Multi-state Protein Sequence Design with DynamicMPNN
Authors: Alex Abrudan
Sebastian Pujalte Ojeda
Chaitanya K. Joshi
Matthew Greenig
Felipe Engelberger
Alena Khmelinskaia
Jens Meiler
Michele Vendruscolo
Tuomas Knowles
Venue: ICLR 2026 Poster (2026)
Keywords: Protein Design
AI
BioML
Protein Dynamics
Multi-state proteins
GNN
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We develop a GNN-based inverse folding model for multi-state proteins.

Abstract: Structural biology has long been dominated by the one sequence, one structure, one function paradigm, yet many critical biological processes—from enzyme catalysis to membrane transport—depend on proteins that adopt multiple conformational states. Existing multi-state design approaches rely on post-hoc aggregation of single-state predictions, achieving poor experimental success rates compared to single-state design. We introduce DynamicMPNN, an inverse folding model explicitly trained to generate sequences compatible with multiple conformations through joint learning across conformational ensembles. Trained on 46,033 conformational pairs covering 75% of CATH superfamilies and evaluated using Alphafold 3, DynamicMPNN outperforms ProteinMPNN by up to 31% on decoy-normalized RMSD and by 12% on sequence recovery across our challenging multi-state protein benchmark.

Published as a conference paper at ICLR 2026
MULTI-STATE PROTEIN SEQUENCE DESIGN WITH DYNAMICMPNN
Alex Abrudan;"
Sebastian Pujalte Ojeda; Yusuf Hamied Department of Chemistry University of Cambridge
Yusuf Hamied Department of Chemistry University of Cambridge
Cambridge, UK (aca41)@cam, @cam. ac uk
Cambridge, UK (sp2120) ) cam. ac.
Chaitanya K. Joshi
Matthew Greenig, Yusuf Hamied Department of Chemistry University of Cambridge Cambridge, UK
Department of Computer Science and Technology University of Cambridge Cambridge, UK
Felipe Engelberger
Alena Khmelinskaia
Jens Meiler
Institute for Drug Discovery Leipzig University Leipzig, Germany
Department of Chemistry
Institute for Drug Discovery Leipzig University Leipzig, Germany
Maxiig-aaximilans-- Miniveilan--niiversitty Munich, Germany
Michele Vendruscolo
Tuomas P. J. Knowles
Yusuf Hamied Department of Chemistry University of Cambridge Cambridge, UK
Yusuf Hamied Department of Chemistry University of Cambridge Cambridge, UK
ABSTRACT
Structural biology has long been dominated by the one sequence, struc- ture, one function paradigm, yet many critical biological processes from en- zyme catalysis to membrane dransport-depend conformational states. Existing multi-state design approaches rely on post-hoc aggregation of single-state predictions, achieving poor experimental success rates compared to single-state design. We introduce DynamicMPNN, an inverse fold- ing model explicitly trained to generate sequences compatible with multiple con- formations through joint learning across conformational ensembles. Trained on 46,033 conformational pairs covering 75% of CATH superfamilies and evaluated using Alphafold 3, DynamicMPNN outperforms ProteinMPNN by up to 31 % on decoy-normalized RMSD and by 12% on sequence recovery across our challeng- ing multi-state protein benchmark.
proteins that adopt multiple
INTRODUCTION
A commonly derived assumption from Anfinsen's experiment is that proteins adopt only one native 3D structure, leading to the "one sequence, one structure, one function" canon. This view has been indirectly reinforced by the predominant
of X- X-Ray crystallography in experimental protein structure determination, which requires that proteins form a homogenous, diffractible crystal to be characterised (Dishman & Volkman 2018). The large collection of static protein structures in the PDB has enabled the development of high-accuracy machine learning models for tasks such as structure prediction (Jumper et al. 2021 Kryshtafovych al. 2019) and inverse folding (Dauparas) et al. 2022: Hsu et al.] 2022). Amongst contemporary inverse folding models, ProteinMPNN has been particularly widely adopted in applied protein design projects due its low inference costs and
"Equal contribution.

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Transporter
Metamorphic
Hinge
Conformational Ensemble
Extract Backbone Features
Auto- Regressive Sequence Decoder
Predicted
Multi-State GNN Encoder
Sequence QSWYLL...
pool
Decoy Structure
AF3
Structural Self-Consistency
Normalized Scores
Scores RMSD, PLODT
AF3
AF3
Figure 1: DynamicMPNN for multi-state protein design. (a) Examples of proteins with multiple conformational states: transporters closed and open states (PDB: 6NC7, 6NC9), metamorphic protein with alternative folds (PDB: 4QHH, 4QHF) and hinges showing domain movement (PDB: 5DOW, ICFC). (b) Schematic of DynamicMPNN, an inverse folding model trained to generate pro- tein sequences with multiple conformational states. Conformations chemical environments (i.e. interaction partners shown in gray). Solid lines show the flow of in- formation in the model, while dashed lines show the evaluation pipeline using AlphaFold 3 (AF3); employing target structures as templates during inference and measuring the deviations between predicted and target structures, with decoy structures serving as negative controls.
encoded with their respective
robust experimental success rates (Dauparas et al. 2022 Watson al. 2023 Goverde et al. 2024), outperforming traditional physics- physics-based design methods on both fronts (Liu & Kuhlman, 2006).
Although direct experimental characterisation of protein dynamics remains challenge, the formational diversity of proteins underlies crucial biological functions such as enzyme catalysis, protein-protein interactions, allostery, and human disease (Monzon et al. 2016). In applied protein design, bio-switches proteins that switch between two structural states are of particular impor- tance, with key applications engineering artificial bio-motors, signalling pathways, biosensors, or drug delivery systems (Stein & Alexandrov/ 2015 Practorius et al. (023).. While most known switches undergo rearrangements in the context of a single fold (Ambroggio & Kuhlman, 2006a), the class of metamorphic proteins undergo changes in both their secondary structure and fold (Fig. πh) and have been predicted represent up 4% of the PDB chains (Porter Looger 2018 These proteins typically adopt two main functional states (Dishman & Volkman) 2018) and finite number of conformations (see Discussion). Beyond the world of switches, other dynamic proteins characterised by continuous conformational landscapes (c.g. intrinsically disordered proteins Tompa Fuxreiter] 2008)).
Multi-state protein design was first achieved through rational design and physics- methods such as RosettaDesign (Liu Kuhlman |0066] Vucinic et al. 2020 Karimi & Shen 2018). Previous campaigns leveraging these methods have attempted to design metamorphic metal-bindin peptides (Ambroggio & Kuhlman| 20066; Cerasoli et al., (005), closely related sequences that adopt di- verging folds (Wei et al. 2020), and hinge proteins with binder-regulated thermodynamic equilibria,
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allowing the relative populations of different structural states to be modulated by exogenous proteins Zhang et al. 2022; Quijano-Rubio Rubio et al. 202I). More recently, Practorius et al. (2023) leveraged (Dauparas et al. 2022) to link independent backbone states via step-wise logit averaging with shared autoregressive context. Multi-state ESM- IF (Hsu al. 2022) employs analogous strategy, differing primarily in aggregating probabilities (geometric mean) rather than logits (see Appendix A.3). ProteinGenerator (Lisanza et al. (0024) extends this principle to sequence diffusion, averaging logits across distinct structural conditioning inputs at each denoising step.
