gRNAde: Geometric Deep Learning for 3D RNA inverse design
Authors: Chaitanya K. Joshi
Arian Rokkum Jamasb
Ramon Viñas Torné
Charles Harris
Simon V Mathis
Alex Morehead
Rishabh Anand
Pietro Lio
Venue: ICLR 2025 Spotlight (2025)
Keywords: RNA Structure
RNA Design
Geometric Deep Learning
Graph Neural Networks
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: GNN-based, wet-lab validated 3D RNA design pipeline; obtains SOTA performance for single-state, multi-state design, mutant fitness ranking.

Abstract: Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: github.com/chaitjo/geometric-rna-design

Published as a conference paper at ICLR 2025
GRNADE: GEOMETRIC DEEP LEARNING FOR 3D RNA INVERSE DESIGN
Chaitanya K. Joshi', Arian R. Jamasb?, Ramon Viñas Charles Harris', Alex Morehead',
Simon V. Mathis",
Rishabh Anand
Pietro Lio"
University of Cambridge, UK
Prescient Design, Genentech, Roche, 3EPFL, Switzerland,
Uniierriity of Missouri, USA
National University of Singapore
Correspondence: chaitanya joshi Bcl, cam. ac.
ABSTRACT
Computational RNA design tasks often posed inverse problems, where quences are designed based on adopting a single desired secondary structure with- out considering 3D conformational diversity. We introduce gRNAde, geometric RNA design pipeline operating on 3D RNA backbones to design sequences that ex- plicitly account for structure and dynamics. gRNAde uses multi-state Graph Neu- ral Network and autoregressive decoding to generates candidate RNA sequences conditioned
or more 3D backbone structures where the identities of the bases are unknown. On single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on new benchmark multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in retrospective analysis of recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: g thub. com/ chai to/ geomet i deeaddeesse in
backbone re-design
Multi-state Graph Neural Network Encoder
Extract Backbones
Sequence Decoder
GAGCGU...
RNA Sequence
Equivariant lo:
RNA Conformational Ensemble
Set of Backbone
3D roto-translations
Geometric Graphs
node order conformation order
The gRNAde pipeline for 3D RNA inverse design. gRNAde is a generative model for RNA sequence design conditioned on backbone 3D structure(s). gRNAde processes more RNA backbone graphs (a conformational ensemble) via a multi-state GNN encoder which is equivariant to 3D roto-translation of coordinates as well conformational state order, followed by conformational state order-invarian pooling and autoregressive sequence decoding.
Figure 1:

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1 INTRODUCTION
Why RNA design? Historical efforts in computational drug discovery have focussed on designing small molecule or protein-based medicines that either treat symptoms or counter the end stages of disease processes. In recent years, there is a growing interest in designing new RNA-based therapeutics that intervene earlier in disease processes cut off disease-causing information flow in the cell (Damase et al., 2021; Zhu et al., 2022). Notable examples of RNA molecules at the forefront of biotechnology today include mRNA vaccines (Metkar et al., 2024) and CRISPR-based genomic medicine (Doudna & Charpentier, 2014). Of particular interest for structure-based design are ribozymes and riboswitches in the untranslated regions of mRNAs (Mandal & Breaker, 2004; Leppek et al., 2018). In addition to coding for proteins (such as the spike protein in the Covid vaccine), naturally occurring mRNAs contain riboswitches that are responsible for cell-state dependent protein expression of the mRNA. Riboswitches act by 'switching' their 3D structure from an unbound conformation to a bound one in the presence of specific metabolites or small molecules. Rational design of riboswitches will enable translation to be dependent on the presence or absence of partner molecules, essentially acting as 'on-off" switches for highly targeted mRNA therapies in the future (Felletti al., 2016; Mustafina et al., 2019; Mohsen al., 2023).
Challenges of RNA modelling. Despite the promises of RNA therapeutics, proteins have been the primary focus in the 3D biomolecular modelling community. Availability of large number of protein structures from the PDB combined with advances in deep learning for structured data (Bronstein et al., 2021; Duval et al., 2023) have revolutionized protein 3D structure prediction (Jumper et al., 2021) and rational design (Dauparas et al., 2022; Watson et al., 2023). Applications of deep learning for RNA design are underexplored compared to proteins due to paucity of 3D structural data (Schneider et al., 2023). Most tools for RNA design primarily focus on secondary structure without considering 3D geometry (Churkin et al., 2018) or use non-learnt algorithms for aligning 3D RNA fragments (Han et al., 2017; Yesselman et al., 2019), which can be restrictive due to the hand-crafted nature of the heuristics used. In addition to limited 3D data, the key technical challenge is that RNA is generally more dynamic than proteins. The same RNA can adopt multiple distinct conformational states to create and regulate complex biological functions (Ganser et al., 2019; Hoetzel & Suess, 2022; Ken al., 2023). Computational RNA design pipelines must account for both the 3D geometric structure and conformational flexibility of RNA to engineer new biological functions.
This paper introduces gRNAde, geometric deep learning-based pipeline for RNA inverse design. As illustrated in Figure I, gRNAde generates candidate RNA sequences conditioned on one or more 3D backbone structures, enabling both single- and multi-state fixed- backbone sequence design. We demonstrate the utility of gRNAde for the following design scenarios:
Our contributions.
Improved performance and speed over Rosetta. We compare gRNAde to Rosetta (Leman et al., 2020), the state-of of-the-art physics-based tool for 3D RNA inverse design, for single-state fixed backbone design of 14 RNA structures of interest from the PDB identified by Das et al. (2010). We obtain higher native sequence recovery rates with gRNAde (56% on average) compared to Rosetta (45% on average). Additionally, gRNAde is significantly faster than Rosetta for inference; e.g. sampling 100+ designs in I second for an RNA of 60 nucleotides on an A100 GPU (<10 seconds on CPU), compared to the reported hours for Rosetta on CPU.
Multi-stan state RNA design, which was previously not possible with Rosetta. gRNAde with multi- state GNNs improves sequence recovery by 5% over an equivalent single-state model on benchmark of structurally flexible RNAs, especially for surface nucleotides which undergo positional or secondary structural changes. gRNAde's GNN is the first geometric deep learning architecture for multi-state biomolecule representation learning.
Zero-shot learning of RNA fitness landscape. In a retrospective analysis of mutational fitness landscape data for an RNA polymerase ribozyme (McRae et al., 2024), we show how gRNAde's perplexity, the likelihood of sequence folding into a backbone structure, can be used to rank mutants based on fitness in zeno-shot/unsupprrvise manner and outperforms random mutagenesis for improving fitness over the wild type in low throughput scenarios.
Wet lab validated. As part of Eterna's 's OpenKnot Round 6, 200 gRNAde-designee RNAs for diverse backbones were independently validated in wet lab via SHAPE chemical mapping experiments. gRNAde demonstrated an overall success rate of 50% at designing RNAs with desired pseudoknotted structures, which is a significant improvement over Rosetta with 35%
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2 THE GRNADE PIPELINE
Figure 1 illustrates the RNA inverse folding problem: the task of designing new RNA sequences conditioned structural backbone. Given the 3D coordinates of backbone structure, machine learning models must generate sequences that are likely to fold into that shape. The underlying assumption behind inverse folding (and rational biomolecule design) is that structure determines function (Huang et al., 2016).
