FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling
Authors: Jianwei Zhu
Yu Shi
Ran Bi
Peiran Jin
Chang Liu
Zhe Zhang
Haitao Huang
Zekun Guo
Pipi Hu
Fusong Ju
Lin Huang
Xinwei Tai
Chenao Li
Kaiyuan Gao
Xinran Wei
Huanhuan Xia
Jia Zhang
Yaosen Min
Zun Wang
Yusong Wang
Liang He
Haiguang Liu
Tao Qin
Venue: ICLR 2026 Poster (2026)
Keywords: Protein Design
Protein Foundation Model
Diffusion
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Protein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-based language models (e.g., ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their reliance on homologous sequences makes them less reliable in highly mutated or alignment-sparse regimes. We present FlexRibbon, a pretrained protein model that jointly learns from amino acid sequences and three-dimensional structures. Our pretraining strategy combines masked language modeling with diffusion-based denoising, enabling bidirectional sequence-structure learning without requiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning interface design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes new state-of-the-art performance on 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based methods often struggle.

Published as a conference paper at ICLR 2026
FLEXRIBBON: JOINT SEQUENCE AND STRUCTURE PRETRAINING FOR PROTEIN MODELING
Jianwei Zhu' 1+, Yu Shi Ran Bi Peiran Jin linl:-t, Chang Liu 1+.i, Zhe Zhang' Haitao Huang 1.2 Zekun Guo', Pipi Hu², Fusong Ju', Lin Huang", Xinwei Tai Chenao Li Kaiyuan Gao", Xinran Wei', Huanhuan Xia', Jia Zhang', Yaosen Min', Zun Wang", Yusong Wang', Liang He', Haiguang Liu Tao Qin *Zhongguancun Academy, Beijing, China Hunan University, Changsha, China.
BBiijin Institute of Mathematical Sciences and Applications. Beijing. China. *Ubiquant, Beijing, China.
Huuzhong University of Science and Technology, Wuhan, China.
c Institute of Biophysics, Chinese Academy Sciences, Beijing. China.
"Shanghai Artificial Intelligence Laboratory, Shanghai, China.
ABSTRACT
Protein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence- based language models (e.g.. ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their re- liance on homologous sequences makes them less reliable in highly mutated alignment-sparse regimes. We present FlexRibbon", pretrained protein model that jointly learns from amino acid sequences and diree-dimensiona structures. Our pretraining strategy combines masked language modeling with diffusion- based denoising, enabling bidirectional sequence-structuur learning without re- quiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning inter- face design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes state-of-the-an performance 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based meth- ods often struggle.
INTRODUCTION
Proteins are fundamental to nearly all biological processes, and modeling their sequences, structures, and functions underpins biomedical and biotechnological advances ranging from enzyme engineer- ing to therapeutic antibody design. Recently, protein foundation models (PFMs) have emerged as a unifying framework that leverages large-scale data and deep learning to capture the principles of protein biology, offering new opportunities for both understanding and design. The development of PFMs has followed two main trajectories. One line of work builds on sequence-only language models (PLMs) such as ESM-2 (Lin et al. 2023 and ProtT5 (Pokharel et al.) 2022), which lever- age large corpora of protein sequences to learn universal embeddings. These models are broadly applicable and computationally efficient, but the lack of physical relevance, particularly informa- tion about three-dimensional geometry, limits their ability to capture the structural basis of pro- tein function. Several subsequent efforts enrich sequence models with structural cues-such as injecting residue-level geometric features (Bepler & Berger 2019). incorporating template-basee or graph- structural information (Heinzinger et al. 2024). distilling representations from struc- ture predictors into sequence encoders (Ouyang-Zhang et al. 2025 Zhang et al. 2024), or perform
'These authors contributed equally.
'Coressonding authors: fjinpeiran, liuchang, liuhaiguang, qintao @ @hza.edu.cn *oode is available https:://// / / thub. ttm..ccmm/bjjcccaaaccccc / bbon

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cnncuure-conditionne sequence design (Zhnng et al. 2023). These approaches improve structural awareness but remain fundamentally sequence-centric structure is used as an auxiliary signal rather than being jointly modeled, and atomic-level geometry is not learned generatively or bidirectionally.
Another line is represented by multiple sequence alignment (MSA) based structure predictors, exem- plified by AlphaFold 2/3 (Jumper al.; 202T Abramson et al. 2024). which exploit evolutionary couplings encoded in MSAs to achieve striking accuracy in structure prediction. Yet, this depen- dence on homologous sequences introduces sensitivity: when alignments are shallow, sparse, or disrupted by extensive mutation, the predictive signal degrades. As a result, critical scenarios such as antibody CDR loops, intrinsically disordered interfaces, and rapidly evolving pathogens remain inadequately addressed by either paradigm; in these settings, single-sequence models that bypass MSAs and directly model individual sequences provide a more faithful way to capture flexible and highly mutated regions where alignment signals are weak.
We introduce FlexRibbon, a 3-billion partmmeer pretrained protein model that learns directly from amino acid sequences and large-scale structural corpora, including experimentally resolved struc- tures (Berman et al.) 2000) and AlphaFood 2 predicted structures (Varadi et al., 2024). Unlike sequence-only models or predictors that impose one-way sequence-fo-structure mapping, FlexRib- bon integrates sequence and structure signals from the outset: each residue is represented by a single embedding that combines sequence identity with structural context. The training strategy couples masked language modeling on sequences with diffusion- based denoising on structures, enabling the model to capture bidirectional sequence-structure dependencies and support full-atom structure generation. To address the variable confidence of predicted structures, we introduce an adaptive loss that selectively weights low-confidence regions, extracting useful signal while avoiding overfitting to unreliable geometry. Previous joint models were designed primarily for structure prediction, but the high memory cost of full-atom representations made the structural component difficult to scale, so most parameters ended up concentrated on the sequence side. FlexRibbon overcomes this limita- tion with hierarchical modeling strategy that allocates scalable capacity across both sequence and structure, allowing efficient large-scale structural learning alongside sequence semantics.
We systematically evaluate FlexRibbon across three broad task families: (i) flexible interface predic- tion and design, such antibody/nanooody CDR modeling and peptide binding; (ii) intermolecular conformational
interaction prediction, including protein-ligand docking prediction, change, and protein-ligant affinity prediction; and (iii) protein function prediction, such gene ontology and enzyme activity. Across these categories, FlexRibbon achieves state-of-the-arr perfor- mance, with especially strong improvements in mutation-rich settings where MSA-based methods often struggle. Beyond outperforming existing models, our results highlight the consistent advan- tages of joint sequence-structtue pretraining. The key contributions are:
Proposing a novel pretraining strategy for FlexRibbon that unifies protein structure prediction and design by combining masked language modeling with diffusion- -baeed denoising, thereby learning a bidirectional sequence-structure mapping rather than a one-way eqquence-t--strucuue mapping.
Introducing a hierarchical modeling strategy that balances scalable capacity across sequence and structure representations, overcoming the memory bottlenecks of full-atom models and enabling structural representations to scale effectively.
Showing that FlexRibbon enables co-design of protein sequence and structure, delivering substan- tial improvements on flexible and highly mutated regions such as antibody/nanobody CDR loops and ceptide-binding interfaces, where MSA-based models struggle.
Demonstrating consistent gains across 12 tasks spanning flexible interface modeling, intermolecu- lar interactions, and protein function prediction, showing that sequence-structuue pretraining trans- fers broadly beyond protein folding.
RELATED WORKS
Protein foundation models. PFMs learn transferable protein representations for diverse tasks. Early PFMs were sequence-only language models such as ESM-1b/ESM-2 (Rives et al.) 2021: Lin ct al. 2023) and ProtT5 (Pokharel et al. (022). trained on large sequence corpora but limited by the absence of geometric priors. A number of follow-up efforts investigates how structural knowledge
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inform sequence models, exploring ideas such as learning embeddings shaped by structural sim- ilarity (Bepler & Berger 2019), leveraging template-base or topology-awart encoders (Heinzinger et al. 2024). transterring internal states from structure prediction networks (Ouyang- Zhang al. Zhang et 2024), and designing sequences conditioned on known folds (Zheng et al. 2023)
2025
In contrast, structure-centrn PFMs such as AlphaFold2/3 (Jumper al.] 2021: Abramson et al, 2024) leverage MSA and templates to achieve high-accuracy folding, yet degrade in highly mutated or low-homology regions. More recently, PFMs have moved toward multimodal, structure-aware pretraining. ESM-3 (Hayes et al. 2025) unifies sequence, structure, and function in frontier gen- erative model. DPLM-2 (Wang et 2025b) extends diffusion protein language models (PLMs) to jointly model both sequences and structures via structure tokenization.
Antibody design. Antibody design methods can be broadly categorized into sequence-basee based and
structure-bard based approaches. On the sequence side, general pprrpose PLMs such as ProtBert (EInag-
gar et al. 2021) provide strong baselines for paratope prediction, mutation recovery, and antibody li-
brary generation. More specialized pretraining frameworks such as SFM-Protein (He et al. (024) in-
troduce masked language modeling with pairwise and span-level objectives, showing improved per- formance on CDR-H3 benchmarks. In addition, graph neural network methods like ABGNN (Gao et al., 2023) and RefineGNN (Jin et al. 2022) attempt to couple sequence embeddings with lo- cal structural context, while Knowledge-driver frameworks such as RosettaAntihodyyDern
(Adolf- Bryfogle et al. 2018) remain widely used in practice. On the structure side, diffusion based models such DiffAb (Luo al. 2022) generate CDR loops conditioned on antigen structures, enabling co-design of sequence and structure, while dyMEAN (Kong et al. 2023b) and MEAN (Kong et al., 2023a) extend this direction with equivvaiant architectures for full-aton design. More recently, IgGM (Wang et al. (2025a) expands design capabilities to antibodies and nanobodies by producing antigen-specific complexes. Together, these approaches demonstrate the promise of combining se- quence information and structural priors for flexible and functional antibody design.