ProteinMPNN Mtlti-state Design [ProteiMMPNN-MSS
Despite these advances, current multi-state design pipelines have shown limited success. In Hsu et al. (2022), sequences designed with the dual-state strategy only showed marginally lower per- plexity compared to sequences inverse-folded from one of the states and refoldability was not ex- plored. |Preeoorius et al. (2023) used ProteinMPNN-MS
to design a de novo hinge protein with one sequence binding to a peptide: From an initial pool of over 2M computational designs condi- tioned on 28K similar de novo backbone pairs, rigorous set of computational filters yielded only 9K sequences as likely candidates; experimental validation of few sampled candidates showed a high success rate (Appendix A.3). Likewise, the authors of ProteinGenerator Lisanza et al. (2024) reported a significantly lower in silico design success rate of 0.05% for their multi-state backbone design task compared rates of 2-10% observed for various single-state sequence design objec- tives using their framework (Lisanza et al., (024).. These observations combined with the relative scarcity of published data on ML-driven multi-state de novo design campaigns suggest that cur- rent ML methods for multi-state protein design have been significantly less successful than their single-state counterparts. We propose that this gap can attributed to limited multi-conformational datasets, weak benchmarks, and the poor performance of folding models in predicting alternative states (Chakravarty et al., (2024). which adversely affects their efficacy as self-coonistency filters in protein design workflows.
Our contributions. bearning-based pipeline for multi-state protein sequence design.
This paper introduces DynamicMPNN (Fig.
[p), a novel geometric deep
DynamicMPNN is the first explicit multi-state inverse folding model for protein design. To train DynamicMPNN, we create new MI.-ready dataset of proteins with multiple con- formations using the PDB and CoDNaS (Monzon et al., (016) databases, and evaluate the method on 96 biologically relevant metamorphic, hinge, and transporter proteins.
We introduce a novel data processing pipeline and architecture able to handle sequence- aligned structural ensembles with heterogencous sequences, enabling us to leverage the conformational diversity across proteins with high sequence similarity.
We propose a multi-state self-consistency metric and benchmark based on Alphafold3 (AF3) (Abramson et al. 2024b) using target structures as templates.
DynamicMPNN improves performance of ProteinMPNN (Dauparas al. 2022 by up to 12% on sequence recovery, as well as 31% and 8% on decoy-normalized self-consistency values of the RMSD and TM-ssorre respectively.
2 THE DYNAMICMPNN PIPELINE
2.1 PROTEIN MULTI-CONFORMATIOOAAL DATASET
While over 900K individual protein chains (sequence-structtuee pairs) are available the PDB, multi-conformatiooall data is far more scarce with only roughly 12,000 NMR-derived protein en- sembles covering just 21% of CATH superfamilies. To overcome this limitation, we exploit the sequence redundancy across the PDB to create build two multi-conformational datasets:
CoDNaS We use the CoDNaS dataset MMonzon et al., 2016), which clusters PDB chains at 295% sequence similarity with unique UniProt IDs per cluster to prevent homologue lcakage, yielding 46,033 clusters with varying numbers of conformations and covering 75% of CATH superfamilies (Fig.
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PDB80 To capture greater conformational diversity within training ensembles, we also employ 280% sequence similarity clusters available from the Protein Data Bank (Berman
ct al. 2000 yielding 46,924 clusters with at least two conformations (Fig. 2
Training-time time data sampling, From CoDNaS, we select the maximum- chain pair from each cluster to maximize conformational signal and reduce alignment artifact (Fig. 24). For PDB80, pairs are sampled with probability proportional to their structural dissimilarity (1 TM-score), bi- asing training toward larger conformational changes (Appendix A.4). We also explore extending the latter approach to and states (Section 2.2.2). though most known multi-state proteins switch between just functional conformations (Dishman & Volkman 2018 Leaver-Fay al. 2011] AI-
berstein et al. 2022), and validating designs beyond two states remains experimentally challenging NNizii 2025). While our architecture supports arbitrary numbers states, for the aforementioned reasons, we focus primarily on two-state design. Molecular dynamics datasets were not used due to limited sequence variability and scarcity of trajectories capturing large conformational changes (Vander Meersche et al., 2023 |Mirarchi et al. (2024); disordered protein simulations (Tesei et al., 2024) were similarly excluded given their continuous conformational landscapes and low structural signal-to-noise ratio compared to globular multi-state proteins (Tompa & Fuxreiter 2008
Dataset splitting. We curate a benchmark composed of four previous studies of proteins with large 2-state conformational changes: (1) 92 metamorphic proteins (Porter & Looger 2018). (2) 91 apo- holo proteins (Saldaño et al. 2022), (3) the OC23 and OC85 open-closed datasets (Kalakoti & Wallner 2025), and (4) 20 transporter proteins (Kalakoti & Wallner 2025). The proteins with the highest inter-state RMSD were assigned to the test set (96 samples), while the rest were assigned to the validation (100 samples). Training clusters were filtered to exclude any with TM-score > 0.4 (Zhang & Skolnick 2004 Xu & Zhang 2010) and > 30% sequence similarity to test/validation structures, preventing structural similarity leakage and yielding a final training set of 44,243 con- former pairs. To evaluate designs beyond two states, we additionally curate a set of six proteins with cell-characterized intermediate or flexible conformations: MBP (Wang al.) 2012), a-hemolysin (Chatterjee et al. 2025), and Selecase Peleez- et al. 2013) (stable intermediates; 3-states), and Calmodulin, a-synuclein (Chen et al. 2021), and A,342 (fexxble/disordeeedd; 5-states).
We additionally curate a set of single-state sequennc-strructue pairs from the 30% sequence sim- ilarity clusters not represented in our multi-conformational training dataset (n 27,394) 394). This augmentation strategy maximizes coverage of protein fold space while preserving the multi- conformational learning signal. See Appendix A.4 for further details on dataset composition.
2.2 DYNAMICMPNN FOR MULTI-STATE INVERSE FOLDING
Single-state inverse folding methods seek to model the conditional distribution p(Y[X) where X E R=x33x3 represents protein backbone with ni residues, and Y = (y:,...,
:......:.ee. Vn.) is the amino acid sequence. Extensions of these methods to multi-state design have thus far been limited to post- hoc/decoder-level aggregation (A.3) of independent single-state predictions. We believe such meth- ods favour logits highly biased towards one conformation, whose average
the states is higher than the one of moderately valued logits across both states (Joshi et al. 2025). Instead, Dynam- icMPNN learns the joint conditional distribution of pY 191 X m) directly through autoregres- sive sequence generation, where {Xi,..., Xm represent distinct protein conformations encoded into a shared latent space; thus learning sequence distribution that simultaneously satisfies multi- ple structural constraints. We decompose this joint conditional probability using the autoregressive factorization:
p PYY X II p p(vl (olalli--- (-., V15 Xx, X1:.., X,a)
where each factor represents the probability of selecting residue y given the sequence prefix and the complete structural ensemble.
Note that aligning more sequences introduces additional gap tokens (i.e. reducing sequence-structure over- lap) diluting the conformational signal.
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Overall architecture. DynamicMPNN independently encodes each of the functional states of protein, together with their binding partners, into a shared latent feature space (Fig. [p). Embeddings of the chains-to-be-desiee lo-be-designed are then pooled across conformations to obtain single embedding from which a sequence is auto-regressively generated.
Our architecture builds upon gRNAde (Joshi et al., 2025 multi-state GNN model for RNA inverse folding. For both encoder and decoder, we employ -qquivaauva Geometric Vector Perceptron Jing al., 2021 layers which maintain computational efficiency through edge sparsity (k-NN edges with k=32). Within the GVP, scalar features 8; E R and vector features U, R x3 are defined for each node Duval et al. 2024):
mi m, = Σ
MSG (8,, (,), (8), ;j),)s) eis)
jEM
2 = UPD (s;, 8.) (m, m.))