Following best practices in protein design, gRNAde uses a structure-conditioned autoregressive language model with geometric GNN encoder and decoder (Jing et al., 2020; Dauparas et al., 2022). Our main architectural contribution (described in Section 2.2) as well as an efficient PyG impiementation (Appendix Figure 12 and the pseudocode). To the best of our knowledge, gRNAde is the first explicitly multi-state inverse folding pipeline, allowing users to design sequences for backbone conformational ensembles (a set of 3D backbone structures) as opposed a single structure.
multi-state GNN for modelling sets of 3D backbones
2.1 RNA CONFORMATIONAL ENSEMBLES AS GEOMETRIC MULTI-GRAPHS
Featurization. The input to gRNAde is RNA to be re-designed. For instance, this could be set of PDB files with 3D backbone structures for the given RNA (a conformational ensemble) and the corresponding sequence of nucleotides. As shown in Appendix Figure 9, gRNAde builds geometric graph representation for each input structure:
1. We start with a 3-bead coarse-grained representation of the RNA backbone, retaining the coordinates for P. C4', NI (pyrimidine) or N9 (purine) for each nucleotide (Dawson et al., 2016). This 'pseudotorsional representation describes RNA backbones completely in most cases while reducing the size of the torsional space to prevent overfitting (Wadley et al., 2007).
2. Each nucleotide is assigned node in the geometric graph with the 3D coordinate क; R corresponding to the centroid of the 3 bead atoms. Random Gaussian noise with standard deviation 0.1AA is added to coordinates during training to prevent overfitting on crystallisation artifacts, following Dauparas et al. (2022). Each node is connected by edges to its 32 nearest neighbours as measured by the pairwise distance in 3D space, [ -ūjle Il2-
3. Nodes are initialized with geometric features analogous the featurization used in protein inverse folding (Ingraham et al., 2019; Jing al., 2020): :): (a) forward and reverse unit vectors along the backbone from the 5' end to the 3' end, (Ø.+1 - E, and क; -z,1)): and (b) unit vectors, distances, angles, and torsions from each C4' to the corresponding P and N1/N9.
4. Edge features from node j to are initialized as: (a) the unit vector from the source to destination node, 2,,- H;; (b) the distance in 3D space, || T.l12, encoded by 32 radial basis functions; and (c) the distance along the backbone, j - i, encoded by 32 sinusoidal positional encodings.
Multi-graph representation. As described in the previous section, given a of k (conformational state) structures in the input conformational ensemble, each RNA backbone is featurized as separate geometric graph (A)) = (A) S()), (*)) with the scalar features (kk) E Raxf, vector features adjacency matrix. For clear presentation and without loss of
(A)) E R x3 and A (k), generality, we omit edge features and use f. f" to denote scalar/vector feature channels.
The input to gRNAde thus a set of geometric graphs {g .(..)(())))) which is merged into a 'multi-graph' representation of the conformations, M = (A, V)), by stacking the set of scalar features ( ..... {(*) into one tensor S < Ruxkxf along new axis for the set size k. Similarly, the set of vector features { (1) (k) is stacked into one tensor V E Bnxkx x3 Lastly, the set of adjacency matrices (A A (A)) are merged via union U into joint adjacency matrix A.
2.2 MULTI-STATE GNN FOR ENCODING CONFORMATIONAL ENSEMBLES
The gRNAde model, illustrated in Appendix Figure 10, processes one or more RNA backbone graphs via multi-state GNN encoder which is equivariant to 3D roto-translation of coordinates well as to the ordering of conformational states, followed by conformational state invarratt pooling and sequence decoding. We describe each component in the following sections.
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Multi-state GNN encoder. When representing conformational ensembles multi-graph each node feature tensor contains three axes: (#nodes, #conformations, feature channels). We perform message passing the multi-gu gualti-rapphhaa adjaceney to independently process each conformational state, while maintaining permutation equivariance of the updated feature tensors along both the first (#nodes) and second (#coonformation) axes. This works by operating only the feature channels axis and generalising the Py PyTorch Geometric (Fey & Lenssen, 2019) message passing class to account for the extra conformations axis; see Appendix Figure 12 and the pseudocode for details.
We use multiple rotation-equivarian GVP-GNN (Jing et al., 2020) layers to update scalar features 8, E Ækxf and vector features U, k x3 for each node i:
m. m. = MSG( (s,, ū.). (8,, T,) eny)
JEN;
00 = UPD (s;, 85)) (m; m.)):
where MsG, UPD are Geometric Vector Perceptrons, generalization of MLPs to take tuples of scalar and vector features as input and apply 0(3)-equivariant non-linear updates. The overall GNN encoder is S0 (3) -equivvaaii due to the use of refeeciionsensiitiee input features (dihedral angles) combined with ('8--evivaaiaan GVP-GNN layers.
Our multi-state GNN encoder is easy to implement in any message passing framework and can be used as a plug-and-plas extension for any geometric GNN pipeline incorporate the multi-state inductive bias. It serves as an elegant alternative to batching all the conformations, which we found required major alterations to message passing and pooling depending on downstream tasks.
Conformation inder-invariant pooling. The final encoder representations in gRNAde account for multi-state information while being invariant to the permutation of the conformational ensemble. To achieve this, perform Deep Set pooling (Zaheer et al., 2017) over the conformations axis after the final encoder layer reduce S E Runkx and V E Rankx x3 S' TR= and V Rax f33, x3,
&
S.V
Σ S| ( (, 1))
simple sum or average pooling does not introduce any new learnable parameters to the pipeline and is flexible to handle variable number of conformations, enabling both single-state and multi-state design with the same model. In Appendix B, we also explore more expressive geometric set pooling functions (Maron et al., 2020).
Sequence decoding and loss function. We feed the final encoder representations after pooling, S', V', to autoregressive GVP-GNN decoder layers to predict the probability of the four possible base identities (A, G, C, U) for each node/nucleotide. Decoding proceeds according to the RNA sequence order from the 5' end to 3' end. gRNAde is trained in a self-supervised manner by minimising a cross-entropy loss (with label smoothing value of 0.05) between the predicted probability distribution and the ground truth identity for each base. During training, we use autoregressive teacher forcing (Williams & Zipser, 1989) where the ground truth base identity is fed as input to the decoder each step, encouraging the model to stay close to the ground-truth sequence.
Sampling. When using gRNAde for inference and designing new sequences, we iteratively sample the base identity for a given nucleotide from the predicted conditional probability distribution, given the partially designed sequence up until that mucleotide/decoding step. We can modulate the smoothness or sharpness of the probability distribution by using temperature parameter. gRNAde can also use unordered decoding (Dauparas et al., 2022) with minimal impact on performance, as well as masking or logit biasing during sampling, depending the design scenario at hand.
2.3 EVALUATION METRICS FOR DESIGNED SEQUENCES
In principle, inverse folding models can be sampled from to obtain a large number of designed sequences for given backbone structure. Thus, in-silico metrics to determine which sequences are useful and which ones to prioritise wet lab experiments are a critical part of the overall pipeline. We currently use the following metrics to evaluate gRNAde's designs, visualised in Appendix Figure 11:
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Native sequence recovery, which is the average percentage of native (ground truth) nucleotides correctly recovered in the sampled sequences. Recovery is the most widely used metric for biomolecule inverse design (Dauparas et al., 2022) but can be misleading in the case of RNAs where alternative nucleotide base pairings can form the same structural patterns.
Secondary structure self-consistency score, where we 'forward fold' the sampled sequences using secondary structure prediction tool (we used EternaFold (Wayment-Shel -Senlee al., 2022)) and measure the average Matthew's 's Correlation Coefficient (MCC) to the groundtruth secondary structure, represented as binary adjacency matrix. MCC values range between and +1, where +1 represents a perfect match, 0 an average random prediction and 1 an inverse prediction. This measures how well the designs recover base pairing patterns.