3 METHODS
3.1 DIFFUSION PRETRAINING
We employ diffusion modeling as generative pretraining objective for protein structures. A struc- ture R E RRN is represented by the 3D coordinates of all heavy atoms. Following Karras et al. (0022) (also adopted by AlphaFold 3), we connect the data distribution p(R)) with Gaussian noise Pere through a variance-explodingg process (Song et al. (0021):
R = Ro + o,e, (O,
where increases with time t. Sampling amounts to reversing this process, which requires learn- ing the score function V VlogP: Pt. We approximate with a neural network 8 (R, t), equivalently parameterized as:
Do - of
R, t)
(1)
where Dy (R, denotes model output. The effective learning objective amounts to denoising loss:
min W, Ep(Ro) RR) Dg (R,,
Ro
with a time-step sampler p(t) and weight wy. A key challenge is ensuring invariance to rigid- body transformations. We remove translational freedom by centering structures on the center of mass, and enforce rotational invariance by augmenting data with random SO(3) rotations, instead of relying on heavy S0(3) -qqiivaanta architectures (Köhler et al. (2000) that may also introduce undesired reflection symmetry. We also found that alignment-basee ct al. 2024) did not improve training stability in our settings but added the risk of improper sam- pling WWohwwend et al. 2025). Further details are provided in Appendix C.6
objectives (Xu et al.] [022] Abramson
3.2 ARCHITECTURE
Our architecture is organized in three stages: sequence module, coarse-graines structure module, and all-atom structure module (Fig. and Fig. 77. This balances efficiency and expressivity: coarse-
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bond foa
20 molccale topology
R2
restditructure
Sequence Module
Coanse greined Structure Module
All-atam Structure Module
Dg(R,
R
RMSE
Training Pipeline
R
R..
sig step
Sampling Pracedure
Deet pereeie too
Reeible inceraaee prediction
Internlleuuar Interaction predictoo
Protels function presittion
Figure I: FlexRibbon framework. The model architecture consists of three modules: (i) a sequence module that encodes masked protein sequences and ligand topologies, (ii) coarse-grained structure module that encodes residue-level structural information, and (iii) an all-atom structure module that refines these representations into chemically consistent coordinates. The framework combines diffusion-basse denoising with sequence recovery. enabling joint alignment of sequence, residue, and atomic representations for complex modeling. Downstream tasks, including antibody/nanooody design, intermolecular interactions modeling and protein function prediction, are supported.
grained modeling captures global protein-ligand organization, while the all-atom stage ensures fine- grained structural accuracy. Table shows the architectural hyperparameters.
Sequence Module. The sequence module jointly embeds protein residues and small-molecule atoms into a unified representation space. For protein residues, we apply a standard Transformer encoder (Appendix B with rotary position embeddings (Su al. 2023). focusing purely sequence- derived semantics. To incorporate covalent-bone information of small molecules, we apply a small MLP to the atom-type embeddings produce a 2D bond feature matrix. This learned projection enables the model to recover the covalent bonding pattern from atom identities through a single MLP layer, rather than relying handcrafted bond encodings. The combined representations de- fine a residue-atom graph, which is further refined by pair-feature update module that models residue-residue and residue-atom interactions.
Coarse-grained Structure Module. The coarse-grained structure module employs a Diffusion Transformer (DiT) (Peebles & Xie 2023) denoise coordinates at residue level for proteins and atom level for small molecules. Each residue is represented as a coarse structural anchor, while each ligand atom represented by a position embedding derived from its noised coordinates. The module conditions on embeddings from the sequence module to guide denoising.
All-atom Structure Module. The all-atom structure module employs DiT where each atom of proteins is represented explicitly. Noised 3D coordinates of all atoms are encoded into position features that serve as token inputs. The coarse-grained outputs are broadcast to all atoms of each residue, providing residue-level guidance conditional input. To preserve chemical validity, learn- able attention biases are added to atom pairs connected by covalent bonds, combining atom-type and bond-type embeddings additive bias terms in the attention map. This refinement stage allows the model to reconcile global residue-level context with detailed atomic-level interactions, yielding chemically consistent and high-resolution structures.
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3.3 PERUCTURE-INFORMED MASKED LANGUAGE MODEL (SIMLM)
Masked language modeling (MLM) (Kenton & Toutanova 2019 Lin et al. 2023 has proven effec- tive for predicting masked amino acids in protein sequences. In the spirit of unifying sequence and structure, the masked positions should be inferred from correlations within the surrounding sequence and reflect the structural context that these residues possess. To realize this principle, we extend MLM beyond sequence-only inputs by integrating diffusion- noise into structural representa- tions, yielding a structure-informed masked language model (SIMLM). This formulation couples sequence recovery with structural denoising, thereby reinforcing the mapping between amino acid identity and three-dimensional conformation.
Concretely, we integrate MLM and diffusion through three complementary training modes. Mode Seqquenceto-SSruucuur standard diffusion-basse structure reconstruction, where clean sequences condition the generation of noisy structures. Mode 2 (Coupled Perturbation): for 15% of residues selected at random, mask the amino acid type and add diffusion noise to their local structures, while leaving all other tokens and structures unperturbed. Mode 3 (Sequence-Masked Global Perturbation): randomly select I 15% of residues for type masking, while applying diffusion noise to the structures of all residues.
Through these modes, the model alternates between one-way mapping, localized joint perturbation, and global perturbation, which together encourage robust learning of the bidirectional relationship between protein sequences and structures. These allow the model to capture not only sequence- level regularities but also the structural constraints and variability that underlie protein evolution and function. More details are provided in Appendix C.3
3.4 TRAINING AND SAMPLING
Loss function. Our training objective integrates four complementary components to balance coor- dinate accuracy, sequence recovery, and structural plausibility. The overall loss is defined
C = LSSE + LMLM + Dist + Lsmooth-IDDT
Here, CSE denotes the diffusion pretraining loss function given in Eq. 2 LVEM improves sequence-level representation through masked residue prediction, LDia regularizes predicted inter- residue distances to maintain realistic tertiary structure geometry, and S-mootti-DD aligns training with widely used structure quality metries by emphasizing local geometric accuracy.
Training. Pretraining is organized into two progressive stages. Stage A optimizes all components except LMLM, training proteins with up to 384 residues. Deferring MLM at this stage avoids the instability that arises when it is introduced too early, while the residue cap improves efficiency and helps the model prioritize learning core structural regularities. Stage B expands the input length to 768 residues and incorporates LMLM, enabling stable joint optimization of sequence and structure on larger scales. In both stages, include confidence-weightee diffusion loss that scales residue- level contributions by pI.DD'-derrved sigmoid weights, reducing noise from low-confidence re- gions while emphasizing reliable structural signals. Stage C further extends the maximum sequence length to 1024 residues and introduces training of the confidence head, allowing the model to learn calibrated residue-level uncertainty estimates alongside structure generation. By jointly optimizing structural, sequence, and confidence objectives at full sequence scale, Stage C consolidates earlier- stage representations and equips the model with robust long-range reasoning ability and reliable self-assessment. More details are provided in Appendix C.5
Sampling. The sampling procedure is the simulation of the reverse process. By leveraging the relation of the denoising model to the score model in Eq. (I. we have:
Ro ~ Pare = OZ)
R = R Do (R, t) R
Or-F
We follow similar modifications as used AlphaFold 3 (Abramson et al. 2024 Karras et al. 2022), but forgo applying the random rotation at each sampling step orientation aligmment is not used in the loss function. Hence, the model learns the correct output orientation relative to the input. The detailed sampling algorithm presented in Appendix C.7
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Antlbody
Nanobody
Poptide
60
Figure 2: Success rates of structure prediction for antibodies, nanobodies, and peptides. tFold- Ag and AlphaFold take MSA information as input. IgGM, dyMEAN, and FlexRibbon leverage antigen structural information. All methods, except AlphaFold 3, additionally incorporate epitope information. Results except FlexRibbon are taken from Wang et al. (2025a).
4 EXPERIMENTS
We entries from the AlphaFold Protein Structure Database (AFDB, CC-BY 4.0 License) (Varadi et al. 2024) and the Protein Data Bank (PDB, CCO 1.0 License) (Berman et al.) 2000) released on or before 2021-09-30 for pretraining (Appendix A). For downstream evaluation,
consider three major task families: (i) Flexible interface prediction and design, spanning five tasks involving anigen-anntbodyy.
and mooeinn-ppptice complexes; (ii) Intermolecular interac- tion prediction, including three tasks centered on protein-ligand binding; and (iii) Protein function prediction, comprising four tasks focused on functional annotation. addition, we employ confi- dence head that predicts PLDDT and PAE; following AlphaFold 3, we compute pTT directly from the predicted PAE to provide a consistent estimate of global structural accuracy.
4.1 FLEXIBLE INTERFACE PREDICTION AND DESIGN
Biomolecules with flexible binding interfaces are difficult model and design (Wu et al. 2025 Antibodies, nanobodies, and peptides are key examples, as their functions depend on flexible bind- ing (Wu et al., 2023). This flexibility allows them target diverse molecules, but also makes structure prediction challenging. To study this problem, we introduce tasks on flexible interface modeling, including aatigen--ntiboue
antigen-nanobody. and protein- ppeetid complexes. Each task is defined as: given the sequence or structure of the components, predict the structure of the com- plex. To avoid overlap, protein chains in the test sets share most 40% sequence identity with the training data. We focus on two tasks: interface structure prediction and interface design.
