protein complex design task. Our PIDiff achieves the best sequence design success rate, structure binding energy and sequence-structure co-designability.

Models	FGFR2 (Seen)			InsulinR (Seen)			PDGFR (Seen)
Models	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )
RFD+IF	36.84%	-42.04	63.15%	56.52%	-35.35	65.21%	3.84%	-59.37	53.84%
APM	29.82%	-37.02	54.38%	43.47%	-31.04	82.61%	0.0	-61.54	61.53%
Complexa	54.38%	-45.81	73.68%	78.26%	-41.32	86.95%	19.23%	-66.56	88.46%
PIDiff	61.40%	-50.87	98.24%	86.95%	-46.92	100%	7.69%	-68.82	30.76%
Models	TGFb (Seen)			VirB8 (Seen)			EGFR (Zero-shot)

Table 2: Model performance on target-protein mini-binder complex design task. Consistency denotes sequence-structure co-designability. Our PIDiff achieves the best average sequence design success rate, structure binding energy and sequence-structure co-designability.

Models	Sequence						Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	H1-Novelty ( $\uparrow$ )	H2-Novelty ( $\uparrow$ )	H3-Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.508	80.05	16.79%	47.30%	56.22%	61.98%	-33.09	59.29%	59.29%
APM	0.256	77.92	6.61%	45.71%	54.17%	64.43%	-26.68	53.57%	52.74%
Complexa	0.515	81.08	16.92%	53.80%	66.26%	71.91%	-32.80	90.26%	92.32%
PIDiff	0.526	81.41	18.77%	52.31%	67.85%	71.93%	-44.46	98.34%	99.39%

Table 3: Model performance on antigen-antibody complex design task. Our PIDiff achieves the best sequence design success rate, structure binding energy and sequence-structure co-designability.

Panel of Figure 2 — Figure 2: Ablation study: (a) - (e), and case study with target protein (f) PDB 1STX.E, (g) InsulinR, (h) HIV-1 (7UCG). Target proteins are shown in blue, the folded structures for the designed binder sequences in green, and the designed binder structures in gray, with light chain in pink for antibody.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

	Complex
Training	602,129
Validation	189
Test	46
Total	602,364

Appendix

Table 4: Data statistics for PIBench.

Models	Seen Class					Zero-Shot
Models	FGFR2	InsulinR	PDGFR	TGFb	VirB8	H3	IL7Ra	EGFR	TrkA	Tie2
Training	465	192	210	77	57	–	–	–	–	–
Validation	57	23	26	9	7	–	–	–	–	–
Test	57	23	26	9	7	38	7	4	4	2
Total	579	238	262	95	71	38	7	4	4	2

Appendix

Table 5: Detailed data statistics for curated target-protein mini-binder complex dataset.

	CDR-H1 Clustering	CDR-H2 Clustering	CDR-H3 Clustering
Complexes	4,261	4,261	4261
Clusters	1,034	1,418	2,254
Training	3,590	3,576	3,427
Validation	403	399	451
Test	268	286	383

Appendix

Table 6: Detailed data statistics for antigen-antibody complex dataset.

	Structure Diversity ( $\uparrow$ )
RFD+ProteinMPNN	0.3097
APM	0.3063
Proteína-Complexa	0.1597
PIDiff	0.3135

Appendix

Table 7: Structure diversity on the general protein-protein complex design task, which is quantified as

1-\mathrm{TM\text{-}score}

averaged across generated candidate structures.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.637	78.92	36.84%	31.97%	68.25%	-42.04	63.15%	63.15%
APM	0.538	76.39	29.82%	29.05%	48.39%	-37.02	71.92%	54.38%
Complexa	0.823	80.67	54.38%	49.25%	76.91%	-45.81	91.22%	73.68%
PIDiff	0.931	82.58	61.40%	41.75%	77.74%	-50.87	100%	98.24%

Appendix

Table 8: Model performance on target-protein mini-binder complex design for target FGFR2. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves the best performance across sequence evaluation, structural evaluation and sequence-structure consistency metrics. Additionally, PIDiff also obtains the highest novelty score. Altogether, the results validate PIDiff is capable of designing high-affinity and novel protein complexes.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.789	80.43	56.52%	30.89%	69.18%	-35.35	65.21%	65.21%
APM	0.562	79.01	43.47%	26.91%	52.91%	-31.04	78.26%	82.61%
Complexa	0.832	86.90	78.26%	41.93%	76.93%	-41.32	100%	86.95%
PIDiff	0.895	93.04	86.95%	27.54%	79.51%	-46.92	100%	100%