Tertiary structure self-consistency scores, where we 'forward fold' the sampled sequences using 3D structure prediction tool (we used RhoFold (Shen et al., 2022)) and compute the average RMSD, TM-score and GDT TS to the groundtruth C4' coordinates to measure how well the designs recover global structural similarity and 3D conformations.
Perplexity, which can be thought of the average number of bases that the model is selecting from for each nucleotide. Formally, perplexity is the average exponential of the negative log- likelihood of the sampled sequences. A 'perfect' model which regurgitates the groundtruth would have perplexity of I, while a perplexity of 4 means that the model is making random predictions (the model outputs a uniform probability over possible bases). Perplexity does not require ground truth structure to calculate, and can also be used for ranking sequences as it is the model's 's estimate of the compatibility of a sequence with the input backbone structure.
Significance and limitations. Self-consistency metrics, termed 'designability' (eg. ScRMSD well perplexity have been found to correlate with experimental success in protein design (Watson et al., 2023). While precise designability thresholds are yet to be established for RNA, pairs of structures with TM-5 or EDD_TS>>..5 are known correspond to roughly the same fold (Zhang et al., 2022). Another major limitation for in-silico evaluation of 3D RNA design compared to proteins is the relatively worse state of structure prediction tools (Schneider et al., 2023).
EXPERIMENTAL SETUP
3D RNA structure dataset. We create a machine learning-ready dataset for RNA inverse design using RNASolo (Adamczyk al., 2022), a novel repository of RNA 3D structures extracted from solo RNAs, protein-RNA complexes, and DNA-RÑA hybrids in the PDB. We used all currently known RNA structures at resolution <4.0A resulting in 4,223 unique RNA sequences for which a total of 12,011 structures are available (RNASolo date cutoff: 31 October 2023). As inverse folding is a per-nodde/er--nucleotddd level task, our training data contains over 2.8 Million unique nucleotides. Further dataset statistics are available in Appendix Figure 13, illustrating the diversity of our dataset in terms of sequence length, number of structures per sequence, as well as structural variations among conformations per sequence.
Structural clustering. In order to ensure that we evaluate gRNAde's generalization ability to novel RNAs, we cluster the 4,223 unique RNAs into groups based on structural similarity. We use US Ullali (Zhang et al., 2022) with a similarity threshold of TM-score >0.45 for clustering, and ensure that we train, validate and test gRNAde on structurally dissimilar clusters (see next paragraph). We also provide utilities for clustering based sequence homology using CD-HIT (Fu et al., 2012), which leads to splits containing biologically dissimilar clusters of RNAs.
Splits to evaluate generalization. After clustering, we split the RNAs into training 4~~0000 samples), validation and test sets (100 samples each) to evaluate two different design scenarios:
1. Single-state split. This split is used to fairly evaluate gRNAde for single-state design on a set of RNA structures of interest from the PDB identified by Das et al. (2010), which mainly includes riboswitches, aptamers, and ribozymes. We identify the structural clusters belonging to the RNAs identified in Das et al. (2010) and add all the RNAs in these clusters to the test set (100 samples). The remaining clusters are randomly added to the training and validation splits.
Note that such model would he practically useless for real design tasks.
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2. Multi-state split. This split is used to test gRNAde's ability to design RNA with multiple distinct conformational states. We order the structural clusters based on median intra-sequent RMSD among available structures within the cluster'. The top 100 samples from clusters with the highest median intra-sequence RMSD are added to the test set. The next 100 samples are added to the validation set and all remaining samples are used for training.
Validation and test samples come from clusters with at most unique sequences, in order to ensure diversity. Any samples that were not assigned clusters are directly appended to the training set. We also directly add very large RNAS (> 1000 nts) to the training set, as it is unlikely that we want to design very large RNAs. We exclude very short RNA strands (< 10 nts).
Evaluation metrics. For given data split, we evaluate models on the held-out test set by designing 16 sequences (sampled at temperature 0.1) for each test data point and computing averages for each of the metries described in Section 2.3: native sequence recovery, structural self-consistency scores and perplexity. We employ early stopping by reporting test set performance for the model checkpoint for the epoch with the best validation set recovery. Standard deviations are reported across 3 consistent random seeds for all models.
Hy perparrameerrs.. All models use encoder and decoder GVP-GNN layers, with 128 scalar/16 16 vector node features, 64 scalar/4 vector edge features, and drop out probability 0.5, resulting in 2, 1,14,994 trainable parameters. All models are trained for a maximum of 50 epochs using the Adam optimiser with an initial learning rate of 0.0001, which is reduced by factor 0.9 when validation performance plateaus with patience of 5 epochs. Ablation studies of key modelling decisions are available in Appendix Table 1.
RESULTS
4.1 SINGLE-STATE RNA DESIGN BENCHMARK
We set out to compare gRNAde to Rosetta, a sfate-of-the-ar physics- based toolkit for biomolecular modelling and design (Leman al., 2020). We reproduced the benchmark setup from Das al. (2010) for Rosetta's ' fixed backbone RNA sequence design workflow on 14 RNA structures of interest from the PDB, which mainly includes riboswitches, aptamers, and ribozymes (full listing in Table 2). We trained gRNAde on the single-state split detailed in Section 3, explicitly excluding the 14 RNAS as well as any structurally similar RNAs in order to ensure that we fairly evaluate gRNAde's generalization abilities vs. Rosetta.
gRNAde improves sequence recovery over Rosetta. In Figure 2, we compare gRNAde's 's native sequence recovery for single-state design with numbers taken from Das et al. (2010) for Rosetta, FARNA (a predecessor of Rosetta), ViennaRNA (the most popular 2D inverse folding method), and RDsssgn (Tan et al., 2023). RDesign is concurrent deep learning-basee 3D inverse folding model which uses invariant GNN layers and -oooreotoreeessivvee decoding without sampling; see Appendix for details. gRNAde has higher recovery of 56% average compared to 45% for Rosetta, 32% for FARNA, 27% for ViennaRNA, and 43% for RDesign.
gRNAde is significantly faster than Rosetta. In addition to superior sequence recovery, gRNAde is significantll faster than Rosetta for high-throughout design pipelines. Training gRNAde from scratch takes roughly 2-6 hours on single A A100 GPU, depending on the exact hyperparameters. Once trained, Ade can design hundreds of sequences for backbones with hundreds of nucleotides in 10 seconds CPU and ~ second with GPU acceleration. On the other hand, Rosetta takes order of hours to produce a single design due to performing expensive Monte Carlo optimisation until convergence on CPU Deep learning methods like gRNAde are arguably easier to use since no expert customization is required and setup is easier compared to Rosetta (the latest builds do not include RNA recipes), making RNA design more broadly accessible.
For each RNA sequence, we compute the pairwise C4' RMSD among all available structures. We then compute the median RMSD across all sequences within each structural cluster.
'We note that Rosetta documentation states that "runs on RNA backbones longer than -etnn nucleotides take many minutes or hours". We have not run Rosetta ourselves recent builds do not include RNA recipes. It is hard fairly compare inference time Rosetta recipes are not GPU-accelerated, while gRNAde is.

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gRNAde seq, recovery
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(a) gRNAde outperforms Rosetta.
(b) gRNAde's perplexity correlates with recovery.