Antibody and nanobody interface prediction is a cornerstone of structural immunology. as ac- curate modeling underpins antibody discovery and therapeutic engineering. Nanobodies can be regarded as single-domain antibodies derived from VHH fragments (Harmsen De Haard 2007), allowing both classes to be modeled within shared framework. For this task, we follow the eval- uation procedure of IgGM (Wang et al., 2025a). measuring performance by the success rate (SR) based on the DockQ (Mirabello & Wallner 2024) score, with threshold of DockQ > 0.23. Exper- iments are conducted on the SAb23H2 test set from IgGM, where we compare Flex against the structure prediction models AlphaFold and tFold-Ag (Wu al. 2024a), as well as the anti- body design methods dyMEAN (Kong et al. (023b) and IgGM. Following the IgGM protocol, we predict antigen-antin -nntibody (-nanobody) complex structures given the antigen sequence and antibody (nanobody) sequence. As shown in Fig. 2 FlexRibbon achieves success rates of 61.3% for antigen- antibody and 51.1% for antigen- nanobody complexes, yielding absolute improvements of 14.6% and 7.1% over IgGM, respectively. These results demonstrate that FlexRibbon effectively models intigen-antibody and aatigen-nanobodd interactions (Tables and g).
antigen-nanobody
Protein-pepticn interface prediction is another important scenario, as peptides often act as recog- nition motifs or regulators for diverse cellular processes. We use FoldBench (Xu et al. (025) as the benchmark. We follow the FoldBench evaluation protocol, also reporting the success rate based on DockQ. FlexRibbon is compared with the structure prediction model AlphaFold and the peptide design method PepGLAD (Kong al.) 2025). As shown in Fig. 2 FlexRibbon achieves SR of 91.4%, exceeding AlphaFold and PepGLAD by 7.0% and 10.2%, respectively. These findings suggest that FlexRibbon generalizes well to flexible peptide-protein binding scenarios (Table

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Table 1: Metrics for antibody and nanobody design. Both IgGM and FlexRibbon leverage antigen
structure and epitope information. All baseline results are taken from Wang et al. (2025a)
Antibody
Nanobody
Method
H3-AAR
DockQ
SR
H3-AAR DockQ
SR
dyMean
0.294
0.079
0.049
DiffAb (AF3)
0.226
0.208
0.368
0.156
0.211
0.346
IgGM
0.360
0.246
0.433
0.183
0.267
0.415
FlexRibbon
0.414
0.273
0.460
0.218
0.244
0.437
Figure 3: Predicted structures for (left) SARS-CoV-2 RBD with the Re30H02 nanobody (or our design) and (right) SARS-CoVas Omicron BA.4 RBD along with its antibody (or our design). Na- tive structures are shown in grey, while those for sequences generated by FlexRibbon are in colour. The insets show close-up views of the interaction region, with dashed lines indicating presumed hydrogen bonds. For SARS-CoV-2 RBD (left), the native and designed sequences are structurally similar, with our design manifesting one additional hydrogen bond, whereas for SARS-CoV-2 Omi- cron BA.4 RBD (right), the predicted structures differ significantly in the interaction region, with our design yielding a greater number of hydrogen bonds. These showcases demonstrate the ability of FlexRibbon to generate sequences that structurally sound.
Antibody and nanobody design is a key challenge in developing novel binders for therapeutic and diagnostic applications. In this task. the input is the antigen sequence and structure, and the objective is to design sequences for all antibody/nanobody CDR regions while jointly generating the full complex structure. We evaluate FlexRibbon on the SAb23H2 test set from IgGM, following the same protocol. Performance is measured by amino acid recovery (AAR), DockQ, and success rate (SR, defined as the proportion samples which have DockQ 0.23) relative to wild-type complexes. Baselines include dyMEAN, DiffAb (Luo et al. 2022). and lgGM. As shown in Table FlexRibbon achieves 41.4% AAR and 46.0% SR for antibody design, surpassing IgGM. For nanobody design, FlcxRibbon also slightly outperforms IgGM, with higher AAR (21.8%) and SR (43.7%). In addition, FlexRibbon supports user-specified CDR lengths, enabling flexible design. Figure 3 Uillustrates some representative designs: specifically for the highly flexible CDR-H3 region, which is also the most challenging to design. Detailed results provided in Appendix D Together, these results show that FlexRibbon can generate realistic CDR sequences while maintaining structural fidelity to wild-type complexes.
framework generates up six CDRs simultaneously. with AAR reported
4.2 INTERMOLECULAR INTERACTION PREDICTION
Protein-ligand interactions are fundamental in understanding protein conformational changes, bind- ing affinities, and diverse biological functions. The accurate prediction of these interactions is thus crucial for elucidating molecular mechanisms and accelerating drug discovery. We evaluate FlexRibbon three downstream tasks: protein-ligant docking prediction, ligand-induces confor- mational changes and protein-ligand binding affinity, which sharing a common foundation mod- eling protein-ligand complexes, while emphasizing different aspects of interaction.

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100
MSA-based
single-sequue sequcee
uence
80
60
40
20
AlphaFold Chai-1
Protenix
Dirlbcck
DifDock-L.
Reekibbhnn ReaRibbon
top-1
rendom-1
Figure 4: Evaluation of protein-ligand docking on the PoseBusters VI benchmark. The methods are separated into (left) MSA-based and (right) single-sequenee groups. The success rate is defined as percentage of predictions with pocket-aligned ligand RMSD <2 Å. Apart from DiffDock and DiffDock-L,, which predict the ligand pose with the protein structure given, all other methods jointly generate the structure of the protein-ligame complex. Results for AlphaFold 3 are taken from from Jumper et al. (2021). Chai-l from Chai Discovery (2024), DifiDDok from Corso et al. (2023), and DiffDock- from Corso et al. (2024). Results for Protenix, Boltz-1 and FlexRibbon were generated locally using single seed with five generated samples. Other methods report top-1 ranked predictions, while our model does not use a confidence head; thus, we report random single-sample performance,
with "oracle" denoting the best prediction among five samples selected against the ground truth.
65
2.69
0.50
2.43
ESON
Rrotaban
3.42
0.75
Abhalicc
Figure Evaluation of ligand-induced conformational change prediction. The left panels show the per-target mean correlations (top) and mean ensemble TM-scores (bottom). In the right panel, an overlay of the predicted apo (blue, PDB 4AKE) and holo (yellow, PDB 2ECK) structures of adenylate kinase is presented, illustrating the conformational changes (highlighted by the dashed boxed regions) induced by the presence AMP and ADP molecules (orange). FlexRibbon predicts both states, with TM-score (Zhang & Skolnick 2004) of 0.985 and 0.984, respectively.
Protein-ligand docking prediction is a key task for modeling intermolecular interactions with broad implications for life sciences and drug discovery. We follow the AlphaFold protocol on the PoseBusters V1 benchmark (Buttenschoen et al.) (024)) of 428 protein-ligane ligand complexes, compar- models (AlphaFold3, Chai-1 (Chai Discovery 2024), Protenix (ByteDance AML A14Science Team et al. (025), Boltz-1 /Wohlwend et al. (0255) and single-sequence-bee based models (DiffDock (Corso et al. (023), DiftDock-L (Corso et al. (024)). Unlike previous single- sequence-based methods, which assume fixed protein structure and only generate ligand poses, FlexRibbon jointly predicts -igand structures directly from sequence like AlphaFold 3. As shown in Fig. FlexRibbon achieves 71.82% in the random-1 regime and 78. 3.70% under oracle selection, surpassing all single-sequence baselines by substantial margin and reaching parity with MSA-based approaches (Table 12).
ing against
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Table 2: Evaluation of protein-ligante binding affinity prediction on CASF-2016.
Method
RMSE (4)
R (1)
SIGN
1.316
0.797
GIANT
1.269
0.814
SPIN
1.258
0.826
FlexRibbon
1.150
0.848
Table 3: Fj for the Enzyme Commission (BC) and Gene Ontology (GO) tasks. The GO task is comprised of biological process (BP), molecular function (MF), and cellular component (CC).
Method
EC
GO-BP
GO-MF
GO-CC
ESM-2-3B
0.863
0.476
0.659
0.497
SFM-Protein-3B
0.869
0.495
0.673
0.510
ESM-GearNet
0.890
0.488
0.681
0.464
FlcxRibbon 0.891
0.539
0.694
0.560
higand-induced conformational change prediction key to understanding how proteins adapt upon ligand binding. We follow the ESMDiff protocol (Lu et al. 2025) and use the Apo/Holo dataset (Saldaño et al.] 2022) to evaluate FlexRibbon with ensemble TM-score (TM-ens) and residue flexibility correlations (ResFlex r) at both global and per-target levels. Baselines include AlphaFlow (MSA-based), ESMFlow (Jing et al.] 2024) isequence-basedd and ESMDiff saructure-language Unlike these methods, FlexRibbon only (5 samples) and (ii) mixed (3 apo + 2 holo samples) with ligand guidance. Using a zero-shot pretrained checkpoint, FlexRibbon achieves a TM-ens score 0.889 (improving upon ESMDiff by 0.038) and stronger flexibility correlations. Adding ligands further improves TM-ens by 0.012, showing the importance of ligand context. Figure and Table summarize the results.
model protein-ligand complexes in modes: (i) protein-
Protein-ligand binding affinity prediction is a cornerstone of drug discovery, enabling efficient prioritization of candidate compounds for therapeutic targets. Traditional high-th thghtthroughput screening is costly and limited in scope, motivating computational approaches that estimate binding affinities directly from protein-ligand structures. We evaluate FlexRibbon on the CASF-2016 benchmark (Su et al. 2018), using the standard metrics of root mean square error (RMSE) and Pearson's 's correlation coefficient (R). Comparisons are made against state-of-the-an baselines SIGN (Li et al., 202I),
GLANT et al. 2023), and SPIN (Choi et al., 2024). As shown in Table 2 FlexRibbon achieves the best performance on both criteria. These results highlight the value pretrained embeddings derived from joint protein-ligand structures strong basis for accurate affinity prediction.