(3)
where MsG, UPD are Geometric Vector Perceptrons generalization of Multi Layer Perceptrons that takes as input and updates scalar and vector features along separate channels in order to achieve -qqiivarrar message passing. The overall GNN encoder is SO(3) -eg))-iuuvariiant due to the use of innlection-sessitvvy imput features (dihedral angles) combined with ((3) -equivariant GVP-GNN layers Joshi al. (2025). Both the encoder and decoder assigned 8 GVP-GNN layers, following findings in Hsu et al. (2022) (2022). See Appendix A.5 for further details.
We present two different encoder architectures:
DynamicMPNN uses independent encoder channels for each conformation, followed by Deep Set pooling (Zaheer et al. 2017) it is invariant to conformation order and does not add extra parameters to the model. We note that while some more expressive pooling strategies have been shown to provide marginal performance improvements, they usually come at great cost efficiency (Joshi et al., 2025). Only node features are updated.
DynamicMPNN + DSS implements cross-attention between the encoder channels after each layer using a Deep Symmetric Set (DSS) (Maron al. 2020) module, which allows for richer inter-conformation interactions at the cost of increased computational cost. In the scatter/gather DSS strategy, node embeddings of all design chains are averaged, passed through GVP layers, and added back to features of each channel through residual con- nection. Both node and edge features are updated.
Heterogeneous sequence processing. key architectural contribution is our handling of non- identical sequences across conformations, necessary for exploiting the full conformational diversity in the PDB. Aligning non-identical sequences introduces gap tokens, and X-ray structures often con- tain unresolved residues; together resulting in ensembles of heterogeneous composition (i.e. varying lengths, missing residues, alignment gaps). We address this with the following protocol: (1) cluster chain members are aqquence-alignee prior featurization; (2) paired PDB complexes are featurized and encoded independently; (3) gap positions are masked and excluded from message passing; (4) during pooling, embeddings from all cluster members are extracted, stacked, and pooled, with gap- node embeddings zeroed out. This preserves all available structural information while incorporating the context of the surrounding chemical environment (i.c. binding partners). To prevent Icakage, during training we mask sequence information for chains with > 70% similarity to the ground truth.
2.2.1 MULTI-CHAIN STRATEGY
Unlike previous work (Joshi et al. 2025). DynamicMPNN processes full PDB entries containing sampled cluster chains, enabling the model to condition conformational changes on binding partners and oligomeric states. This opens possibilities for engineering controllable conformational switches by tuning free energy differences between folds and their binding interactions (Alberstein et al.) 2022). Note that while explicitly 2-state approach, multi-chain training implicitly exposes the model to additional states when PDB entries contain multiple cluster conformations (Fig. 2i). As discussed in Section 2.1 most multi-state proteins switch between only two conformations (Dish- man & Volkman 2018 Leaver-Fay et al., 2011 Alberstein et al. 2022), making 2-state design the most biologically relevant regime.
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a
Conformer
PDB
Sequence Aligned Structural Ensembles
Pairs WYLGCK
Highest RMSD Pair
WYL--K
Clustering
b
c
6303 43
03
2.4
1.3
15
150
Figure 2: Multi-state protein dataset. (a) Data processing pipeline: structures from the PDB clustered by sequence identity at given threshold (e.g. 80%), to form sequence aligned structural ensembles and the highest-RMSD pair is selected from each cluster for training. (b-d) Dataset statistics for PDB clustered at 80% sequence identity (see Appendix [.4]for details). Distribution of the maximum Ca-RMSD (b) and minimum TM-score (c) between pairs of structures in each cluster. (d) Distribution of the number of conformations per cluster.
2.2.2 SINGLE-CHAIN STRATEGY
To efficiently run ablations for k 2 states staies-trainnng time and memory scale linearly with the num- ber of encoded states we implement single-chain strategy encoding only cluster chains without binding partners. Single-chain training performed on PDB80, which provides sufficient cluster diversity for sampling A E {2, 3, states via TM-score weighted sampling. We evaluate these models on 6 proteins with well-characterized multi-state behavior (Tables 66] 7 and directly com- pare k=2 single-chaie versus multi-chain performance (Table 3). Extending multi-chain training to > states is straightforward and left for future work.
2.3 MULTI-STATE DESIGN EVALUATION
Given the high degeneracy of the sequence-to-structuue mapping. where divergent sequences can adopt identical folds (Rost 1999 Sander & Schneider 1991). sequence recovery is often insuf- ficient metric for design success. Therefore, following previous work Wang et al. (2023), we also evaluate the refoldability of generated sequences (Appendix (A.2). Existing refoldability methods compare target structures to single conformations predicted by folding models such as AlphaFold2 Jumper al. 202T). We argue that this unconstrained approach is unsuitable for multi-state de- sign since folding models typically predict one dominant state or an unphysical interpolation. failing sample the full conformational ensemble Lane (2023): Chakravarty et (2024); Saldaño et al. (2022).
Template-based Alphafold refoldability. To address these sampling limitations, we propose template-bass based AlphaFold 3 (AF3) framework Abramson et al. (024b) to explicitly verify structural compatibility we adapt the findings of Roney & Ovchinnikov (2022) to our multi-state setting (see Appendix A.6). While unconstrained DI. models may fail to spontaneously find alternative states, we leverage AF3's template mechanism to assess the compatibility between the designed sequence and true template. For each de novo sequence, perform 2 AF3 runs, with each conforma- tional state provided as template, shifting the evaluation from structural prediction to compatibility assessment. The similarity Ca-RMSD TM-score (Zhang & Skolnick 2004). between predicted structures and the ground-truth templates, along with AF3 confidence scores, serve as a proxy for the likelihood that the designed sequence adopt the target states.
Formally, for a protein with conformational states X = Xx...,,,,, and designed sequence Y, define the AF3 RMSD for each target conformation X₃

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(4) where teeemptttee xx) is the structure predicted by AlphaFold3 for sequence Y when a tem- plate of Xx is provided. To account for the structural bias induced by the template, we define normalization strategy to contextualize observed deviations:
AF3 AFtemplatte Xx) = (M
We provide AF3 structurally dissimilar decoy structures as templates (TM-score < 0.44 using the same sequences designed and measure the resulting devia- tions. This control assesses whether sequences fold specifically into their targets or compatible with arbitrary structures:
Decoy normalization (Decoy Norm):
AFtemplate (Y,
RMSD decoy (Y, Xx; D) AF Ae3empplate (Y,D)
where D is decoy that is structurally dissimilar to Xg. As noted in Roney Ovchinnikov (2022), when provided false decoy, AF2 globally aligns its prediction with the decoy, but it senses the local inconsistencies between the sequence and decoy backbone structure AF2 therefore relaxes the structural prediction locally with respect to the decoy and correctly yields low self-consistency metries. Additionally, we measure PLDDT confidence scores to evaluate AF3 fold uncertainty. High RMSD with low pLDDT indicates poor template matching, while low RMSD with high PLDDT suggests a successful design. The same decoy normalization strategy is also applied to TM-score and pLDDT metrics.
Template-free baseline. To quantify the impact of template conditioning, we additionally evalu- refoldability without structural guidance by omitting the template input in AF3 (i.e. standard AF3 pipeline). Comparing these predictions against both target conformations reveals: (1) whether templates overly bias predictions toward target conformations, and (2) whether AF3 can recover alternative states or collapses to a single dominant structure.