Figure gRNAde compared Rosetta for single-state design. (a) We benchmark native sequence recovery of gRNAde, RDesign, Rosetta, FARNA and ViennaRNA on 14 RNA structures of interest identified by Das al. (2010). gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45%) and all other methods. (b) Sequence recovery per sample for Rosetta and gRNAde, shaded by gRNAde's perplexity for each sample. gRNAde's perplexity is correlated with native sequence recovery for designed sequences (Pearson correlation: -0.76, Spearman correlation: -0.67). Full results on single-state test set are available in Appendix and per-RNA results in Appendix Table 2.
gRNAde's perplexity correlates with sequence recovery. In Figure 2b, plot native sequence recovery per sample for Rosetta vs. gRNAde, shaded by gRNAde's average perplexity for each sample. Perplexity an indicator of the model's confidence in its own prediction (lower perplexity implies higher confidence) and appears to be correlated with native sequence recovery. In the subsequent Section 4.3, we further demonstrate the utility of gRNAde's perplexity for zero-shot ranking of RNA fitness landscapes.
4.2 MULTI-STATE RNA DESIGN BENCHMARK
Structured RNAs often adopt multiple distinct conformational states to perform biological functions (Ken et al., 2023). For instance, riboswitches adopt at least two distinct functional conformations: ligand bound (holo) and unbound (apo) state, which helps them regulate and control gene expression (Stagno et al., 2017). If were attempt single-state inverse design for such RNAs, each backbone structure may lead to a different set sampled sequences. It is not obvious how to select the input backbone well designed sequence when using single-state models for multi-state design. gRNAde's multi-state GNN, descibed Section 2.2, directly 'bakes the multi-state nature of RNA into the architecture and designs sequences explicitly conditioned on multiple states.
In order to evaluate gRNAde's multi-state design capabilities, we trained equivalent single-state and multi-state gRNAde models the multi-state split detailed in Section 3, where the validation and test sets contain progressively more structurally flexible RNAs as measured by median RMSD among multiple available states for an RNA.
Multi-state gRNAde consistently boosts sequence recovery. In Figure 3a, we compared a single- state variant of gRNAde with otherwise equivalent multi-state models (with up 5 states) in terms of native sequence recovery. Multi-state variants show consistent 3-5% improvement, with the best performance obtained using 3 states. This trend holds lesser extent on the single-state benchmark where the multi-state model is being used with only one state as input. This suggests that seeing multiple states during training can be useful for teaching gRNAde about RNA conformational ficxibility and improve performance even for single-state design tasks. As caveat, is worth noting that multi-state models consume more GPU memory than an equivalent single-state model during mini-batch training (approximate peak GPU usage for max. number of states 1: 12GB, 3: 28GB, 5: 50GB on a single A A100 with at most 3000 total nodes mini- batch..
Improved recovery in structurally flexible regions. In Figure 3b, evaluated gRNAde's multi- state sequence recovery at ane-grained, pec-nocce level to understand the source performance gains. Multi-state GNNs improve sequence recovery the single-state variant structurally
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(b) Per-mucleotide recovery vs. structural flexibility
Figure 3: Multi-state design benchmark. (a) Multi-state gRNAde shows a consistent 3-5% im- provement over the single-state variant in terms of sequence recovery on the multi-state test set of 100 RNAs, with the best performance obtained using 3 states. (b) When plotting sequence recovery per-nucleotide, multi-state gRNAde improves over single-state model for structurally flexible regions of RNAs, as characterised by nucleotides that tend to undergo changes base pairing (left) and nucleotides with higher average RMSD across multiple states (right). Marginal histograms in blue show the distribution of values. We plot performance for one consistent random seed across all models; collated results and ablations available in Appendix B.
flexible nucleotides, as characterised by undergoing changes in base pairing/secondary structure and higher average RMSD between 3D coordinates across states.
4.3 ZERO-SHOT RANKING OF RNA FITNESS LANDSCAPE
Lastly, we explored the use of gRNAde as a zero-shot ranker of mutants in RNA engineering campaigns. Given the backbone structure of a wild type RNA of interest well as a candidate set of mutant sequences, we can compute gRNAde's ' perplexity of whether a given sequence folds into the backbone structure. Perplexity is inversely related to the likelihood of sequence conditioned structure, as described in Section 2.3. We can then rank sequences based on how 'compatible" they with the backhone structure in order select subset to be experimentally validated in wet labs.
Retrospective analysis on ribozyme fitness landscape. A recent study by McRae et al. (2024) determined a cryo-EM structure of a dimeric RNA polymerase ribozyme at 5Å resolution", along with fitness landscapes of ~55K mutants for the catalytic subunit STU and 448K mutants for the scaffolding subunit t1. We design a retrospective study using this data of (sequence, fitness value) pairs where we simulate an RNA engineering campaign with the aim of improving catalytic subunit fitness over the wild type 5TU sequence.
We consider various design budgets ranging from hundreds to thousands of sequences selected for experimental validation, and compare 4 unsupervised approaches for ranking/selecting variants: (1) random choice from all 75,0000 sequences; (2) random choice from all 449 single mutant sequences; (3) random choice from all single and double mutant sequences (as sequences with higher mutation order tend to be less fit); and (4) negative gRNAde perplexity (lower perplexity is better). For each design budget and ranking approach, we compute the expected maximum change in fitness over the wild type that could be achieved by screening as many variants as allowed in the given design budget. We run 10,000 simulations to compute confidence intervals for the 3 random baselines.
gRNAde outperforms random baselines in low design budget scenarios. Figure illustrates the results of our retrospective study. At low design budgets of up to hundreds of sequences, which are relevant in the case of a low throughput fitness screening assay, gRNAde outperforms all random baselines in terms of the maximum change in fitness over the wild type. The top 10 mutants ranked by gRNAde contain a sequence with 4-fold improved fitness, while the top 200 Ieads to a 5-fold improvement. Note that gRNAde is used zero-shot here, i.e. it not fine-tuned on any assay data.
"This RNA not present in gRNAde's training data, which contains structures at <4.0A resolution.
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Max Fitness by Sample Size and Condition (n=74,943; simulations=10.00
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2.48
2nd best (f1) 2.40)
Expected max faxd
10x
5th best 2:23).
2.30
Fitreds
8x
2,08
10th best (f6)1198)
1.79
20th (f6111.61)
Condition
4x
(,35)
1.39
rendom 1_mut=1
0.68
n_mute-2
wikllyne
0.00
gRNAde
0x
Selected sequencas for assaying
Figure 4: Retrospective study of gRNAde for ranking ribozyme mutant fitness. Using the backbone structure and mutational fitness landscape data from an RNA polymerase ribozyme (McRae et al., 2024), retrospectively analyse how well we rank variants at multiple design budgets using random selection vs. gRNAde's perplexity for mutant sequences conditioned on the backbone structure (catalytic subunit STU). Note that gRNAde is used zero-shot here, i.e. it not fine-tuned on any assay data. For stochastic strategies, bars indicate median values, and error bars indicate the interquartile range estimated from 10,000 simulations per strategy and design budget. At low throughput design budgets of up to 500 sequences, selecting mutants using gRNAde outperforms random baselines in terms of the expected maximum improvement in fitness over the wild type. In particular, gRNAde performs better than single site saturation mutagenesis, even when all single mutants are explored (total of 449 single mutants, 10,493 double mutants for the catalytic subunit STU in McRae et al. (2024)). See Appendix Figure 7 for results on scaffolding subunit tl.