Appendix

Table 9: Model performance on target-protein mini-binder complex design for target InsulinR. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves the highest sequence design success rate, structure binding energy and sequence-structure co-designability, validating its ability in simultaneously designing high-quality and consistent protein-protein complex sequences and structures.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.598	76.56	3.84%	28.93%	72.83%	-59.37	53.84%	53.84%
APM	0.391	73.89	0.0	30.27%	52.19%	-61.54	65.38%	61.53%
Complexa	0.652	78.09	19.23%	41.25%	79.42%	-66.56	88.46%	88.46%
PIDiff	0.678	81.73	7.69%	25.78%	82.43%	-68.82	100%	30.76%

Appendix

Table 10: Model performance on target-protein mini-binder complex design for target PDGFR. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves better structure design performance, but lags behind Proteína-Complexa on both sequence design quality and sequence-structure design consistency.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.589	76.90	11.11%	35.17%	68.23%	-31.09	55.56%	55.56%
APM	0.461	72.10	11.11%	32.81%	48.65%	-23.16	77.78%	11.11%
Complexa	0.683	80.15	22.22%	46.17%	76.12%	-46.54	88.89%	33.33%
PIDiff	0.733	78.17	22.22%	48.57%	76.46%	-49.29	100%	22.22%

Appendix

Table 11: Model performance on target-protein mini-binder complex design for target TGFb. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves better performance on both sequence and structure design quality. However, the sequence-structure co-designability of PIDiff is a bit lower than that of Proteína-Complexa.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.570	82.89	14.28%	32.95%	72.91%	-32.90	57.14%	57.14%
APM	0.368	85.09	0.0	30.19%	52.87%	-19.59	71.74%	42.85%
Complexa	0.602	86.10	14.28%	46.52%	80.15%	-38.56	85.71%	85.71%
PIDiff	0.657	86.67	14.28%	36.17%	81.16%	-48.31	100%	100%

Appendix

Table 12: Model performance on target-protein mini-binder complex design for target VirB8. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff obtains better sequence and structure design quality as well as sequence-structure consistency.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.571	83.09	25.00%	29.15%	71.26%	-46.87	25.00%	25.00%
APM	0.512	83.19	0.0	27.93%	51.34%	-32.76	50.00%	50.00%
Complexa	0.627	86.76	0.0	40.16%	76.31%	-40.90	100%	50.00%
PIDiff	0.680	90.19	0.0	22.62%	82.56%	-48.35	100%	100%

Appendix

Table 13: Model performance on target-protein mini-binder complex design for target EGFR. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves better structure binding energy and sequence-structure co-designability. However, the sequence design success rate of PIDiff is zero, which might be due to the lack of deep pockets of EGFR targets and the zero-shot setting of this class.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.782	78.56	52.63%	28.16%	71.04%	-35.93	26.31%	26.31%
APM	0.562	76.29	47.38%	23.91%	53.16%	-36.45	49.87%	42.10%
Complexa	0.891	79.05	60.52%	38.16%	80.47%	-40.65	78.93%	47.36%
PIDiff	0.934	82.32	97.36%	21.97%	83.16%	-47.08	100%	31.57%

Appendix

Table 14: Model performance on target-protein mini-binder complex design for target H3. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff obtains higher sequence design success rate and lower structure binding energy, confirming that PIDiff has the capability to design high-affinity binders even in zero-shot setting.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.543	85.67	14.28%	29.18%	70.19%	-40.08	42.85%	42.85%
APM	0.429	78.09	0.0	27.56%	53.96%	-23.93	85.71%	57.14%
Complexa	0.634	89.90	28.57%	36.17%	80.16%	-42.26	100%	71.42%
PIDiff	0.734	92.21	42.85%	20.35%	82.30%	-50.42	100%	100%

Appendix

Table 15: Model performance on target-protein mini-binder complex design for target IL7Ra. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . PIDiff achieves higher sequence design success rate, lower structure binding energy and higher sequence-structure co-designability. Note that the designability and co-designability of PIDiff on this target are both 100%, validating the strong binder design capability of PIDiff in the zero-shot setting.