4.3 PROTEIN FUNCTION PREDICTION
Protein function prediction is central to characterizing novel proteins, understanding disease, and guiding therapeutic discovery. We evaluate this by finetuning FlexRibbon on Gene Ontology (GO) (Ashburner al. 2000) and Enzyme Commission (EC) (Bairoch 2000) annotation tasks using the DeepFRI (Gligoorjvvi et al. 2021) setup. Baselines include sequence-only models (ESM-2-31) 2B, SFM-Protein-3B (He et al. (2244) and giquence-structurg hybrid (ESM-GearNet (Zhang et al.) 2.23 Unlike these methods, FlexRibbon jointly predicts structures and embeddings without ternal structural input. Performance is measured by maximum Fi score.
EC number prediction provides controlled benchmark for catalytic function annotation, formu- lated as a binary classification task. As shown in Table 3 FlexRibbon achieves an F1 score of 0,891, slightly exceeding ESM-GearNet and clearly outperforming sequence-only baselines (ESM- 2 and SFMMProtein). These results highlight the importance structural information in accurately capturing enzymatic function.

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0.85
0.8
0.80
0.6-
0.4
0.70
0.2
0.60
0.0
0.2
0.4
0.6
0.3
1.0
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AlphaFold3
ESMFold
ESM4
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Figure 6: Confidence estimation and structure quality on the CASP15 benchmark. Left: Correlation between observed TM-score and predicted pTM, with Pearson correlation coefficient of 0.89. For each target, 25 candidate structures are generated, and the nighest-confidence sample is used to compute the chain-level TM-score. Right: TM-scores of the highest- coofhes--oofiidecc predicted structures compared with their corresponding native structures.
GO term prediction evaluates protein function across biological processes (BP). molecular func- tions (MF), and cellular components (CC), each framed as an independent multi-label classification task, consistent with the EC setup. As shown in Table 3 FlexRibbon achieves F1 scores of 0.539 (BP), 0.694 (MF), and 0.560 (CC), outperforming ESM-GearNet by 0.051, 0.013, and 0.096, re- spectively. These gains indicate that FlexRibbon provides more informative representations for finetuning, enabling more accurate functional annotation across ontologies.
4.4 CONFIDENCE HEAD
Confidence estimation essential for evaluating the reliability predicted protein structures. Fol- lowing AlphaFold 3, our confidence head predicts the pairwise alignment error (PAE), and we com- pute the pTM score from these PAE values using the same formulation. This allows the model to produce global fold-accuracy estimate (pTM) that reflects its learned uncertainty distribu- tion. As shown in Fig. 6 the predicted pT'M correlates strongly with the true TM-score on the CASP15 Kryshtafovych al. 2023) benchmark, achieving Pearson correlation of R = 0.89.
For protein monomer structure prediction, we generate 25 samples per target and select the struc- ture with the highest confidence score for evaluation. As shown in Fig. 6 FlexRibbon achieves TM-score of 0.703 on CASP15 benchmark, surpassing the single-sequence baselines ESMFold and ESM3 by 0.019 and 0.030 TM-score, respectively. These results demonstrate the reliability of confidence estimation and the strong effectiveness of the overall structure prediction. Additional details are provided in Appendix D.9
CONCLUSION
We introduce FlexRibbon, a pretrained protein foundation model that integrates both sequence and structural information into unified framework. Unlike prior approaches that impose way sequence-to-struutume mapping. FlexRibbon implements joint training via masked language modeling and diffusion-based denoising, enabling bidirectional sequence-structure representations that support both prediction and design. Extensive evaluations across diverse set of downstream tasks spanning antibody/nanobody and peptide interface modeling, ligand-induee induced conformational change, protein-ligand binding affinity, and functional annotation demonstrate strong and consistent gains, with especially notable improvements on flexible and mutation-rich interfaces where existing methods struggle. These results highlight the effectiveness of joint sequence-structure pretrain- ing and show that its benefits extend broadly beyond protein folding, establishing FlexRibbon as general-purpost foundation model for protein science.
ACKNOWLEDGMENTS
This work is supported by Zhongguaneun Academy (Grant No. C20250514).
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A DATA
The pretraining dataset is constructed from two primary sources: AFDB and PDB.
The AlphaFold Protein Structure Database (AFDB), released by Google DeepMind and EMBL-EBI, contains over 200 million predicted structures spanning nearly the entire UniProt 2021 04 release. cluster sequences 90% identity and retain only entries with global PLDDT score greater than 50, discarding low-confidence structures. This yields approximately 78 million AFDB samples.
To reduce redundancy,
For experimentally resolved structures in Protein Data Bank (PDB), we use PDB 20210930, adopt- ing the same cutoff date AlphaFold Following their filtering protocol, we exclude structures with more than 300 chains, resolution worse than 9 A,or fewer than residues. After filtering, we obtain roughly 181 thousand PDB samples.
In total, our pretraining corpus comprises more than 78 million protein structures.
The datasets used for each downstream task are detailed in Appendix D
B ARCHITECTURE
Figure 7 and Table 4 summarize the model. FlexRibbon adopts a three-stage design:
Sequence module. Jointly embeds protein residues and small-molecule using a Transformer encoder. Protein residues receive RoPE for order sensitivity, while atom em- beddings remain permutation-invariiatt Learnable attention biases from atom categories and bond types provide chemical context, and a pair-feature update module refines residue-aton atom interactions. In addition, we introduce associated with each potential bond. This yields pairwise tensor of shape (B. I, I. Hbr) that encodes bond-aware chemical relations across all atom pairs. A lightweight MLP projects this rep- resentation single scalar per pair, which is added to the attention logits as additional structural prior. This learned 2D bond feature enables the model to capture covalent connectivity in a differ- entiable manner, complementing both sequence embeddings and atom-type cues.
into unified space
covalent-bond feature by embedding the ordered atom-tyre atom-type pairs
Coarse-grained structure module. Applies a Diffusion Transformer (DiT) to denoise residue and atom coordinates reduced resolution. Conditioning sequence embeddings integrates sequence
x
All atom Offlusion Tranformer Block
Pork
Kall
Mironn
Mastia
Elass
Rosiduc Offfusion Torsformrr Block
Diflsion Tranformer Bock
X.N
Scquerce Tranformer ock
inge
Sryakent
Sequeesce Thanebrrme
Figure 7: Detailed Model Architecture of FlexRibbon.
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semantics with structural noise while keeping attention maps small enough to allocate parameters to structural reasoning.
All-atom structure module. Upsamples to full atomic resolution with second DiT. Coarse outputs are broadcast to all atoms in each residue as conditional input. Atom- and bond-type biases are added to attention maps preserve chemical validity, reconciling residue-level context with detailed atomic geometry.
This design captures the global geometry of diverse biomolecular complexes and refines them into chemically consistent, high-resolution structures. Key hyperparameters are listed in Table
Table Key architectural hyperparameters of FlexRibbon.
Component
Hyperparameter
Value
Number of layers
32
Hidden size
2,048
Sequence Module
FFN dimension
8,192
Attention heads
32
Number of layers
16
Hidden dimension
2,048
Coarse-graines structure module
FFN dimension
8,192
Attention heads
32
Number of layers
8
Embedding dimension
256
All-atom structure module
FFN dimension
256
Attention heads
4
C TRAINING AND INFERENCE
C.I SORRDENCE-WEIGGHEEE DIFFUSION LOSS.
To incorporate structural reliability, we scale the diffusion MSE loss using residue-level pLDDT scores with a sigmoid weighting function. Residues in low-confidence regions (pLDDT around 60 or below) receive weights near zero, whereas residues in high-confidence regions (pLDDT around 80 or above) receive weights near one. The transition between these regimes is controlled by smooth sigmoid:
w - PLDDT - -70 -70)
10
where ((.) is the logistic sigmoid and 3 controls the steepness of the curve. In practice, set 3 = 5 such that weights are near zero at PLDDT = 60 and near one at pLDDT = 80. This formulation avoids hard thresholds while ensuring that uncertain structural regions contribute less the optimization, and high-confidene regions dominate the learning signal. Also, this design keeps signal from disordered regions without letting their noisy coordinates dominate the loss, a balance that aligns with the qualitative behavior shown in Figure of Section D.11 using the disordered example reproduced from the AlphaFold3 paper.
C.2
DISTANCE LOSS.
We apply Lis top of the sequence module to regularize predicted inter-residue distances. Specif- ically, the sequence encoder outputs residue-level embeddings, which are combined through an outer product to form pairwise features. A lightweight MLP head then predicts the C.-C, distance for each residue pair. The loss penalizes deviations between predicted and ground- truth distances, en- couraging the encoder to capture geometric constraints directly the sequence -air feature level. This design provides the model with explicit supervision on tertiary structure geometry while avoid- ing direct reliance coordinate-level regression.
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C.3 STRUCTURE-INFORNIE MASKED LANGUAGE MODELING (SIMLM) LOSS.