Generative ensemble evaluation. As an orthogonal evaluation approach, we employ BioEmu (Lewis et al. 2025). generative model trained to sample from protein conformational equilib- rium distributions. BioEmu represents a class of emerging generative models specifically trained to approximate conformational ensembles. We selected BioEmm over alternative ensemble gener- ators such as AlphaFlow (Jing et al. 2024) or PepFlow (Abdin Kim 2023) as represents the current state-of-the-art (Jing et al. 2025), trained on orders of magnitude more data than competing methods, including molecular dynamics equilibrium distributions and conformational clusters from AFDB (Varadi al. 2024). However, current ensemble generation methods like BioEmu and Al- phaFlow are restricted to single protein chains, precluding evaluation of refoldability in the presence of binding partners that often drive conformational changes and likely underestimating our designs' true multi-state capacity. For each designed sequence, we sample 50 structures (Fig. (5) and measure the maximum TM-score and minimum RMSD achieved against each target conformation.
Table I: Refoldability performance comparison of DynamicMPNN model variants on multi-state protein design benchmark (n 96). Raw metries show absolute performance values, while normal- ized metrics show performance relative to random decoy structures.
Raw Metrics
Decoy-Normalized Metries
Model Variant
PLDDT
RMSD (Å) |
TM-score
PLDDT
RMSD
TM-score
Combined Training
82.08 (7.62)
2.35 (2.51) 0.870 (0.162)
1.354 (0.410) 0.124 (0.130) 6.684 (1.485)
Combined Training + DSS
81.61 (7.23)
2.56 (2.36)
0.862 (58)
1,398 (0.442) 0.131 (0.125) 6.627 (1.514)
Single Training
68.35 (15.46)
8.16 (9.96)
0.652 (0.322)
1,383 (0. 467) 0.348 (0.336) 4.830 (2.285)
Sampled Pair Training
82.26 (7.61)
2.29 (1.92)
0.872 (0. 161)
1.470 (0.466) 0.125 (0.129) 6.630 (1.506)
Sampled Pair Training + DSS
81.88 (7.85)
2.45 (2.33)
0.865 158)
1.436 (0.470)
0.127 (0.121) 6.668 (1.466)
RESULTS AND DISCUSSION
Setup. We evaluate how exposure to multi-state training data affects design performance. Our pri- mary comparison is between Single Training (single-state pairs only, analogous to standard inverse- folding models) and Combined Training (mixing single-state and multi-state samples). We addi- tionally explore multi-staged training where single-state pretraining precedes multi-state finetuning (Multi-Finetuning.
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Table Refoldability performance comparison of DynamicMPNN model variants on template-less multi-state protein design benchmark (n 96).
Template-less both states
Template-less worst state
With Template worst state
PLDDT 1 RMSD TM-score
PLDDT1
PLDDT
Combined Training 81.87 (7.99) 4.38 (4.05)
0.78 (0.20) 79.72 (9.89)
6. (5.92)
0.89 (0.27)
80.43 (9.50) 3.05 (3.41)
0.82 (0.22)
Single Training
70.11 (12.41)
10:87 (10.48)
0.54 (0.31)
67.65 (13.7)
0.47
66.49 (16.09) 9.34 (10.52)
0.59 (0.35)
Table 3: Sequence recovery performance comparison across DynamicMPNN model variants and ProteinMPNN baseline on multi-state protein design benchmark.
Model Variant
Sequence Recovery (%) 1
Combined Pretraining Multi Finetuning
42.7 (8.8)
Single Pretraining + Multi Finetuning
42.1 (8.3)
Combined Pretraining Multi Finetuning + DSS
41.9 (8.6)
Combined Training
41.0 (8.5)
Single Pretraining + Multi Finetuning + DSS
40.3 (8.2)
Combined Training + DSS
38.8 (7.8)
ProteinMPNN MSD*
38.0 (11.0)
Single chain 2-state
37.35 (9.04)
Single Training Single Training DSS
27.1 (9.4)
26.2 (8.5)
Table 4: Performance comparison of DynamicMPNN variants and ProteinMPNN baseline on subset (n 61) of multi-state protein design benchmark. Standard deviations shown in parentheses. Note that ProteinMPNN MSD's 's handling of gap tokens and missing residues (i.e., X tokens) limited the number of designs that could be refolded using AF3, necessitating separate comparison.
Raw Metrics
Decoy-Normalized Metries
Model
PLDDT
RMSD (Å)
TM-score
PLDDT
RMSD
TM-score
Combined Training
82.22 (6.76)
2.27 (1.59)
0.849 (0.166) 1.266 (0.332) 0.129 (0.109) 6.482 (1.340)
Combined Training + DSS
82.00 (6.66)
2.65 (2.40)
0.836 (0. 176)
1.286 (0.336) 0.144 (0.126) 6.513 (1.586)
Single Training
69.35 (14.45)
8.00 (9.44)
0.623 (0.324)
1.286 (0.396) 0.376 (0.338) 4.455 (2.186)
Sampled Pair Training
81.89 (7.36)
2.28 (1.75)
0.850 (0.179)
1.370 (0.40) 405) 0.134 (0.127) 6.449 (1.521)
Sampled Pair Training DSS
82.45 (6.96)
2.58 (2.47)
0.839 (0.1
1.341 (0.431) 0.139 (0.120)
6.529 (1.570)
ProteinMPNN MSD*
79.55 (9.75)
3.31 (2.88)
0.806 (0.207) 1.326 (0.348) 0.187 (0.191) 6.054 (1.771)
Table 5: BioEmu refoldability comparison of DynamicMPNN model variants on test set (n = 96).
Standard deviations in parentheses.
Performance Metries
Success Rate (%)
Model Variant
TM-score 1
RMSD (Å) 4
TMS
TMSp.s 1
TMS t
Combined Training
0.623 (0.230)
5.66 (4.82)
37.5
22.9
11.5
Single Training
0.394 (0.252)
13.28 (10.27)
17.7
7.3
3.1
Table Comparison between 2-state and 3-state single chain models with average pooling or DSS for 3 curated 3-state proteins.
Sequence Recovery (%)
TM-score
Protein
2-state
2-state DSS
3-state
3-state DSS
2-state
2-state DSS
3-state
3-state DSS
MPD
(1.01)
(1.03)
51.3 (0.98)
56.1 (1.07)
00.93 (0.04)
0.90 (0.04)
0.88 (0.02)
0.89 (0.04)
Selecase
42.9 (2.81)
(2.211
(1.22)
45.0 (1.90)
0.77 (0.03)
0.81 (0.01)
0.81 (0.03)
0.81 (0.01)
a- ahhemolysin
32.3 (0.87)
(1.24)
33.0 (1.10)
32.4 (0.92)
0.93 (0.03)
0.91 (0.03)
0.94 (0.02)
0.91 (0.03)
We train multi-conffrmatiooaal datasets and explore several architectural variations:
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Table Comparison between 2-state and 5-state single chain models with average pooling or DSS for 3 curated highly flexible proteins.
Sequence Recovery (%)
TM-score 1
Protein
2-state
2-state DSS
S-state
Sistate
DSS
2-state
2-state DSS
5-state
5-state
DSS
A33-2
27.8 (2.25)
9.8 (1.21)
17.4
16.8 (1.74)
0.08 (0.05)
0.16 (0.01)
0.08 (0.04)
0.07 (0.05)
o-symuclein
15.7 (1.20)
15.8 (1.47)
17.6 (2.80)
24.2 (1.71)
0.07 (0.02)
0.10 (0.03)
0.15 (0.01)
0.12 (0.08)
Calmodulin
34.2 (1.10)
34.0 (1.48)
43.4 (0.80)
43.0 (1.09)
0.47 (0.15)
0.46 (0.11)
0.51 (0.05)
0.58 (0.05)
CoDNaS: Multi-chain models encoding full PDB complexes including binding partners. Maximum RMSD conformer pairs pre-selected per cluster prior to training.