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.562	86.90	50.00%	29.31%	72.91%	-38.95	75.00%	75.00%
APM	0.421	82.19	0.0	30.19%	51.43%	-27.85	100%	50.00%
Complexa	0.590	90.10	0.0	39.42%	81.58%	-33.83	100%	50.00%
PIDiff	0.635	92.98	0.0	10.52%	88.20%	-42.39	100%	100%

Appendix

Table 16: Model performance on target-protein mini-binder complex design for target Tie2. (

\uparrow

): the higher the better. (

\downarrow

Models	Sequence					Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	Diversity ( $\uparrow$ )	Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.791	72.08	25.00%	28.17%	70.16%	-33.28	50.00%	50.00%
APM	0.563	73.90	25.00%	31.42%	49.87%	-31.92	75.00%	25.00%
Complexa	0.829	80.10	50.00%	47.92%	78.93%	-41.50	100%	50.00%
PIDiff	0.840	80.31	75.00%	35.49%	78.92%	-46.04	100%	75.00%

Appendix

Table 17: Model performance on target-protein mini-binder complex design for target TrkA. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) . Similarly, PIDiff achieves better sequence design success rate, structure binding energy and sequence-structure consistency, confirming its ability in designing good binders in the zero-shot setting.

Models	Sequence						Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	H1-Novelty ( $\uparrow$ )	H2-Novelty ( $\uparrow$ )	H3-Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.538	80.67	15.67%	49.78%	51.26%	59.31%	-30.03	58.20%	58.20%
APM	0.239	79.23	7.46%	42.18%	53.37%	63.17%	-29.38	57.08%	52.61%
Complexa	0.523	81.09	14.92%	55.36%	68.26%	72.93%	-35.35	88.43%	90.67%
PIDiff	0.548	82.10	17.16%	56.64%	66.46%	74.68%	-42.49	100%	100%

Appendix

Table 18: Model performance on antigen-antibody complex design task for CDR-H1 clustering. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) .

Models	Sequence						Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	H1-Novelty ( $\uparrow$ )	H2-Novelty ( $\uparrow$ )	H3-Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.497	79.29	17.48%	43.87%	58.69%	62.89%	-32.98	58.04%	58.04%
APM	0.271	76.32	5.59%	45.28%	52.97%	63.16%	-28.78	52.98%	53.14%
Complexa	0.519	81.90	17.83%	52.79%	65.23%	70.95%	-30.13	91.25%	93.35%
PIDiff	0.506	80.62	19.58%	49.38%	67.21%	69.61%	-44.37	100%	100%

Appendix

Table 19: Model performance on antigen-antibody complex design task for CDR-H2 clustering. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) .

Models	Sequence						Structure		Co-designability ( $\uparrow$ )
Models	ipTM ( $\uparrow$ )	pLDDT ( $\uparrow$ )	SR ( $\uparrow$ )	H1-Novelty ( $\uparrow$ )	H2-Novelty ( $\uparrow$ )	H3-Novelty ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Designability ( $\uparrow$ )	Co-designability ( $\uparrow$ )
RFD+IF	0.487	80.17	17.23%	48.26%	58.73%	63.75%	-36.25	61.62%	61.62%
APM	0.259	78.23	6.78%	49.67%	56.19%	66.97%	-21.89	50.65%	52.48%
Complexa	0.502	80.26	18.01%	53.26%	65.29%	71.86%	-32.90	91.12%	92.95%
PIDiff	0.525	81.50	19.58%	50.88%	69.85%	71.51%	-46.53	95.03%	98.17%

Appendix

Table 20: Model performance on antigen-antibody complex design task for CDR-H3 clustering. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better. IF denotes the inverse folding model ProteinMPNN ( Dauparas et al., 2022 ) and Complexa denotes Proteína-Complexa ( Didi et al., 2026 ) .

Models	FGFR2 (Seen)			InsulinR (Seen)			PDGFR (Seen)
Models	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )
PIDiff	61.40%	-50.87	98.24%	86.95%	-46.92	100%	7.69%	-68.82	30.76%
w/o finetuning	5.52%	-55.88	100%	8.69%	-44.08	100%	0.0	-72.28	100%
Models	TGFb (Seen)			VirB8 (Seen)			EGFR (Zero-shot)
Models	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )	SR ( $\uparrow$ )	$\Delta$ G ( $\downarrow$ )	Consistency ( $\uparrow$ )
PIDiff	22.22%	-49.29	22.22%	14.28%	-48.31	100%	0.0	-48.35	100.0%

Appendix

Table 21: Pretrained model performance on target-protein mini-binder complex design task. (

\uparrow

): the higher the better. (

\downarrow

): the lower the better.

Protein Binder Design

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Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \textbf{ProtLiD

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