Overall, it is promising that gRNAde's perplexity correlates with experimental fitness measurements out-of-the-box (zero-shot) and can be a useful ranker of mutant fitness in our retrospective study. In realistic design scenarios, improvements could likely be obtained by fine-tuning gRNAde on a low amount of experimental fitness data. For example, latent features from gRNAde may be finctuned or used input prediction head with supervised learning fitness landscape data.
INDEPENDENT WET LAB VALIDATION OF GRNADE DESIGNS
Finally, we present the results of independent wet lab validation of gRNAde via Eterna, an online platform for computational RNA design. Etema regularly releases RNA design tasks global community of researchers and citizen-ssientists who design sequences using expert intuition or computational tools (such as gRNAde and Rosetta). The designs are then experimentally validated Stanford University via high-throughhut SHAPE assays which measures the reactivity of an RNA sequence a chemical modifier, as described in He et (2024).
Eterna OpenKnot Round 6. As part of OpenKnot Round 6, we submitted gRNAde designs for 10 target RNA backbones: C-VS-CoV-2 frame shift element, Tetrahydrofolate riboswitch, GMP-II riboswitch, SAM riboswitch, HCV internal ribosome entry site, synthetic kissing loop structure, donggang dumbell, telomerase ribozyme, HDV ribozyme, and CPEB3 ribozyme. We submitted total of 20 designs for each backbone via two approaches: (1) 10 partial designs, where parts of the wildtype sequence are kept fixed; and (2) 10 full designs, where the entire sequence designed. In addition to designs from participants including gRNAde, the organizers also evaluated the wildtype sequence for each RNA as a sanity check for their assay, as well as two variants of Rosetta's 's RNA inverse folding protocol (Das et al., 2010).
OpenKnot score determines success. For each design, the organizers compute an OpenKnot Score (between 0-100) to measure how likely the sequence is form the target 3D structure. A score

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Experimental OpenKnot Score
100
Score
WT: 84.99
90-
Rosetta H: 76.85
Rosetta L: 75.31
gANAde P: 80.56
80
x
gRMAde F: 79.
70-
50-
40
Wildtype sequence
Rosetta HiRes (35%)
Rosetta LoRes
oRNAde Partial (52%)
gRNAde Full
(78%)
(49%)
Method (success rate)
Wet lab validation of gRNAde designs in Eterna's OpenKnot Round 6. Each point represents the OpenKnot score (higher better) for designed RNA sequence given one of 10 target RNA backbones (10 designs per target per method). Wildtype sequences were included sanity checks and upper bounds on performance. 50% of gRNAde designs have a score above 80, which is the threshold for successful design. The median gRNAde design obtains a score of 80, while Rosetta designs have a median score of 76, with a 35% sucess rate. See Appendix Figure for per-puzzle results. Note that have obtained permission from Eterna organizers to share this data.
Figure 5:
above 80 is estimated to be highly likely to form the target structure (scores below 80 may also be successful, but their SHAPE reactivity profiles cannot determine this). The score developed to aid the discovery and characterisation of pseudoknotted structural elements in RNAs using SHAPE data as part of the OpenKnot challenge series.
gRNAde has 50% % success rate and outperforms Rosetta. In Figure 5, we plot the distribution of OpenKnot scores across all target RNAs for gRNAde and Rosetta designs as well as the wildtype control sequences. 52% of gRNAde partial designs and 49% of gRNAde full designs obtained OpenKnot scores greater than 80, compared to 35% for the best performing Rosetta variant. The median gRNAde design has score of 80, while the median Rosetta design scores 76. See Appendix Figure 8 for per-puzzle results where we find that gRNAde designs obtain higher scores than wildtype sequences for 3 targets, suggesting that designed sequences can have higher likelihood of forming the target structure compared to sequences observed in nature.
CONCLUSION
We introduce gRNAde, geometric deep learning pipeline for RNA sequence design conditioned on one or more 3D backbone structures. gRNAde represents significant advance over physics-base Rosetta in terms of both computational and experimental performance, well inference speed and ease-of-use. Further, gRNAde enables explicit multi-state design for structurally flexible RNAs which was previously possible with Rosetta. gRNAde's 's perplexity correlates with native sequence and structural recovery, and can be used for zero-shot ranking of mutants in RNA engineering campaigns. gRNAde is also the first geometric deep learning architecture for multi-state biomolecule representation learning; the model is generic and can be repurposed for other learning tasks on conformational ensembles, including multi-state protein design.
Limitations. Key avenues for future development of gRNAde include supporting multiple interacting chains, accounting for partner molecules with RNAs, and supporting negative design against undesired conformations. We discuss practical tradeoff's to using gRNAde de in real-world RNA design scenarios in Appendix E, including limitations due to the current state of 3D RNA structure prediction tools. Finally, we hopeful that advances in RNA structure determination and computationally assisted cryo-EM (Kappel et al., 2020; Bonilla & Kieft, 2022) will further increase the amount of RNA structures available for training geometric deep learning models in the future.
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ACKNOWLEDGEMENTS
We would like to thank Roger Foo, Phillip Holliger, Alex Borodavka, Rhiju Das, Janusz Bujnicki, Edoardo Gianni, Ben Porebski, Samantha Kwok, Michael Mohsen, Christian Choe, and John Boom for helpful comments and discussions. CKJ was supported by the STTRAR Singapore National Science Scholarship (PhD) and Qualcomm Innovation Fellowship. SVM was supported by the UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (EP/S022961/1), AM was supported by a U.S. NSF grant (DB12308699) and two U.S. NIH grants (R01GM093123 and RO1GM146340), This research was partially supported by Google TPU Research Cloud and Cambridge Dawn Supercomputer Pioneer Project compute grants.
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APPENDICES
Related Work
15
B Ablation Study
16
C Additional Results
18
Additional Figures
20
E FAQs using gRNAde
24
Comparison to contemporaneous work
25
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A RELATED WORK
We attempt to briefly summarise recent developments in RNA structure modelling and design, with an emphasis on deep learning-based approaches.
RNA inverse folding. Most tools for RNA inverse folding focus on secondary structure without considering 3D geometry (Churkin et al., 2018; Runge al., 2019; Cole al., 2024) and approach the problem from the lens of energy optimisation (Ward et al., 2023). Rosetta fixed backbone re-design (Das et al., 2010; Leman et al., 2020) the only energy optimisation-bals -bsed approach that accounts for 3D structure. Rosetta aims to find the lowest energy RNA sequence for given structure: (1) Given a starting point such as a target backbone and random/heuristic starting sequence, Rosetta uses Markov Chain Monte Carlo methods to sample 'moves' which are random changes to the sequence (whether a base is an A, U, G, or C) or side chain rotamer (the orientation of the base). (2) Next, Rosetta updates the all-atom 3D structure of the RNA backbone and uses an all-atom energy function to accept or reject a move based on whether stabilises the target backbone. This process is repeated until convergence with simulated annealing.
Deep neural networks such as gRNAde can incorporate 3D structural constraints and produce designs in single forward pass with GPU acceleration, which is orders of magnitude faster than optimisation- based approaches. This is particularly attractive for high-throughy inverse folding optimisation problem NP hard (Bonnet et al., 2020). Independent of our work, Tan et al. (2023) also developed a contemporaneous deep learning-based 3D RNA inverse folding model.
design pipelines as solving the
RNA structure design. Inverse folding models for protein design have often been coupled with backbone generation models which design structural backbones conditioned on various design constraints (Watson al., 2023; Ingraham al., 2023; Didi al., 2023). Current approaches for RNA backbone design use classical (non-learnt) algorithms for aligning 3D RNA motifs (Han et al., 2017; Yesselman et al., 2019), which are small modular pieces of RNA that are believed to fold independently. Such algorithms may be restricted by the use of hand-crafted heuristics and we plan to explore data-driven generative models for RNA backbone design in future work.