We design a stracture-informed masked language modeling loss to align sequence and structure representations. Only protein sequences (FASTA) are masked, following the BERT-style policy: 15% of residues are selected for corruption, with 80% replaced by [MASK], 10% replaced by a random amino acid, and 10% left unchanged. For each masked residue with ground-truth identity y/. we compute hidden features from both the sequence encoder f; and the coarse-grained structure encoder f (e.g., based on Ca geometry). Two independent prediction heads are applied: one (struct) (yi). The loss averages the maps S to a distribution ((04) (y:) and the other maps fuinuet to Pa negative log- ligeliisssssss from both heads:
LS
Σ KEMM
(struct) logrb (se4) (y: 11,09 + log Pb
(1s [(tmict)]
M
where M is the set of masked positions. This formulation encourages both the sequence and struc- ture pathways to retain predictive signal for residue identity, thereby improving cross-modal con- sistency. In practice, we interleave the three perturbation modes (Mode I, Mode 2, and Mode 3) during training with a ratio of 6 2: 2, balancing standard sequeecceto--srrucum generation with increasingly challenging coupled and global perturbations.
C.4 SMOOTH-LDDT LOSS.
Following AlphaFold2 (Jumper et al. 2021 we compute the smooth local distance difference test (IDDT) loss assess local structural accuracy. The smooth IDDT metrie measures the agreement of predicted inter-residue distances with the ground truth in differentiable manner. Specifically, for each residue we evaluate all neighboring residues within cutoff radius (typically 15 A). For each pair (), the absolute deviation of the predicted Ca-Ca distance from the reference is mapped to a soft score using a piecewise linear function with thresholds at 0.5, 1, 2, and 4 A. The usiduee-wise scores are averaged across neighbors and then across residues to produce the overall smooth IDDT. In training, we only use Ca atoms to compute this loss, consistent with AlphaFold2. The resulting value serves both as a differentiable accuracy proxy and as a regularizer encouraging the model to capture local geometric consistency.
C.5 TRAINING RECIPE.
We adopt a three-stage pretraining strategy (see T). Stage focuses on diffusion- -ased denois- ing with proteins of up to 384 residues. Training uses the Adam optimizer (Kingma & Ba 2015) in b6 mixed precision with a batch size of 4,096 on 128 A100 GPUs for 200k steps and learning rate of x 10 This stage builds the core ability reconstruct clean structures from noisy inputs while incorporating structural regularization via distance and smooth-IDDT losses. Stage B extends the maximum protein length to 768 residues and adds the masked language modeling (MLM) ob- jective. Training uses batch size of 2,048 on the same hardware for 100k steps with a learning rate of 6 x 10-5, This stage enables the model handle larger proteins and integrate sequence-level supervision, while continuing to optimize diffusion, distance, and smooth-IDDT objectives.
Stage C further increases the maximum sequence length 1,024 residues and introduces super- vised training of the confidence head. Following AlphaFold 3 (Abramson et al. 2024), we gener- ate confidence labels by running 20-step mini rollout sampling to obtain predicted structures and computing pLDDT and PAE targets from these rollouts. In our architecture, the confidence head implemented as lightweight MLP attached after the coarse- gaarse-graindd structure module, taking its structure-informed residue embeddings as input to predict PAE and PLDDT. Unlike AlphaFold 3. however, we do not stop gradients from the confidence losses; instead, we allow them to up- date sequence module and coarse- coarse-grained structure module's parameters. Our confidence head is lightweight MLP rather than a specialized architecture. Stage C is trained for 20k steps with a batch size of 2, 2,048 and learning rate of 2 x 10-5, consolidating joint optimization of structure, sequence, and confidence estimation at full sequence scale.
C.6 DIFFUSION TRAINING DETAILS
We provide here the complete derivations and formulation details omitted from the main text.
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Table Three-stage pretraining configuration.
Stage A
Stage B
Stage C
Max residues
384
768
1024
MSE Loss
True
True
True
Dist Loss
True
True
True
Smooth IDDT Loss
True
True
True
MLM Loss
False
True
True
pIDDT Loss
False
False
True
PAE Loss
False
False
True
Batch size
4,096
2,048
2,048
Steps
200k
100k
20k
Learning rate
10-4
10-5
10-5
Forward process. Diffusion-based generative modeling aims to approximate a target distribution ((R) by connecting to a tractable source distribution Psre- We represent protein structure R RAN by the 3D coordinates of all heavy atoms. The forward noising process is defined as R₂ Ro + oiEt, e, e,,,(( t e [0,
with Ø, monotone increasing in t. For sufficiently large oT, Rr approximates Gaussian distribu- tion Pare = (0, 1).
This corresponds the SDE
dR. Vityy dw
where W; is Wiener process on 33
Reverse process. By stochastic process theory (Anderson 1982). one can recover p(R) by sim- ulating the reverse diffusion process. A deterministic equivalent given by the probability-flor ODE (Song al. 2021):
dR. =
where denotes reversed time and R, denotes the reversed sample trajectory.
The only unknown term is V log Pt, which approximate with so (R,,t) Min- imizing the score-matching objective
Score estimation.
(R,) ||so (R,,)) () V (R.))
equivalent denoising objective with conditional distribution p(R, Ro) N(R, Ro, o²1): min Ep(r we (p( (R,, R
where we use the parameterization
Do t) R of Intuitively, the network predicts the clean structure Ro from its noisy version R,, hence the name "denoising model."
R,
Rigid-budy invariances. Protein structures are equivalent up to rigid-body transformations. Translations are removed by centering at the center of mass. Rotational invariance is harder: while -qquivaanvv networks (Köhler et al. 2020) can guarantee invariance, they often require heavy operations and introduce reflection symmetry. We instead
standard architecture and provide rotational invariance information via random SO(3) data augmentation. Some works apply explicit rotational alignment the loss (Xu et 2022 Abramson al. 2024), but such alignment lacks consistent orientation correspondence and complicates sampling WWohlwend et al. 2025). In our ex- periments, the plain denoising objective already yielded stable and effective training, we removed the alignment operation in the loss.
"eeoverrnng the exact Rg impossible due information loss; the model in fact predicts R.R., Ry].
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Algorithm Sampling procedure.
Require: A trained diffusion model in denoising form (g(R, t) under the noise schedule choice
0t 1, sampling time schedule to < li < ....
< IN = T, recursion ratio Tresurs recursion
threshold Tmin noise scale X.
1: Initialize Ro ~ Pare == (0, TI)::
2: for t0 N 1 do
3: Center R, to its center of mass;
4:
Trecur if T
E Mmin else O;
5: t = (1 7/ TT:
6:
R R + +> 1.22 2-- (T ( where (0,
Do(R, -T- Rτ, (f: 14+1);
7: R = R +
T-h
8: end for
9: return R
C.7 SAMPLING PROCEDURE
From the reverse sampling formulation in Eq. what essentially controls the progression of the diffusion process is the discretization of σ₁. A convenient choice is thus to let a, (Karras et al. 2022). The sampling process then specified by discretization of the reverse time ... < IN T. where N is the number of discretization steps. Following (Karras et al. 2022) (which also adopted in Alphafold 3 (Abramson al.] 2024)), in each step. the update starts not directly from the current time step f, Instead, the clock is first recurred back to f = (1 (1+ 7T (which comes from increasing the forward time by (1 + 7), i.e., T more noisy state, which can be implemented by simulating the forward process from T - to T
4g
(1 (T - f,)) with
(). The simulation then proceeds by an update from L, to [ following Eq. (3) In contrast to the sampling process by Alphafold 3, we do not need random rotation in each step as we do not use rotational alignment in training. The complete procedure is presented in Alg.
as R3 RG + (T 2 - (T F2 2 where
C.8 INFERENCE TIME AND MEMORY
To assess the practicality of FlexRibbon for real-world downstream applications, we measure its inference time and memory consumption. We select four heteromers and four homomers from the PDB, with sequence lengths of approximately 256, 512, 1024, and 2048 residues as test cases. Note that the comparison focuses solely on the forward pass of the network and does not include data preprocessing time. All experiments are conducted single NVIDIA A100 (80 GB) GPU.
Table 6: Inference time and memory profile using single A100 80 GB.
PDB
Type
Residue number Inference time (sec.)
GPU Memory (GiB)
IAB9
Heteromer
246
11.3
6.7
IKGO
Heteromer
517
16.7
8.0
8ODX
Heteromer
1,024
43.3
11.0
STQO
Heteromer
2,064
139.8
27.0
4W80
Homomer
256
10.9
7.0
8VE4
Homomer
512
15.7
7.7
1 IVHI
Homomer
1,024
42.0
11.0
6EBM
Homomer
2,052
139.0
27.2
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D EXPERIMENTS DETAILS
Downstream tasks are application-specife benchmarks designed to evaluate how effectively a pre- trained foundation model can be adapted solve targeted scientific problems. While pretraining provides the model with general sequence-structtre representations, downstream tasks assess its transferability to practical domains such as protein design, intermolecular interaction prediction, and functional annotation. These tasks typically involve fine-tuning the model on smaller, curated datasets and comparing its performance against established baselines. By systematically evaluating across diverse downstream tasks, we demonstrate not only the generality of the pretrained model, but also its ability to capture biologically meaningful features that enable scientific discovery. Details of the fine-tuning procedure, dataset partitioning, and evaluation metries, are provided below.