PDBS0 (Sampled Pair Training): Multi-chain models with TM-score weighted sampling to dynamically select conformer pairs during training, capturing greater conformational diversity.
Single-chain ablations: To efficiently ablate designs beyond two states, we train single- chain models (encoding only target chains without binding partners) on PDB80, dynami- cally sampling up to k E (2,3,5) conformational states per cluster using the same TM- score weighted scheme.
We also compare architectures with and without DSS cross-attention modules across these configu- rations. All models were trained either 8 A A100-80GB or 8 H100-80GB GPUs using a batch size of 32 and Adam (Kingma 20144) optimizer with learning rate 10-3 Training for each stage was run until convergence of performance on the validation set, typically after 20-40 epochs (*=17-34 hours).
Then, DynamicMPNN models and ProteinMPNN (using Multi-state Design inference strategy) were used sample 16 sequences for each of the 96 benchmark test proteins (see Table for DynamicMPNN inference costs and model size).
Evaluation protocol. For each designed sequence, we evaluate refoldability using the template- based pipeline described above, sampling 5 structures per sequence. For each target conformation, we select the best-performing sample (i.e., lowest RMSD, highest TM-score, highest pLDDDT), then aggregate metrics by averaging across the 16 designed sequences. The same protocol is applied with decoy templates for normalization.
Additionally, for sequences designed using Combined Training and Single Training models, we con- duct refoldability evaluations with BioEmu and template-free AF3. For BioEmu (Lewis et al, 2025), sample 50 structures per sequence and report the best-performing sample per target conforma- tion averaged across designs. We define success rates (TMS,) as the percentage of targets for which least one designed sequence has both conformational states recovered with maximum TM-score (across the sampled ensemble) exceeding threshold 7 E {0.7, 0.8, 0.9].
For template-free free AF3, apply the same evaluation protocol as template- eemplate-based AF3. To assess whether designs consistently recover both states, we additionally report worst-state metrics-the performance whichever conformational state is harder to recover for each design-for both template-based and template-free evaluations (Table
DynamicMPNN outperforms existing benchmarks across multiple evaluation metries. Our best-performing model achieves substantial improvements over baseline methods: a 31 % reduction in neroy-normalized RMSD (Tab. 4) and 12% improvement sequence recovery (Fig Tab. compared to ProteinMPNN Multi-State Design (MSD). This performance gain is particularly noteworthy considering that ProteinMPNN's training dataset contains proteins within 30% sequence similarity clusters of 91 out of 96 benchmark proteins, making our improvement on this established baseline significant. We leave the retraining of ProteinMPNN on a rigorous train-test split for future work.
Combined and multi-state finetuning training strategies far outperform the single-state only training. A more rigorous comparison involves single-state trained model, which adheres to the same training-test split and thus eliminates potential data leakage concerns. Models trained only on single-state data (Single Training) perform poorly on multi-state design they are unable to create and decode a meaningful latent representation of the conformational changes. The Combined Train- ing and approaches, which exposes models multi-state examples during training, achieve the opti-

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Published as a conference paper at ICLR 2026
mal balance and consistently outperfonm Single Training as well other models across all metries. (Tab. 3 Ip. Some likely reasons to explain the under performance of our Single Training check- point with respect to ProteinMPNN is the aforementioned unfair train splitting of ProteinMPNN as well as the multi-state latent space embeddings being out-of- duttr--distributionn for the decoder parameters that have only been trained single state data. Additionally, versions trained on PDB80 with TM- weighted sampling slightly outperform those trained on CoDNaS with MAX TM-score sampling, likely due to the increased diversity of conformational pairs seen during training 1
Fold-switch designs show best improvements over ProteinMPNN. Analyzing performance strat- ified by the protein class in the test set (i.e. metamorphic, hinge, and transporter), (Fig. Gb. b), we observe that DynamicMPNN's advantage over ProteinMPNN is most pronounced in fold-switching proteins (metamorphie). This shows that DynamicMPNN can effectively fit the most complex con- formational changes that undergo complex rearrangements of both tertiary and secondary structure (Dishman Volkman 2018). The distributions of AF3-predicted TM-scores over the inter-state
TM-scores are plotted in Fig. 7
Template-free AF3 is not suitable for evaluating multi-state designs. As has been previously shown for AlphaFold 2 (AF2) Chakravarty & Porter (2022), without structural guidance, AF3 collapses to a single dominant conformation, with worst-state TM-scores degrading from 0.82 (template-baseet to 0.69 for Combined Training designs (Table 2 This inability to recover al- ternative states motivates our semplate-basee evaluation framework: we expect AF3 to be reliable with respect to sequence-template folding compatibility estimation, based on on|Roney& & Ovchinnikov 2022).. Notably, Combined Training outperforms Single Training across all conditions- free average (0.78 vs 0.54), worst-state (0.69 vs 0.47), and template-based (0.82 vs con9i-mm ing multi-state training benefits persist regardless of evaluation protocol.
BioEmu mirrors AF3-template results. Both AF3 and BioEmu evaluation frameworks demon- strate consistent model rankings and highly correlated refoldability scores (Pearson r = 0.71 for TM-score, r 0.79 for RMSD; decoy-normmlliedd
0.58, r 0.61). with Combined Training consistently outperforming Single Training. This further emphasizes that regardless of the in silico evaluation metric that we used, models trained with multi-state data consistently outperform those without.
The optimal number of states is highly dependent on the protein system. While 2-state mod- elling is the most biologically relevant approach for most multi-state proteins, a question worth exploring is whether DynamicMPNN can be generalised to proteins with metastable states, interme- diates even proteins with flatter energy landscapes. We compared the performance of the 2-state single chain model to the 3-state and 5-state respectively on proteins each: (1) 3 proteins with main states with stable intermediate (MPD, a-hemolysin, Selecase Table 6 (2) 3 highly flexible proteins, either with disordered linkers (calmodulin) or fully disordered and which fold upon bind- ing (a-synuclein, A,8-42)- Table 7 Results show how including extra states can be both beneficial (calmodulin) as well as detrimental (MPD), as the model has to learn to reconcile more diverse struc- tural information. The inclusion of DSS is equally inconclusive. This is probably due to the lack of such proteins in the training set. To assess whether the single chain nature of these models impacts the metrics, we also compare the 2-state single chain to the 2-state multi chain model, observing a moderate albeit expected drop in performance (Table 3)
4 CONCLUSION
We present DynamicMPNN, the first explicit multi-state inverse folding model, achieving up to 31% % improvement over ProteinMPNN on our multi-state benchmark. By jointly learning across confor- mational ensembles rather than aggregating single-state predictions, DynamicMPNN captures se- quence constraints required for multiple functional conformations. Critically, models trained with multi-state data consistently outperform single-state trained models across all evaluation metrics (template guided AF3, AF3 template-free, and BioEmu) demonstrating robust benefits of explicit multi-state training regardless of evaluation framework. This opens possibilities for engineering synthetic bioswitches, allosteric regulators, and molecular machines. It is unclear if the presented one-size-fitt -its-all approach to multi-state design will be effective experimentally, if specialized models for different classes of conformational changes depending on their thermodynamic com- plexity will be beneficial.
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SOFTWARE AND DATA
We provide the code for our model in the following repository: wac.uu..omm/lcc.. Atthub.com/Alee-Arruuaa WynamicMPNN
ACKNOWLEDGEMENTS
The authors would like to thank Rohith Krishna, Adam Broerman, Kai Yi, Simon Mathis, Vladimir Radenkovic, Michaela Brezinova, Gustavo Parisi, and Pietro Liô for insightful comments and dis- cussions. SPO was supported by the Una and Derek Finlay scholarship. CKJ was supported by the A Singapore National Science Scholarship (PhD). AK was supported by the Graduate School of Quantitative Biosciences Munich (QBM). DynamicMPNN tional resources provided by the CSD3 Cambridge HPC cluster and Modal's GPU infrastructure.
trained using computa-
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A APPENDIX
A.1
LLM ACKNOWLEDGEMENT
The authors acknowledge that they have used LLMs in the process of paper writing for style sug- gestions and proofreading.