RNA structure prediction. There have been several recent efforts to adapt protein folding architectures such as AlphaFold2 (Jumper et al., 2021) and RosettaFold (Baek al., 2021) for RNA structure prediction (Li et al., 2023b; Wang et al., 2023; Back et al., 2024). A previous generation of models used GNNs as ranking functions together with Rosetta energy optimisation (Watkins et al., 2020; Townshend et al., 2021). None of these architectures aim at capturing conformational flexibility of RNAs, unlike gRNAde which represents RNAs as multi-state conformational ensembles. Neither structure prediction tools be used for RNA design tasks as they not generative models.
RNA language models. Self-supervised language models have been developed for predictive and generative tasks RNA sequences, including general-purpose models such RNA FM (Chen et al., 2022) and Mo (Penic et al., 2024) as well as mRNA-specific CodonBERT (Li et al., 2023a). RNA sequence data repositories are orders of magnitude larger than those for RNA structure (eg. RiNaLMo is trained on 36 million sequences). However, standard language models can only implicitly capture RNA structure and dynamics through sequence co-occurence statistics, which can pose a chellenge for designing structured RNAs such as riboswitches, aptamers, and ribozymes. RibonanzaNet (He et al., 2024) represents a recent effort in developing structure-informed RNA language models by supervised training experimental readouts from chemical mapping, although RibonanzaNet cannot be used for RNA design. Inverse folding methods like gRNAde are language models conditioned on 3D structure, making them natural choice for structure- baased design.
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Table 1: Ablation study and aggregated benchmark results for gRNAde. We report metries averaged over 100 test sets samples and standard deviations across 3 consistent random seeds. The percentages reported in brackets for the 3D self coffssensittecy scores are the percentage of designed samples within the "designability" threshold values (scRMSD 22A, scTM20045, scGDT20.5).
Self-consistecy metrics
Max. Vstalles
Max, train length
Perplesity (
Native sey.
2D EtermaFold scM
3D RhoPold
Split
Model
GNN
recovery (7)
scRMSD (1)
scTM score (7)
xcGDT_ TS (7)
AR
Equiv
500
1,77
0:43810001
0:62411007
1300112.8
0,11300
0.221:00 (12.7%
AR
1000
1.7350.03
0:453:00011
0,64810011
13,1010.58
0.20g00 (10.8%)
0.21290
ARK
Equiv
2500
1.412001
0.513
0,63310033
11.761691 (1.4%)
0.27 (2.886)
0.271000 (28.0%)
Single stae spte
AR
1.29-000
0.538=000
0.61240022
1.5044064
0.28-0 1%)
0.28+0. (36.276)
AR, mad
Equiv
1.5950.16
0.531:0004
0.621
11:574:00 (1.9%)
0.26a00
0.26600.0244155
AR
1.320004
0:531+001
0,585+006
11, 11,7040.56
(24.8)))2488))
0.25e00
NAR MAR
1.5420044
0,57112000
0,43010022
14,26110.11 (1.3%)
0.19LaD (15.916)
0.181200 (12.7%)
Equiv
1.46.0066
0.54410000
0,47311002
13,0L10088 (1.3%)
0,23LDD (24.0%)
0,221000 (17.9%)
AR
Equis, DS
1.23
0
0,620100001
11.4741005
0.25LDD (31.45)
0.281100 (27.2%6)
AR
Equix, DS
1.2510m
0,59900022
0,596.1063
11.50.1.000
(2.9%)
0,27La0
0,26.100 (26.4%)
Gooundraah sequence prodiction buseline:
1000100000
0,686:10000
5,23 (27.9%)
0,56 (68,7))
0.55.100 (68.7%)
Random sequence prediction baseline:
0:51110000
0:012110000
24.40.10.34 (0.0%)
0,04100 (0.0%)
0,021.00 (0.0%)
3D-culy beselline
0,25=00000
0.611:50000
20.34460.00
0,0740.0 (0.6%)
0,07
AR Equiv
1.5120001
0:48110000
0,57340004
21,8330.533 (000%)
0.12ADD (2.615)
0.15 (5.596)
AR
Equix, DS
1.8720004
0.44420011
0.567
22.09930.33
0.12
0.142:00 15.796)
AR
DS
1000
1.7620004
0.45510008
0,50M11004
12.921143
(0.0%)
0.11100.0 (2.316)
0.14100 (5.89)
Mohi 5325 spiit
AR
Equis, DS
Equis, DS
1.44:004
1.54-1007
0.531=000
0.500=001
0,57385068
0,54346001
22.19-6038
22 22.00460:1
0.12+0.0
0.11+a.
0.34+00
0.15+00 (7.5%)
Equas, DSS
1,37:004
04000000
0,5741005
22.30:10:45 (0.00)
0.12::0 (4.0%)
0,151:00 (7,58)
AR
Equis, DS
1.37
0.51040000
0,51485000
21 50:000000000
(2.99)))2299)
0.34800
NAR
Eguiv
1881000
0.40-00000
0,3724005
24. 18-8840 (0.0%)
0.09 (2.2%)
0.32+00 (4.7%)
Equis, DS
1.65e0.1
0.506e0001
0.54645002
24.06-50043
0.06+0. (2.0%)
0.11+00 12.9%%
Equos, DSS
0:520:0000
24. 55:10:555 (0.0%)
0.09:
0.12:00 (4.7%)
Equis,
1:59:021
0,350-001
24, 1616665755 (0.09)
0..6+a0(2.28)
0.10100 (4.58)
sequenee perdiction
1000::000
0,25 (4.255)
0.291:00 (3)
Random sopurtes besellin::
0:249:0000
0.013+0000
31 31.00/1::00
(0.0))
(0.0%)
ViennaRNA only baseline:
0,258.10000
0,470110.00
29,10,10:00 (8L0%)
0,05100 (0.0%)
0,081:0 (0.0%)
B ABLATION STUDY
Table 1 presents an ablation study as well as aggregated benchmark for various configurations of gRNAde. Key takeaways are highlighted below. Note that all results in the main paper are reported for models trained on the maximum length of 5000 nucleotides using autoregressive decoding and rotation-equivariint GNN layers, as this lead to the lowest perplexity values.
Split. Single- and multi-state splits are described in Section 3; the multi-state split is relatively harder than the single-state split based on overall reduced performance for all baselines and models. The multi-state split evaluates a particularly challenging o.o.d. scenario as the RNAs in the test set have significantly higher structural flexibility compared to those in the training set.
Max. #states We evaluate the impact of increasing the maximum number of states as input to gRNAde. Multi-state models improve native sequence recovery as well as structural self-consistency scores over an equivalent single state variant. Notably, on the more challenging multi-state split, the improvement in sequence recovery was observed to be as high as 5-6% for the best multi-state models. This trend holds even for the single-state benchmark where the multi-state model is being used with only one state input. This suggests that seeing multiple states during training can be useful for teaching gRNAde about RNA conformational flexibility and improve performance even for single-state design tasks.
GNN and pooling architecture We ablated whether the internal representations of the GVP-GNN are rotation invariant or equivariant. Equivariant GNNs are theoretically more expressive (Joshi et al., 2023) and we find them more capable at fitting the training distribution (as shown by lower perplexity) which in turn results in improved metrics compared to invariant GNNs.