D.1 ANTIBODY AND NANORODY INTERFACE PREDICTION
Datasets. High-quality datasets are essential for evaluating antibody and nanobody interface mod- cling. We use SAbDab (Dunbar et al., 2013) as the training dataset and adopt the same training, validation, and test splits as in IgGM (Wang et al., 2025a) to ensure fair comparison with cutoff 2022-12-31. Moreover, we removed anti-ligand pattern from the dataset. In total, we constructed 10028 samples from 5146 unique PDB ids, in which 2023 samples are nanobody and make up I 1108 unique PDB ids. for training and validation and evaluate performance on 60 antigen-antibode dock- ing structures (SAb-23H2-Ab) and 27 antigen-nanobod, docking structures (SAb-23H2-Nano),
Finetuning and inference. Accurate antibody modeling requires leveraging both sequence and structural information. We incorporate epitope annotations, which have been shown to be critical for reliable antibody prediction (Wang et al. 2025a). by labeling residues with at least one heavy atom within 10 Á of antibody or nanobody chain. During fine-tum munintnnng,
adopt 4 complementary training modes to balance complex structure prediction and antibody design: (i) with 30% probabil- ity, the model receives full sequences and predicts the complex structure; (ii) with 40% probability, the model is provided with the antibody backbone sequence, antigen sequence, and antigen structure, and is tasked with designing antibody CDR sequences and structures; (iii) with 15% probability, the model receives antibody and antigen sequences along with the antibody structure and predicts the antigen structure: and (iv) with 15% probability, the model receives antibody and antigen sequences along with the antigen structure and predicts the antibody structure. Following the IgGM infer- ence protocol, we generate samples per test case. The input consists of antigen sequence, antigen structure, epitope annotations, and antibody sequence, and the model predicts the complex structure.
Evaluation metrics. Model performance is evaluated using DockQ, interface RMSD (iRMS), lig- and RMSD (LRMS), and success rate (SR, defined as DockQ > 0.23), which are widely used in the antibody modeling community (Mirabello & Wallner 2024 Wu et al., 2022b; Wang et al. (025a). DockQ, iRMS, and LRMS are averaged across all generated samples, while the success rate is com- puted as the fraction of all generated samples with DockQ 2 0.23. As shown in Table 7 and Table R
FlexRibbon consistently achieves the best performance across all metrics, substantially outperform- ing existing baselines.
Table 7: Metrics for prediction of antigen-antibody docking structure. (Fold-Ag and AlphaFold use MSA information as input. dyMEAN, IgGM, and FlexRibbon use antigen structure information. All methods except AlphaFold use epitope information. AlphaFold 3, dyMEAN, tFold-Ag, and IgGM results are taken from Wang et al. (2025a). Methods marked with use MSA input.
Method
DockQt
iRMS}
LRMS
SuccessRate
IFold-Ag-HDode
0.022
16.652
48.157
0.0000
(Fold-Ag
0.252
6.796
21.035
0.4068
AlphaFold 31
0.295
10.965
32.408
0.3684
dyMEAN
0.101
8.923
27.423
0.0667
IgGM
0.299
6.220
19.489
0.4667
FlexRibbon
0.384
5.704
14.533
0.6133
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Table 8: Metrics for structure prediction for nanobody. (Fold-Ag and AlphaFold use MSA in- formation as input. IgGM, and FlexRibbon use antigen structure information. All methods except AlphaFold 3 use epitope information. AlphaFold 3, tFold-Ag, and IgGM results are taken from Wang et al. (2025a). Methods marked with use MSA as input.
Method
DockQ1
iRMS
LRMS}
SuccessRatet
tFold-Ag AlphaFood 31
0.288
6.349
15.081
0.4296
0.287
11.219
32.676
0.3885
IgGM
0.291
7.988
22.017
0.4400
FlexRibbon 0.336
5,380
11.632
0.5111
D.2 PROTEIN-PEPTINE INTERFACE PREDICTION
Datasets. High-quality, non-redundant datasets are essential for training accurate protein-peptide interface models. We constructed the training dataset from PepGLAD (Kong et al., 2025) and ap- plied temporal filter to exclude entries released after September 30. 2021, ensuring that the pre- trained model had no prior exposure test-like data. After filtering, the dataset contains 5,202 non-redundant protein-peptide complexes, reduced from the original 6,105 entries. For evaluation, we use FoldBench (Xu et al. 2025), which comprises 51 protein-peptide pairs, all sharing less than 40% sequence identity with the training and validation sets.
Finetuning and inference. Effective fine-tuning crucial for adapting a foundation model to spe- cific downstream tasks. We adopt the same hyperparameterss as in the antibody and nanobody inter- face prediction task. During inference, we follow the AlphaFold 3 procedure, generating samples per test instance. The input consists of the protein sequence, protein structure, epitope annotations, and the peptide sequence, and the model predicts the corresponding protein-peptide complex struc- ture. Additional details are provided in Section D.1
Table 9: Metrics for peptide structure prediction. AlphaFold and Boltz-1 use MSA information as input, while PepGLAD and FlexRibbon leverage protein structure and epitope information. Results for Boltz-1 and AlphaFold 3 taken from (Xu et al. 2025), where DockQ scores are not reported. PepGLAD results are obtained by running the method on this benchmark. Methods marked with use MSA input.
Method
DockQt
iRMS
LRMS}
SuccessRatet
Boltz-1i
2.88
8.82
0.8039
AlphaFold 3t
2.81
6.56
0.8431
PepGLAD 0.413
3.18
6.72
0.8118
FlexRibbon 0.558
1.93
5.17
0.9137
Evaluation metrics. Standardized metrics are important for consistent assessment of interface pre- diction performance. We evaluate model performance using DockQ, interface RMSD (iRMS), lig- and RMSD (LRMS), and success rate (SR, defined as the proportion of samples which have DockQ 0.23). DockQ, iRMS, and LRMS are averaged across all generated samples, while the success rate is computed as the fraction of samples with DockQ 2 0.23. When computing DockQ, the heavy and light chains of the antibody are merged into a single chain, with the antigen treated as a sepa- rate chain. As shown in Table 9 FlexRibbon outperforms all other methods across these metrics, demonstrating more accurate modeling of protein- peptide interfaces.
D.3 ANTIBODY AND NANOBODY DESIGN
Datasets. High-quality and consistent datasets essential for evaluating CDR design performance. We use the same training. validation, and test datasets as described in Section D.I to ensure compa- rability and reproducibility.
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Training and inference. Effective CDR design requires careful integration of sequence and struc- tural information. During training, we follow the procedure in Section D.I At inference, the CDR regions masked to enable the model to design sequences based the antigen structure and epitope annotations. Users can also specify different CDR lengths to generate diverse designs. Following the IgGM evaluation protocol, generate 5 samples per test case using the same CDR lengths as the wild-type sequences, jointly designing all six CDR regions for antibodies and all three CDR regions for nanobodies.
Table 10: Comparison of antibody modeling methods for antibody design, reporting CDR loop accuracy (AAR, RMSD) and docking performance. Higher values of AAR, DockQ, and indicate better performance, while lower values of RMSDD, iRMS, and LRMS preferable. DockQ scores are computed by comparing the designed structures against the corresponding wild-type complexes.
Model
DiffAb (IgFold)
dyMEAN
IgGM
FlexRibbon
AAR 1
LI
0.597
0.633
0.750
0.727
L2
0.598
0.634
0.743
0.773
L3
0.421
0.570
0.635
0.653
HI
0.642
0.742
0.740
0.745
H2
0.363
0.627
0.644
0.683
H3
0.214
0.294
0.360
0.414
Docking with wild-type
DockQ f
0.022
0.079
0.246
0.273
iRMS 1
17.034
9.698
6.579
6.961
LRMS $
48.163
28.764
19.678
19.599
Success Rate f
0.000
0.049
0,433
0.460
Evaluation metrics. To assess both sequence fidelity and structural accuracy in CDR design, employ standard quantitative metries. Sequence similarity to the wild type measured using amino acid recovery (AAR) (Wang et al.] 2025a), where higher values indicate closer agreement. For an- tibodics, AAR computed independently for each of the six CDRs (three heavy-chain and three light-chain regions) and then averaged across samples; for nanobodies, AAR is computed over the three CDRs. Structural quality evaluated using DockQ, interface RMSD (iRMS), ligand RMSD) (LRMS), and success rate (SR; the percentage of designs with DockQ 2 0.23). DockQ is computed by merging the antibody heavy and light chains into a single chain and treating the antigen as a sepa- rate chain; for nanobodies, DockQ is computed between the nanobody and the antigen. We compare against established baselines including DiffAb (Luo et al. 2022). (yMMEN((onng et al. 2023b) and IgGM (Wang al. 2025a) over the evaluation metries. shown Tables I0 and [1 FiexRibbon achieves performance comparable to IgGM across all metrics, indicating that it generates realistic CDR sequences while preserving structural accuracy.
Table 11: Comparison of nanobody modeling methods for nanobody design, reporting CDR accu- racy (AAR), RMSD, and docking performance. Higher values of AAR, DockQ, and SR indicate better performance, while lower RMSD, iRMS, and LRMS are preferable. DockQ scores are calcu- lated by comparing the designed structures the corresponding wild-type complexes.
Method
CDR1
CDR2 t
CDR3 t
DockQ 1
iRMS 4
LRMS 4
Success Rate f
DiffAb (AF3)
0.533
0.291
0.156
0.211
13.265
35,805
0.346
IgGM
0.565
0.330
0.183
0.267
6.927
14.966
0.415
FlexRibbon
0.500
0.441
0.218
0.244
5.571
13.506
0.437
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D.4 PROTEIN-LIGAND DOCKING PREDICTION
Datasets. Benchmarking performance without finetuning is key way to assess a model's gener- alization ability. For this task, directly evaluate our model in the pretraining setting. The test set is PoseBusters VI (Buttenschoen et al. (024). which contains 428 protein- protein-ligand complexes deposited in the PDB between January 2021 and May 30, 2023. For pretraining, we follow the same protocol as Boltz-1 and Protenix, using all PDB structures released before 2021-09-30. Since these three methods share same cutoff time, comparisons on test set remain fair.
Training and inference. Evaluating direct inference provides insight into model's ability to di- rectly predict complex structures from minimal imputs. Given protein sequences and ligand SMILES strings, we generate full protein-ligand complex structures in manner similar to AlphaFold 3. For FlexRibbon, Protenix, and Boltz-1, we generate samples per case using a single random seed.