A.2 SUPPLEMENTARY RESULTS
330
381
-28
Figure 3: Overview of CoDNaS dataset. Distribution of the maximum Ca-RMSD (a) and minimum TM-score (b) between pairs of structures in each CoDNaS cluster. (c) Distribution of the number of conformations per CoDNaS cluster.
Table 8: Inference cost for sampling 16 sequences using one NVIDIA A100-80GB GPU and 32 AMD EPYC 7763 CPU cores. Residue count includes target and binding partner chains. Dynam- icMPNN model sizes: 4.86M parameters (+DSS) and 4.23M parameters (no DSS).
Time (s)
Peak GPU Memory (MB)
Total Residues
+ +DSS
No DSS
+DSS
No DSS
250
47.7
28.6
1067.1
534.3
500
86.4
51.7
1568.6
788.2
1000
156.5
93.5
2305.8
1162.9
2000
283.3
169.1
3389.6
1715.6
4000
512.9
305.8
4982.7
2531.1
8000
928.5
552.8
7324.6
3734.3
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05
Recovery
03-
Combined Pnstrining MMuli rineturing
Soggl Peatrinng Muto rinetting
Combined hetrainng Hooli rinetinng uss
Single Pretcaning Mudi finetoning tGS
Conbined tharing
Comblnedi Raining tis
Single mabing
sinle traineg 185
Model
Figure 4: Sequence recovery performance across DynamicMPNN model variants and PrONeinMPNN baseline on multi-state protein benchmark (n 96). Combined training approaches achieve highest performance, with models that only incorporate single state training data performing poorly.
0.66
Curuietive max TM4 scare
Currulative max TMecorre
0.44
0.42
0.64
0.40
0.63
0.38
10
10
20
Number BioEmu samples
Number BlaEmu
Figure 5: Convergence of cumulative maximum TM-score for BioEmu samples of Combined Train- ing (left) and Single Training (right) model sequences. The cumulative maximum across 16 se- quence designs is averaged over all test set targets.
A.3 DETAILS ON PREVIOUS WORK
A.3.1
COMPARISON OF MULTI-STATE INFERENCE STRATEGIES
Both ProteinMPNN-MSD (Dauparas et al. 2022 Practorius et al. 2023) and Multi-state ESM-IF (Hsu et al. 2022 extend single-state inverse folding models to multi-state design tasks using
dentical underlying mathematical strategy: step-wise logit aggregation with shared autoregressive context.
Hsu et al. 2022) formulate the objective as maximizing the geometric average the conditional likelihoods for states A and B. Since sampling only depends relative logit values (softmax
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1.0
Debeaet rel on tohe (Erge prosincs Tanneetters
Dataset Hit seichs
itrga prosates
Tansprttrr
Proteio TM (8-0000
Proteio Tisccoo
0.6
0.0
0.2
0.6
0.8
1.0
0.0
0.2
0.6 0.6
1.0
Thaining
Sampled Training
(a) Combined Training vs Protein-
(b) Sampled Pair Training Pro-
MPNN MSD (n=61)
teinMPNN MSD (n=61)
Figure 6: Comparison between 2 DynamicMPNN versions and ProteinMPNN MSD on protein dlass-stratifies TM-score AF3 refoldability. The classes are Fold-switches (yellow), Hinge proteins (blue; combining the APO-HOLO, OC23, and OC85 datasets), and Transporters (purple).
ning pronns
Niburtin
(c) Combined Training + DSS
(a) ProteinMPNN (n=61)
(b) Combined Training (n-96)
(n=96)
(d) Sample Pair Training + DSS (n=96)
Sample Pair Training (n=96)
(l) Single Training (n=96)
Figure 7: Distribution of Predicted template-AF3 TM-score of samples against the inter-state ground-truth TM-score. All multi-state trained model prediction accuracy correlate well with the inter-state TM-scores.
normalizes), averaging logits before sampling yields equivalent results to the geometric mean of
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a
c
b
d
Figure 8: Switch Arc protein case study. (a, b) ProteinMPNN and (c,d) DynamicMPNN best design structure prediction (pink and salmon, respectively) against both Are states from PDB ID: 1BDT and 1 QQTG respectively (grey). The DynamicMPNN design recapitulates the beta sheet fold (c), but ProteinMPNN designO does not (a).
probabilities:
Logits(y) (x)) + Logits(ye) XT))
P()(XAA) P(v(XD) exp
(6)
Geometric Mean (ESM-IF)
Arithmetic Mean of Logits (PrecinMPNNNMMSD)
Consequently, both methods perform the same operation: averaging the logits from independent structural encoders at step I, sampling token, and feeding that single consensus token back into the autoregressive context for all states step /+1 1.
Weighted aggregation. While Hsu et al. (2022) used unweighted logit averaging, Praetorius et al. (2023) applied their hinge protein design 40%-60% weighting scheme bias designs towards the effector-bound (holo) state.
A.3.2 EXPERIMENTAL VALIDATION HINGE PROTEIN DESIGNS BY PRAETORIUS AL. (2023
Out of the 9K designed sequences using ProteinMPNNNMMSD and Rosetta (Liu & Kuhlman 2006 76 were randomly selected for experimental validation based on RosettaDesign (Liu & Kuhlman 2006) and AlphaFold2 (LLDDD > 92, RMSD 1.5, PAE < (Jumper al. 2021 filters.
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46 out of 79 designs were expressed solubly and predominantly monomeric. Because many of the corresponding effector peptides were insoluble, only 20 out the 46 designs were tested for binding. 9 out of 20 designs showed binding via SEC experiments; advanced structural characterization with DEER and FRET confirmed the presence of the desired conformational change and peptide binding for all 8 and respectively tested designs. in out of these 9 designs.
A.4 DATASET DETAILS
To construct the dataset, we obtained 46,033 Multiple Sequence Alignment (MSA) clusters at 2 95% local sequence similarity from the latest version of CoDNaS (v2025) Monzon al. 2016), including NMR model structures. Importantly, the CoDNaS dataset prevents potential errors created by mixing different homologues in the same cluster by enforcing the same UniProt ID) for all custer members. All available conformations of a protein are included as different experimental condi- tions and sequence variations can reveal distinct thermodynamic states of the same protein (Best et al. 2006). While clusters contain varying numbers of conformations (Fig. 3+) we constructed our dataset using only pairs of chains from one or two PDB entries that have the largest RMSD from each cluster (Fig. Bl). Chosen pairs represent the most distinct conformational states.
While other inverse folding models (Hsu et al. 2022) saw improved performance when trained on orders of magnitude of more protein structures from AFDB, previous studies have found that the majority of high-confidence structures in AFDB map to known CATH superfamilies (Bordin et al. 2023), and that AlphaFold struggles in predicting alternative states (Chakravarty al. (024)). We
therefore decided against including AFDB structures in our training set.
structural redundancy via its lower sequence similarity threshold (80%) at the expense of homology leakage and introduction of muta- tional fold-switches (He al. 2012). These are known adopts one state while related mutants can adopt a different state; unlike environmental switches which are the primary target of our work. We used MMseqs2 easy-cluste: (Steinegger Söding 2017) cluster all deposited SEQRES se- quences in the PDB at 80% sequence identity, retaining only clusters with at least two distinct PDB entries. Clusters were filtered to exclude chains shorter than 30 or longer than 5,000 residues, as well as chains where unresolved residues (i.e. X tokens) constitute more than 40% of the sequence, yielding 46,924 culii-conformationall clusters used for training via TM-score- weiihtee pair sam- pling (Section 2.1 Pairwise Ca-RMSD and TM-score were computed between cluster members using Foldseek (Van Kempen et al. 2024).