In order to study the expressivity of pooling in the multi-state setting, we ablate the set pooling function used in the multi-state GNN: Deep Set pooling (DS) as well as the more expressive Deep Symetric Set pooling (DSS. Maron et al. (2020)). DS pooling is described in Equation (3). In DSS pooling, after each encoder GNN layer, we (1) aggregate node embeddings from each single state representation into a pooled multi-state representation per node, (2) apply a Geometric Vector
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Perceptron update the multi-state representation, and (3) add the updated multi-state representation back to each single state node representation. DSS pooling marginally improves performance on -utto--distribution test sets for both the single- and multi-state splits. We notice that DSS models fit the training data significantly better (final training loss goes from 0.40 to 0.36 for states). While DSS pooling is more expressive that DS pooling, it also adds 200K more parameters to the model and doubles training iteration time (4 mins to 8 mins for 3 states).
Model and decoder "AR" implies autoregressive decoding (described in Section 2.2, uses 4 encoder and 4 decoder layers), while 'NAR' implies non-autoregressive, one-shot decoding using an MLP (uses 8 encoder layers). Across both evaluation splits, AR models show significantly higher self- consistency scores than NAR, even though NAR lead to higher sequence recovery for the single-state split. AR is more expressive and can condition predictions at each decoding step past predictions, while one-shot NAR samples from independent probability distributions for each nucleotide. Thus, AR is a better inductive bias for predicting base pairing and base stacking interactions that are drivers of RNA structure (Vicens & Kieft, 2022). For instance, G-C and A-U pairs can often be swapped for one another, but non-autoregressive decoding does not capture such paired constraints.
Additionally, we also present results for the impact of training gRNAde with random decoding order. This can be practically very useful for partial or conditional design scenarios, and leads to a minor reduction in sequence recovery and 3D self-consistency (in line with what was observed for ProteinMPNN).
Max. train RNA length Limiting the maximum length of RNAs used for training can be seen as ablating the use of ribosomal RNA families (which are thousands of nucleotides long and form complexes with specialised ribosomal proteins). We find that training only short RNAs fewer than 1000s of nucleotides leads to worse sequence recovery and 3D self-consistency scores, even though improves 2D self-consistency across both evaluation splits. This suggests that tertiary interactions learnt from ribosomal RNAs can generalise to other RNA families to some extent (large ribosomal RNAs were excluded from test sets).
Non-learnt baselines. We report the performance of two non-learnt baselines to contextualise gRNAde's performance: for each test sample, simply predicting the groundtruth sequence back and predicting a random sequence. Structural self-consistency scores for the Groundtruth baseline provides rough upper bounds the maximum score that any gRNAde designs can theoretically obtain given the current state of 2D/3D structure predictors being used. gRNAde always performs better than the random baseline and often reaches 2D self-consistency scores close to the upper bound. Both 2D and 3D self-consistency scores are inherently limited by the performance of the structure prediction methods used.
2D inverse folding baseline. We additionally report results for ViennaRNA's 2D-only inverse folding method further demonstrate the utility of 3D inverse folding. ViennaRNA has improved 2D self-consistency scores over gRNAde but fails to capture tertiary interactions in its designs, as evident by poor recovery and 3D self-consistency scores similar to the random baseline. We observed the same trend for other 2D-only inverse folding methods such NuPack's ' design tool. This result should not be surprising. as 2D tools are meant for design scenarios that only involve base pairing and do not take any 3D information into account.
Choice of structure predictors. As previously noted, self-consistency metrics are highly dependent on the performance of the structure prediction method used. We chose EternaFold as it is simple to use as well as validated for designed and synthetic RNAs, unlike most other 2D structure prediction tools. Replacing EternaFold with RNAFold lead to unchanged results and did not modify the relative rankings of the models:
AR, state, Equiv. GNN, EternaFold scMCC: 0.612 RNAFold scMCC: 0.614+0.03.
NAR, 1 state, Equiv. GNN, EternaFold scMCC: 0.473 RNAFold scMCC: 0.477=0.04.
Lastly, would like to note the challenge of evaluating multi-state design: Structural celf-consisteney metrics are not ideal for evaluating RNAs which do not have one fixed structure/undergo changes to their structure. It would be ideal (but extremely slow and expensive) to run MD simulations to validate multi-state design models.
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C ADDITIONAL RESULTS
Table 2: Full results for Figure 2 comparing gRNAde to Rosetta, FARNA and ViennaRNA for single-state design 14 RNA structures of interest identified by Das al. (2010). Rosetta and FARNA recovery values are taken from Das et al. (2010), Supplementary Table 2.
ViennaRNA
FARNA Recovery
RDesign Recovery
Rosetta Recevery
gRNAde (singlo-state)
PDB ID
Description
Recovery
Recovery
Perplexity
2D self-cons.
1CSL
RRE high affinity site
0.25
0.20
0.4455
0.44
0.5719
1.28112
0.8644
Vitamin binding aptamer
0.3929
0.6250
1.3457
1327
Biotin-bindl bondiin RNA pscudeknol
0.30
0.36
0.3013
0.37
0.3437
1.6203
0.4523
1L2X
Viral RNA pscudoknot
0.24
0.45
0.3727
0.48
0.4721
1,3181
0,5692
RNA internal loop SRP
0.33
0.27
0.5556
0.53
0.5843
1.4337
0.1379
Sarcinhicia domain
4:55
Guaninc oboswitch aptamer
0.29
0.28
0,4112
0.36
0.5300
1.3824
0.9116
1X9C
All RI-RNA hairpin riboryme
0.26
0.31
0,3967
0.50
0,5000
1,3905
0.6630
HIV-I B dimerization
26CS
Pre cleavage state of gimS ribozyme
0.25
0.26
0,4118
0.44
0.5078
1.3053
0.4062
2GET
Thiamine pyrophouphtte- specific nihoswitch
0.25
0.38
0L3523
01.48
0.6500
1,2363
-00251
Junctionless hairpin riboyyme
1.0913
2385
Tetrahymenn tibozyme P4-P6 domain
0.27
0.36
0.5641
0.53
0.5689
1.1881
0.7281
3540
Loop E from E. celi SS iRNA
0.28
0.35
0.1158
0.55
0,4410
1,4938
0.0130
Overal reovery:
0.4296
gRNAde seg. recovery
1.00
1.6
gRNAde perplent exxtyy
0.75
13
0.25
0.00
0.00
0.25
0.30
0.75
1.00
RDesign seq. recovery
Figure gRNAde compared to RDesign for single-state design.
Max Fitness Sample Size and Condition (n=47,504; simulations=10.000
(.44)
27x
330
Expectes told WT
3rd (11:
21x
3.04
15x
10th best 2,67)
271
12x
2.48
220
Condition
200th
1.79
n muter1 1umuto-2
3x
1.10
wilthypp
0.00
gRNAde
10
100 200
403
1500
5800
17027
Selected sequercers asseying
Figure 7: Retrospective study of gRNAde for ranking ribozyme mutant fitness subunit), Using the backbone structure and mutational fitness landscape data from RNA polymerase ribozyme (MeRae et al., 2024), we retrospectively analyse how well we can rank variants at multiple design budgets using random selection gRNAde's perplexity for mutant sequences conditioned on the backbone structure (scaffolding subunit tl). gRNAde performs better than single site saturation mutagenesis, even when all single mutants explored (total of 403 single mutants, 17,027 double mutants for the scaffolding subunit tl in McRae et al. (2024)). See Section 4.3 for results on catalytic subunit STU and further discussions.