Table 12: Success Rate (SR) comparison of different methods.
Method
Success Rate (%) f
MSA-based
AlphaFold3 2019
76.40
Chai-1
77.00
Protenix top1
77.10
Protenix oracle
81.31
Boltz-1 top1
69.62
Boltz-1 oracle
74.06
Single-sequencc--aseed
DiffDock
37.90
DiffDock-L
50.00
FlexRibbon random1
71.82
FlexRibbon oracle
78.70
Evaluation metries. Standardized metrics critical ensure reliable comparison across meth- ods. Following the AlphaFold 3 protocol, we report the success rate, defined as the percentage of predictions with a pocket-aligned RMSD <2 A. The pocket-alignment procedure is consistent with AlphaFold 3: the pocket is defined as all heavy atoms within 10 Á of any ligand heavy atom, restricted to the primary polymer chain or modified residue of the ligand, and further limited to protein backbone atoms. Baselines include MSA-based methods (AlphaFold 3, Chai-1, Protenix, Boltz-1) and single-sequence methods (DifiDock. DiffDock-L). For Protenix and Boltz-1, results reported using the top-ranked sample out of five diffusion-generann bon, which does not include a confidence head, we report both the random- score (performance of randomly chosen sample) and the oracle score (the best of five samples selected against the ground truth). Note that Boltz-1 I failed on 7M31 TDR and 7SUC COM due to residue number restrictions.
predictions. For FlexRib-
D.5 LIGAND-INDUCES CONFORMATIONAL CHANGE PREDICTION
Datasets. Benchmarking performance without finetuning provides insights into model's ability to generalize without task-specific adaptation. For this task, we do not perform finetuning and directly evaluate the capability of our model. The test set, originally derived from (Saldñoo al. (2022). consists of 90 apo-holo protein pairs, which were relesed between June 1994 and February 2021.
Training and inference. Direct inference allows us to assess the model's structural prediction ability under different input conditions. Given protein sequences and ligand SMILES strings, we generate structural predictions without any fine-tuning. Specifically, we produce five predictions for each without ligands (apo) and five predictions with ligands obtained from the original holo complexes in the PDB. For fair comparison, we report two evaluation settings: (1) all five apo samples, and (2) mixed set of three apo samples and two holo samples.
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Table 13: Evaluation of ligand-induced conformation changes: (1) Pearson correlation (r) between sampled and ground-truth diversity as measured by the residue flexibility (ResFlex, absolute de- viation after alignment), and (2) the ensemble TM-score (TM-ens). For residue flexibility, both global (gl.) correlations and mean/median per-target (pt.) correlations are reported; for TM-ens, mean/median correlations are reported. Higher values indicate better performance. Methods marked with use MSA as input.
Method
ResFlex (gl.) 1
ResFlex r (pt.) f
TM-ens
Benchmark results compared against baselines
AlphaFlowf
0.455
0.527/0.527
0.864/0.893
ESMFlow
0.416
0.496/0.522
0.856/0.893
ESMDiff
0,424
0.502/0.517
0.851/0.88%
FlexRibbon
0.503
0.551/0.542
0.889/0.92 .200
FlexRibbon with ligand
0.519
0.587/0.615
0.901/0.931
Ablation study
FlexRibbon Sapo
0.503
0.551/0.542
0.889/0.920
FlexRibbon Sholo
0.454
0.524/0.540
0.888/0.918
FlexRibbon 3apo+2holo
0.519
0.587/0.615
0.901/0.931
FlexRibbon 5apo+5holo
0.535
0.620/0.638
0.907/0.936
Figure 8: Overlay of the prediction of the (left) apo and (right) holo states of adenylate kinase, overlaid with the native(grey) from the PDB. The holo state includes an AMP and an ADP molecule (orange), whose presence induces the kinase to fold inwards to hold the molecules in place.
Evaluation metrics. Rigorous evaluation metries are essential to capture both accuracy and diver- sity in structural predictions. Following the protocol in AlphaFlow (Jing et al.) 2024), we use two types of metries. The first is the Pearson correlation (r) between sampled diversity and ground-truth diversity, measured by residue flexibility (ResFlex, absolute deviation after alignment), reported as global (gl.) mean and per-target (pt.) mean/median correlations. The second the ensemble TM- score (TM-ens), reported as mean and median. Results are presented in Section 4.2) In addition, conduct an ablation study with different ligand conditions, showing FlexRibbon can generate struc- tures in multiple conformational states, highlighting its potential to address multi-state problem.
D.6 PROTEIN-LIGAND BINDING AFFINITY PREDICTION
Datasets. Reliable benchmarking requires consistent training and evaluation protocols. Following the strategy of SPIN (Choi al. 2024), use the same training and test sets. The training data
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drawn from PDBbind v2020 (Liu al., 2017). comprising 19,443 protein-ligand complexes. For evaluation, we adopt the CASF-2016 (Su et al. 2018) benchmark, which includes 285 samples. To prevent data leakage, any overlapping entries between CASF-2016 and the training set were removed.
Training and inference. Model training is formulated as regression task to predict binding affinity values. The input is the diree-dimensiona structure of protein-ligans ligand complexes, and the target output is continuous affinity score. During inference, the model predicts one affinity score per sample, which is directly compared against the ground-truth value.
Evaluation metrics. Standard regression metries used to assess predictive accuracy and cor- relation with experimental data. Specifically, we report the Root Mean Square Error (RMSE) and Pearson's correlation coefficient (RR). Detailed results are presented in Section 4.2
D.7 EC NUMBER PREDICTION
Datasets. Accurate enzyme function prediction requires high-quality annotation datasets. We follow the dataset setup used in DeepFRI (Gligonjevii et al.) (021), where enzyme annotations are derived from UniProtKB with experimentally validated Enzyme Commission (EC) numbers. The training. validation, and test set contains 15551, 1729, 1919 protein samples respectively.
Training and inference. The task formulated protein sequence may be associated with one more EC numbers. During inference, the model outputs probability scores over all possible EC labels, which then used to compute the precision- recall curve. For both training and inference, the protein sequences passed through base model once for structure prediction, after which both sequence and structural information are used for model finetuning and evaluation.
multi-label classification problem, where each
Evaluation metrics. Model performance is evaluated using the maximum F-score (F1), which bal- ances precision and recall. Specifically, Fj is defined the maximum F-score across all probability thresholds:
2 Precision(() Recall(()
F
max fE Precision(t) Recall(t)
where t is the threshold applied to predicted probabilities.
D.8 GO TERM PREDICTION
Datasets. Gene Ontology (GO) provides comprehensive representation of protein function, cover- ing three sub-ontologies: Molecular Function (MF), Biological Process (BP), and Cellular Compo- nent (CC). Following DeepFRI (Glliorijjvié et al. 2021), construct the training, validation, and test set as shown in Table
Table 14: Size of data samples used for the Gene Ontology (GO) task.
Sub-ontology
Training samples
Validation samples
Test samples
BP
23,514
2,624
3,415
MF
24,952
2,747
3,415
CC
11,298
1,299
3,415
Training and inference. GO term prediction is also framed as multi-label classification problem. For each protein, the model outputs probability scores over GO terms independently for MF, BP, and CC. The training and inference procedures are identical to those used for EC number prediction, including structure prediction.
Evaluation metrics. Performance is measured by Fi, defined as the maximum Fi-score across thresholds. The metric captures the balance between precision and recall in predicting GO terms and is widely adopted in functional annotation benchmarks.
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D.9 PROTEIN STRUCTURE PREDICTION
Datasets. For the protein structure prediction task, we do not perform any finetuning and therefore do not use a training set. To evaluate the performance of FlexRibbon, we adopt two standard bench- obtain the data from the official
marks: CASPI5 (Kryshtafovych
al. 2023). For CASP15, website btttps://pret ct ooneette wwwentter.rgg........o...... org/). The organizers released 45 native-domain structures derived from 30 full-chain targets, with the first target made available May 2, 2022.
Training and inference. For the protein structure prediction task, we directly use the pretrained model without any task-specific finetuning. During inference, given full-chain amino acid quence, the model predicts atomic-level coordinates for the entire structure. Following the predic- tion protocol of AlphaFold 3, generate 25 samples for each target using different random seeds and select the prediction with the highest confidence score as the final output.
Evaluation metries. For CASPI5, we follow the official evaluation protocol: structures are pre- dicted at the whole-chain level and evaluated on the released domains. The primary metrics are TM- score (Zhang & Skolnick, (004)) and IDDT (Mariani et al. 2013). Baselines include AlphaFold 3, ESMFold and ESM3: we run both methods locally and evaluate all predictions using the TMscore program (version 20220227) and 1ddt program (version 2. 1....1). 1). As shown in Table [5 FlexRib- bon achieves ofate-of-the-art performance among single-sequence-base methods.
Table 15: Evaluation of protein structure prediction on CASPIS. Results for AlphaFold 3, ESMFold, ESM3, and FlexRibbon are obtained by running all methods locally and evaluating them using the same pipeline based on the TMscore and 1ddt programs. Each target is predicted at the whole- chain level and evaluated on the released domains. Methods marked with 7 use MSA as input.
Method
TM-score f IDDT 1
AlphaFold 0.820
0.840
ESMFold
0.684
0.718
ESM3
0.673
0.689
FlexRibbon
0.703
0.726
D.10 FINETUNING TIME AND MEMORY
To assess the practicality of FlexRibbon for real-world downstream applications, report the fine- tuning runtime and GPU memory usage for each task, as different downstream tasks vary substan- tially in dataset size and training configuration. The number of GPUs used and the total finetuning time are summarized in [6 Since the aageeinnaniiodyynnnnobbo
interface prediction task and the antibody/hanooody design task share the same training dataset, we report their finetuning cost as single combined entry. Overall, FlexRibbon can typically be finetuned for wide range of real- world applications using only 8 GPUs, demonstrating both its efficiency and practical applicability.