PDB80 dataset, the other hand, is meant include
During pair sampling in Sample Pair Training, pair is selected with probability function of the TM-score: for sequential selection of l conformations, we sample each structure with probability proportional to its dissimilarity from diready-selectee structures:
TM.
P(c:)= = exp
Σ EjEc exp
I-TM,
where TM, = |ST E.ES TM(c, s) is the average TM-score between candidate q and the set of
already-selected structures S, C is the set of remaining candidates, and 2.0 is temperature parameter.
A.4.1 DATASET LIMITATIONS
Both datasets contain highly unbalanced conformational populations (Fig. 21): number of clusters contain thousands of sequences, while the majority contain only few. This contributes the relatively small intra-cluster RMSD in the majority of the clusters (Fig 2b), since many alternative protein states live in low populations.
relatively small
A.5 MODEL DETAILS
A.5.1
FEATURISATION SCHEME
We use a similar featurisation scheme as in (Jamasb et al.) 2024). Node scalar features transfommerlike positional encoding in 16-dimensional array; backbone dihedral angles
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6 11,w R the virtual torsion and virtual bond angle E R Node vector features po- sition vectors of Ca. 2 E IR Edge scalar features are established via k-NN (k=16) and the edge length expressed in 32 Radial Basis Functions, tRaF _ well the length of the edge itself. Edge vector features are edge directional unit vectors for both directions Vio = f - zj. To further prevent overfitting on crystallisation artifacts, random Gaussian noise (T = 0, a 0.1Å) was added the coordinates (Dauparas al. 2022)
A.5.2 MULTI-STATE GNN
DynamicMPNN processes one or multiple protein backhone graphs via a multi-state GNN encoder (Joshi et al. 2025). Overall, DynamicMPNN's encoder is equivariant to 3D roto-translation of coordinates as well as ordering of the states in its input. Encoding is followed by pooling node features across states, which is invariant to the ordering of the states, and autoregressive sequence decoding.
When representing conformational ensembles multi-graph, each node feature tensor contains three axes: (#nodes, #conformations, feature channels). Multi-state GNN's encode multi-graphs by performing message passing the multi-graph adjaceney independently process each former, while maintaining permutation equivariance of the updated feature tensors along both the first (#nodes) and second (#conformations)) axes.
A.5.3 GEOMETRIC VECTOR PERCEPTRON LAYERS
Geometric Vector Perceptrons (GVPs) (Jing et al. 2021 are a generalization of MLPs take tuples of scalar and vector features as input and apply 0 equivariant non-linear updates. GVP GNN layers process scalar and vector features on separate channels to maintain equivariance. The node scalars S; _2 node vectors V; E IR and edge scalars eij and vectors ē:j communicate through a message passing operation:
mi m, = Σ GVP ((s;, Ve), (s,, Vy), Cij. T.y).
(Message & aggregate steps)
(8)
JEN;
S, V. GVP ((s;, V.), (m; m.))
(Update step)
The overall GNN encoder is (3)) -qquiivrrar due to the use of reflection-sensitivee input features (dihedral angles) combined with 0 (3)-eqquivariant GVP-GNN layers.
A.5.4 CONFORMATION ORDER-INVARIANT POOLING
After using message passing layers that are conformation order-equivariant, we add conformation order-invariant head, which performs average pooling across the conformation channel of the scalar and vector feature tensors, similar to et al. 2025 (2025); S E R= and V E Baxkxx xm' xy S E Raxm and V E RR x8 where n is the sequence length, & is the number of backbones, m the number of scalar features, and m' the number of vector features. The only pooling strategy used in this work is the pooling of the maximum RMSD pair of chains therefore & = 2 although more pooling strategies for homo-oligomers can be used, such as equal averaging of all chains to be inverse folded in the selected PDB entries.
A.6 METRICS
A.6.1 DECOY-NORMALISED AF3 SELF-CONSISTENCY EVALUATION
AlphaFold as Biophysical Energy Function. While AlphaFold (AF) excels predicting static structures from evolutionary data, it often struggles to spontaneously sample alternative conforma- tional states for single sequence, effectively failing at the global search problem for multi-state proteins. However, recent work by [Roney & Ovchinnikov (2022) established that AF has learned a robust, coevolution-ines -nneeeeddenddeeeedeett biophysical energy function that can accurately score sequence- structure compatibility when the search space is constrained (Roney & Ovehinnikov 2022). 2022) Specif- ically, they demonstrated that when candidate structure is provided as a template, AF's output confidence metrics (pLDDT, pTT and structural consistency (TM-score between input template
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and output) correlate strongly with the actual accuracy of the model, effectively acting state-of- the-art energy scoring function.
Leveraging this finding, utilize AlphaFold 3 (AF3) (Abramson et al. 2024a) not search en- gine to find the conformations, but as a scoring function to evaluate the compatibility of our designed sequences with the specific target geometries. By explicitly providing the target conformational state X₁ as a template, we direct the model to the relevant basin of the energy landscape.
Addressing Template Bias via Decoy Normalization, A critical challenge in template- guided eval- uation is the potential for "template bias," where the model might simply copy the input geometry regardless of the sequence's actual propensity to fold into that state. To distinguish between true sequence-structur compatibility and template memorization, we introduce decoy decoo-normalliztion strategy.
We define a set of decoy structures D that are structurally dissimilar to the target (TM-score < 0.4) but represent valid globular protein folds. For a designed sequence Y and a target conformational state Xg. we calculate normalized score that compares the structural ceff-consistency on the target against the self-consistency on decoy:
Normalized Score Self-Consistency(Y, Xx.)
(10)
Self- Self-Consistency(Y.Du
In the context of RMSD (where lower is better), this formulated as:
RMSD decoy (Y, X4; D) = RMSD(AFF(Y, (x,) RMSD(AF3(Y, D),
(11)
Physical Interpretation. This metric serves as a proxy for the specificity gap or energy gap (AE) between the target fold and competing misfolded states.
High Specificity (Successful Design): The sequence is highly compatible with the target (low RMSD/ / high PLDDDT when prompted with X₂) but incompatible with the decoy (high RMSD/ low pLDDT when prompted with D). This results in favorable normalized score, indicating the sequence "accepts" the target fold and "rejects" the decoy.
Low Specificity Aaalucination/Promiscuigg If the sequence creates low-energy struc- for both the target and the decoy (or high error for both), the normalized score ap- proaches 1.0. This identifies sequences that are either generically "sticky" or for which the model is over-relying on template inputs without sequence support.
By requiring our designs to outperform decoys, we extend the "AF-as-energy--unee functionn" paradigm from simple ranking (as proposed by Roney & Ovchinnikov (Roney & Ovchinnikov 2022 rigorous specificity filter for de novo multi-state design.
22


Reviews:
[{"review_id": "MaKg3jKGnn", "reviewer": "Reviewer_5HE4", "summary": "The paper introduces DynamicMPNN, which designs protein sequences that are compatible with multiple conformational states. Instead of aggregating single-state predictions post hoc, the model jointly learns across conformational ensembles. Using a dataset of 46,033 conformational pairs spanning 75% of CATH superfamilies and an AlphaFold–based evaluation, DynamicMPNN outperforms ProteinMPNN on a multi-state benchmark.", "questions": "Please address the questions in the Weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The motivation is clear: the paper tackles a well-defined gap by explicitly modeling multi-state behavior—a cornerstone of many biological functions—where post-hoc aggregation approaches have historically shown low success rates.\n\n2. The training dataset curation is careful and comprehensive.", "weaknesses": "1. The empirical analysis lacks stratification by biologically and structurally meaningful factors. A breakdown by motion class (metamorphic, hinge, transporter) would contextualize the relatively high absolute RMSDs and reveal where DynamicMPNN provides the largest benefits.\n\n2. The paper does not report scalability or runtime characteristics, leaving unclear the training and inference costs (GPU hours, memory footprint), AF3 evaluation throughput per sequence-state pair, and how computation and memory scale with the number of conformational states m.\n\n3. The demonstrated scope is restricted to two-state systems. Although broader applicability is discussed, there is no evidence on proteins with more than two conformational states or on more continuous conformational landscapes. At least one m > 2 case study would substantiate the abstract’s claim of multi-state generality.\n\n4. The title and text use “protein design” broadly, but the contribution is specifically an inverse folding method for protein sequence design conditioned on multiple conformations. To avoid overclaiming and to align with community terminology, the manuscript should consistently use “protein sequence design” (or “inverse folding”) where appropriate, and reserve “protein design” for pipelines that include backbone generation. The related work should be expanded to cover widely used single-state protein sequence design methods, including but not limited to ProteinMPNN, ESM-IF, CarbonDesign, and GeoEvoBuilder, with a brief comparison of how DynamicMPNN differs (e.g., multi-state conditioning, encoding of binding context, pooling across conformers) and where single-state advances might transfer or serve as baselines.\n\n5. Writing and formatting require polish. The unresolved cross-reference “Appendix ??” (around line 105) should be fixed; the duplicated citation “Praetorius et al. (2023) (2023)” should be corrected.", "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": "xHQxX6LwSu", "reviewer": "Reviewer_8aKx", "summary": "This paper introduces DynamicMPNN, an inverse folding model for generating protein sequence based on multiple conformational states of a given protein. It proposes to cluster sequences and conformations in PDB to build an augmented dataset with conformational heterogeneity for training. It uses template-based AF3 for self consistency refoldability check as a metrics, and have outperformed existing inverse folding baselines.", "questions": "- Since there're models that predict conformational ensembles, e.g. BioEmu, AlphaFlow. Have the authors tested the generated sequences with those models, and compare with the input structures?\n- While during the training, the model uses pairs of conformations from the augmented dataset. However, how much those pairs would match the distribution of practical design demands is questionable.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- Develop a novel method to include explicit multi-state conditioning in the model\n- Design a new data processing pipeline to effectively augment PDB for structural heterogeneity\n- Rigorous evaluation: decoy normalization mitigates template bias and focuses on relative compatibility with each state.\n- Clear ablations show that combined training beats single-only or multi-only; simple Deep Set pooling matches more complex DSS.", "weaknesses": "- Dataset uses only the max-RMSD pair, potentially biasing toward extreme transitions and discarding informative intermediates.\n- Entirely in silico; mapping the normalized AF3 gains to experimental hit rates is unknown.\n- Missing appendix reference (line 105)", "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": "4bGshPkU5s", "reviewer": "Reviewer_Pioj", "summary": "The paper proposes a novel approach for protein design given multiple states, which correspond to multiple structures. Instead of an ad-hoc aggregation of predictions from different states, DynamicMPNN first encodes multiple states of protein structures, along with their binding partners, into an aggregated representation in latent space. Then, an autoregressive sequence decoder is applied to decode from the pooled representation. In addition, the paper proposes a more reasonable multi-state protein design evaluation based on folding with AF3 with structures of different states as templates. The paper also provides a valuable two-state protein dataset of more than 40000 conformer pairs. Benchmarked with a wide variety of baseline models, DynamicMPNN shows superior performance in both sequence recovery and refoldability.", "questions": "1.The Appendix number is missing in line 105.\n\n2.In the caption under Figure 2, the description of 2(b) and 2(c) are mismatched with the figures. Their description in the caption should be swapped. \n\n3.How the binding partners are treated in AF3 in multi-state design evaluation.\n\n4.Instead of using states as templates in AF3 evaluation, is it reasonable to provide only the designed sequence along with the sequence of binding partners to AF3, and use the folded structure, without using the ground truth state conformer as a template?\n\n5.How does DynamicMPNN perform when using Single Training + MSD, using the same logit averaging strategy as ProteinMPNN-MSD? It would be interesting to see this result so that the effectiveness of unified encoding approach in multi training can be more clearly validated.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper presents a complete contribution to the problem of multistate protein design, covering from dataset building, model design and evaluation methods.\n\n2.The paper is well written with the idea being clearly demonstrated.\n\n3.The benchmark provides a thorough comparison between various combinations of models and training strategies, providing a strong support to the pretraining-finetuning training pipeline.\n\n4.Performance on the foldability evaluation based on AF3 shows significant improvement of DynamicMPNN.", "weaknesses": "1.Though it is argued that refoldability with a single protein folding model prediction is not suitable for multistate design, it would still be interesting to see how the refoldability would differ if the state conformers are not provided as templates in $\\mathrm{AF3}(Y, X_k)$. In other words, it would still be interesting to see the refoldability with only $\\mathrm{AF3}(Y)$. If the strong bias towards a single dominant state of the existing folding models (in this case, AF3) can be demonstrated over the curated evaluation datasets, it would be valuable to support the newly proposed refoldability definition.\n\n2.DynamicMPNN is trained and evaluated with only on two-state proteins. Extending to proteins with more states can be non-trivial since it may require more sophisticated pooling strategy across the states. DSS in this paper shows no advantage over the simple pooling strategy. The case can be different if we extend to more states, where richer interaction between conformational channels could help.\n\n3.There are minor flaws in the presentation of the paper, see the first 2 items in the Questions section.", "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": "T9nEQBIWdU", "reviewer": "Reviewer_DH5T", "summary": "The authors curated a dataset of similar sequences and their multiple conformations from the PDB. They trained an inverse folding model on this dataset to enable inverse folding with two target conformations. The paper proposes a new metric for evaluating multi-target inverse folding by comparing the target folded structure to the AF3-predicted structure, using the target as a template.", "questions": "1. How is the decoy structure selected for the metric? If it is chosen to be dissimilar to the targets but represents a structure the sequence is unlikely to fold into, it could significantly distort the normalized metric.  \n\n2. Why was AF3 used for evaluation, given that other models such as Boltz or AlphaFlow are known to perform better at predicting structural ensembles?\n\n\n3. Line 105 broken reference", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "This paper challenges the “one sequence–one structure” assumption in inverse folding, which is an important direction to explore.  \nThe dataset curation approach is a clever use of the existing PDB database and MSA alignments to extract sequences with multiple possible protein conformations.", "weaknesses": "1. Using AF3 as a validation metric to assess whether a sequence folds into the desired structure is reasonable in the single conformation case. However, in the multiple conformation setting, where AF3 is known to underperform, this approach is questionable, particularly when the target structure is provided as a template. The choice of decoy structure in such cases could significantly affect the results, and there is insufficient evidence that this metric is meaningful or correlates well with real world folding behavior.  \n2. The claim that the target sequence can form both target conformations is supported only by computational validation, with no experimental evidence demonstrating successful dual conformation inverse folding.\n3. The paper feels somewhat rushed. The authors still have an extra page available to include additional results, while Table 2 and Figure 3 present essentially the same information, which is not an efficient use of space.", "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)