Table 16: Finetuning cost for each downstream task. All experiments conducted the nodes equipped with NVIDIA A100 80 GB GPUs.
Task
# #(A100 80G)
Time (hours)
Antibody/Nanooody interface prediction and design
16
69
Protein-peptide interface prediction
8
15
Protein-ligand docking prediction
N/A
N/A
Ligand-induced conformational change prediction
N/A
N/A
Protein-ligand binding affinity prediction
8
5
EC number prediction
8
10
GO term prediction
8
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(a)
(d)
Figure 9: Comparison of disordered-region predictions for the DP02376 protein from the CAID (Critical Assessment of protein Intrinsic Disorder) dataset, corresponding to the example shown in Extended Data Fig. 1 of the AlphaFold paper. Predictions are shown for: (a) FlexRibbon, (b) Boltz-1, (c) Boltz-2, and (d) AlphaFold 3.
D.11 DISORDERED REGION PREDICTION
To assess whether our model can qualitatively handle intrinsically disordered segments, we repro- duced the disordered-region prediction example from AlphaFold 3's paper (Extended Data Fig. 1). The protein is DP02376 from the CAID 2 (Critical Assessment of protein Intrinsic Disorder pre- diction) set. The sequence contains well-characterized disordered stretch, which AlphaFold highlighted as a case where conventional structure predictors tend to over-compact flexible regions.
Figure 9 presents the same sequence predicted by FlexRibbon. Boltz- (Wohleend al. 2025
et al., 2025), and AlphaFold 3(Abramson et al. 2024). Consistent with AlphaFold 3's original observation, only FlexRibbon and AlphaFold produce conformation that preserves the expected resorder-reffectedd as diffuse, low-confidence geometry rather than artificially rigid fold. The remaining models collapse the flexible segment into compact structure, failing to reflect intrinsic disorder. This qualitative reproduction demonstrates that our model captures structural uncertainty in a manner comparable to AlphaFold 3 for this illustrative case.
E USAGE OF LLM
We employed GPT-5 to assist in refining the writing of this manuscript. Specifically, GPT-5 was used polish grammar, improve readability, and streamline phrasing, while all scientific content, experimental design, and data analysis were developed and verified by the authors. The use GPT- 5 was limited to language refinement and did not influence the technical contributions conclusions of this work.
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Reviews:
[{"review_id": "8NLLCBwTpJ", "reviewer": "Reviewer_RzWH", "summary": "FlexProtein is a pretrained protein model that jointly learns from sequences and 3D structures without MSAs, addressing limitations of existing sequence-only/MSA-based models. It captures global folds and flexible conformations via masked language modeling and diffusion-based denoising. It sets new SOTA on 12 tasks, notably in mutation-rich scenarios.", "questions": "- For all-atom coordinate denoising, how do you determine the number and type of atoms in a residue, when the sequence is masked, or perturbed? Is the structure module optmized only when the clean sequence is given?\n- In Section 3.3, there is denoising diffusion loss for coordinates, why is this section named `masked language model'?\n- In mode 1, do you jointly optimze the coarse grained structure module and the all-atom structure module?\n- In this study, antibodies are generated in a masked-language-model style. Can the model generate longer proteins, like EvoDiff, DPLM?\n- Seems that FlexProtein beats AF3 in several tasks (Fig 4, Table 5), is this due to the less usage of information in AF3?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This study builds a 3B protein foundation model and tests on 12 downstream tasks. The experiments are very comprehensive and the results are promising.\n- FlexProtein enables codesign of sequence and structures for antibodies/nanobodies, which may surpass its MSA-free counterparts since these models may struggle due to few MSA data.", "weaknesses": "- Lack of ablation study for several key components.\n\t- The training objective: how does plddt-based reweighting affect the performance?\n\t- Pretraining tasks: there are three pretraining modes: mode 1 (seq-to-structure), mode 2 (coupled perturbation), and mode 3 (seq-masked global perturbation). How does the balance of these modes change the final results?\n- Potential data leakage. In Figure 5, are the two proteins (4ake, and 2eck) already contained in the PDB dataset?", "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": "tAS9EqGepy", "reviewer": "Reviewer_8Gx5", "summary": "This paper introduces FlexProtein, a unified framework for joint sequence-structure modeling. The method integrates a diffsuion-based structural denoising model with structure-informed masked language model to enable bidirectional learning between sequence and structure.\nIt consists of three modules: (1) a sequence encoder that performs MLM (2) a coarse-grained structure module that learns residue-level topology with noisy structure and sequence (3) an all-atom structure module that refines atomic coords.\nThe authors finetuned on several design tasks including antibody and nanobody design, intermolecular interaction prediction and protein function prediction and show great results.", "questions": "See weakness above. I will raise the scores accordingly if the experiment concerns could be solved.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper proposed a unified modeling of sequence and structure. The integration of structure-informed MLM with diffusion enables the model to simultaneously reason over both modalitiies.\n2. This paper also unified interface for proteins and small molecules. This allows straighfoward protein-ligand binding affinity/docking prediction.", "weaknesses": "1. Lack of comparison with other models. According to the architecture, it looks like FlexProtein should be able to perform structure prediction and inverse folding. However, there is no any benchmark on that. I suggest the authors can compare against DPLM-2 on these two tasks.\n2. Lack of ablation study. In this paper the authors introduce three modules, while the authors did not show any ablation study on these modules. For example, the coarse-grained structure module can be removed according to the framework. Will there be any difference? The authors did not show that.\n3. The authors propose the idea of \"structure-informed masked language model\" but did not cite the paper that exactly used structured-informed protein language model for protein design. [1]\n\nThere are also issues with the presentation of the paper:\n1. In Figure 1, the authors draw a line from $D _ \\theta(R_t, t)$ to single step. I was confused when I first saw this. This should refers to the inferece stage, right? While the loss indicates it's training stage. I suggest the authors seperate the training and inference stages.\n2. In line 171, the authors wrote \"For small molecules, we incorporate 2D topology\". While this can't be found in the framework. I suggest the authors should add the details about small molecules are added to the framework in the figure.\n\n[1] Zheng, Z., Deng, Y., Xue, D., Zhou, Y., Ye, F., & Gu, Q. (2023, July). Structure-informed language models are protein designers. In International conference on machine learning (pp. 42317-42338). PMLR.", "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": "TKxBBg2gpP", "reviewer": "Reviewer_wgxZ", "summary": "The paper presents FlexProtein, a joint sequence–structure pretrained model for proteins that removes dependence on multiple sequence alignments.\nIt integrates a diffusion-based structural generator and a structure-informed masked language model (SIMLM) into a unified framework.\nThe framework is trained on ~78M AFDB and 180k PDB structures, and a pLDDT-weighted denoising loss enables the model to learn reliable geometry from noisy predicted structures.\nExperiments across 12 benchmarks show the performance of the proposed method.", "questions": "I found the paper technically strong and conceptually consistent, my main questions are reflected in the weakness section.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. This paper is well motivated, this paper addresses a well-defined and flexible problem: learning unified protein representations without relying on MSA\n2. The proposed framework combines diffusion-based structure denoising and structure-informed masked language modeling achieving bidirectional supervision between sequence and structure\n3. Extensive experiments across 12 heterogeneous benchmarks demonstrate the performance and generalization ability of proposed FlexProtein\n4. Specifically, FlexProtein achieves consistent gains in flexible or high mutation regimes, where most MSA-based or sequence-only models degrade sharply, providing strong support for the MSA-free framework.", "weaknesses": "Overall, I find this paper well-motivated, technically mature, and experimentally comprehensive. In my view, this work represents a solid contribution to the emerging direction of general-purpose protein foundation models. I have only 1 point of technical curiosity:\n* Given the scale of the training setup and the diffusion architecture, I am curious about the inference efficiency and fine-tuning cost. While the paper provides strong empirical evidence across 12 benchmarks, it would be valuable to include quantitative runtime or memory profiles. This information would help assess FlexProtein’s practicality for real-world downstream tasks.", "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": "TPJk7n0UZJ", "reviewer": "Reviewer_BQ5o", "summary": "FlexProtein unifies protein structure prediction and design. It learns to model the sequence via masked language modeling and learns to model the structure via diffusion-based denoising. This flexible framework is able to generate both structures and sequences and show impressive results on interface, interaction and function prediction.", "questions": "How does FlexProtein perform on monomeric protein structure prediction against ESMFold and AlphaFold?\n\nHow does FlexProtein compare to IgML on design when used in the sequence-only model?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The framework of co-generating sequences and structures is very interesting and an intuitive framework.\nThe authors show that interesting results on interface, interaction and function prediction tasks.", "weaknesses": "While the downstream tasks are very interesting, a framework that \"unifies protein structure prediction and design\" should evaluate on structure prediction.\n\nThe abstract mentions that \"Sequence-only language models (e.g., ESM-2) capture sequence semantics at scale but lack structural grounding\". However there are many works that aim to bridge this gap.\n\n[1] Learning protein sequence embeddings using information from structure. Tristan Bepler, Bonnie Berger.\n\n[2] ProstT5: Bilingual Language Model for Protein Sequence and Structure. Michael Heinzinger,  Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost.\n\n[3] Distilling Structural Representations into Protein Sequence Models. Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Krähenbühl, Adam R Klivans, Daniel J Diaz.\n\n[4] Structure-Informed Protein Language Model. Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang.", "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)