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SARS-COVA
7K01 THF riboswitch
Experimental Opentrorl Score
90
80
X
70
60-
Wildtpp
Wilttype
gRAide
HiRes
Partial
Full
Hiles (805)
LoRes (80%)
Partal (100%)
(100%)
120%]
(20%]
(30%)
(50%)
(200%)
3Q3Z C-di-GMPII switch
3T48 HCV IRES
100
Expetiennal Opennroot Score
80-
70
50
Wildlype sequence
Rosetta Hiles 110%1
Rosetta 1981
oftiidde (99)
gRoedde Full 099
Wilthype sequarce (10083
Rosetta (30%
Rodetta Lofes (30%)
gittiAde (69%)
gfiiide 130%)
(0%6
2AE kissing loop structure
2M8K telomerase
Experimental OpenKnot Score
90-
O
70
60
Wildtype secuence
Rosetta HiRes
Rosetta LoRes
GRNAde Partial
ARNAde Full
Wildbype Sequerice
Resetta HiRes
Rosetta LoRes
oRNidee Paitial
dRNide Full
40QU
donogang
100
Experimental Operkrro Score
80-
70
60-
50
Wildtype sequence
Resetta
Rosetta (20%)
GRNide (40%)
gRivAde (00%)
Wildtype (200%)
Rosetta (30%)
Rosetta (10%)
oRNitde (82%)
GRNide 170%)
(90%)
120%)
1DRZ HDV ribozyme
70R4 CPEB3 ribozyme
1.00
Experimental Openkroot Score
90
70
60
Wildtype sequence (709)
Rosetta
Rosetta Lolles
glibddde Partial
gRNAde Full
Wilthype
Rosetta
Rosetta
gliblde
gliddd Full
Hilles
sequarce
Hilles
Lofes
Partall
(10%)
Figure Per-input wet lab validation results of gRNAde designs. Notably, for the HDV ribozyme, CPEB3 ribozyme and donggang dumbell backbone structures, gRNAde designs tend to obtain higher OnenKnot scores than the wildtype sequences, which suggests that designed sequences can have higher likelihood of forming the pseudoknotted structure compared to naturally observed sequences.
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D ADDITIONAL FIGURES
Figure 9: RNA backbone featurization.
Figure 10: gRNAde model architecture.
Figure 11: In-silico evaluation metrics for gRNAde.
Figure 12: Multi-graph tensor representation of RNA conformational ensembles.
Listing I: Pscudocode for multi-state GNN encoder layer.
Figure 13: RNASolo data statisties.
OF
OF
C3
N1.N0
3x distances angles 3x torsions
Ct
CF
Ribose sugar
3-bead representation (P. C4', N1/N9)
Node (nucleotide)
Backbone chain
RNA backbone atoms
Coarse-grained features
3D neighbourhood
Figure 9: gRNAde featurizes RNA backbone structures as 3D geometric graphs. Each RNA nucleotide is node in the graph, consisting of 3 coarse-grained beads for the coordinates for P, C4', NI (pyrimidines) or N9 (purines) which are used to compute initial geometric features and edges to nearest neighbours in 3D space. Backbone chain figure adapted from Ingraham et al. (2019).
Backbone
GVP-GNN encoder layer
Deep Set Pooling
Per-node logits
Autoregressive decoder
Node Embeddings
G
C
GVP-GNN encoder layer
Backbone
GAGCG
Partial sequence
Sampling
Figure 10: gRNAde model architecture. One RNA backbone geometric graphs encoded via a series of SE(3) -equivrraaa Graph Neural Network layers (Jing et al., 2020) to build latent representations of the local 3D geometric neighbourhood of each nucleotide within each state. Representations from multiple states for each nucleotide are then pooled together via permutation invariant Deep Sets (Zaheer et al., 2017), and fed to autoregressive decoder predict probabilities over the four possible bases (A, G, C, U). The probability distribution can be sampled to design set of candidate sequences. During training, the model is trained end-to-end by minimising cross-entropy loss between the predicted probability distribution and the true sequence identity.
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GAGCGU...
Backbone graph
gRNAde
Sampled sequences
2D: EternaFold
$1
True
3D: RhoFold
Predicted structures
sequence
Sequence recovery 816
S
True
structure
Structural self-consistency scores 2D: MCC, 3D: RMSD, TM, GDT
S16
Figure 11: In-silico evaluation metries for gRNAde designed sequences. We consider (1) sequence recovery, the percentage of native nucleotides recovered in designed samples, (2) self-comsisteney scores, which are measured by 'forward folding' designed sequences using structure predictor and measuring how well 2D) and 3D structure are recovered (we use FternaFold and RhoFold for 2D/3D structure prediction, respectively). We also report (3) perplexity, the model's s estimate of the likelihood of sequence given a backbone.
k conformmae:: &
Mi Mill-gmpphh tansor
Paemutte modies Permute conformins Rotate 3D feasurts
nodess
Set
3D featuren: 8ox3)/O(3)
Updato with mubi-2N laper
9₂ e
Figure 12: Multi-graph tensor representation of RNA conformational ensembles, and the asso- ciated symmetry groups acting on each axis. We process a set of RNA backbone conformations with nodes each into a tensor representation. Each multi-state GNN layer updates the tensor while being equivariant to the underlying symmetries; pseudocode is available in Listing 1. Here, we show tensor of 3D vector-type features with shape x & 3. As depicted in the equivariance diagram, the updated tensor must be equivariant to permutation S., of n nodes for axis 1, permutation Sy conformational states for axis 2, and rotation 50(3)/O(3) of the 3D features for axis 3.
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class MulticVPConv (MessagePassing): GVVConV for handling multiple conformations"
def _init_ ...):
def forward(self, x_s, v, edge index, edge_attr)
A stack scalar feats along axis 1:
[n_nodes, n_conf, d -> [n_nodes, ncconf ds]
11
x_ w_s_ shape [0], x_s. shapehape[1] x_s. x_s. shape
12
13
# stack vector feat along axis 1:
# [n_nodes, n_conf, d_,, 31 -> [n_nodes, n_conf d_v+3] x_v (xw.viewx_ (0), x_v.shape[1] x_v.shape [2] +3)
16
17
message passing and aggregation message prof.proa adge_index, s=x_s, vex_y, edee_attr-egee_atr))
18
20
21
split scalar and vector channels return pplit_multt (message, ds,, d,, __conf)
22
def (self,, s_i, vii, s_t. v_j, edge_attz);
25
26
unstack scalar feats:
27
# [n_nodes, n_conf d] -> [n_nodes, n_conf, d_s]
s_i =
shi.shape[]]/// d_s)
s_j (s_j. shape [0], s_j.shape (1)/dd33, d_s)
30
31
# unstack vector feats:
32
# ln_nodes, n conf v -> nodes, n_nonf d,v, 3]
V_i w_i.view wwvieww__i.ee shape [0], [t_ d_v, 3) v_j v_j v_j.view(f [v_j shape [0], v_j. [1/ (d]v3 d_v, 3)
35
36
# message function for edge i-i
message = tuple_cate (aple_cat((s_})) ((s_), v_i), edge_attr, (s_i, v_i))
message self.message_fiu func (message) GVP
39
40
merge scalar and vector channels along axis 1
41
return merge_multi (+message)
def _split_mule (x, d_,, d_v, n_conf):
44
///
45
Splits merged representation of (s, v) back into a tuple.
46
xl ... :-3 d n_conf] wweefl..ieew...lll.... shape [0), conf, ds) v =xxl... -3 dv conof vveew shape [0], c_no, d,v, 3)
48
49
return 8, V
50
def __eerge_mlti v): ,,,
53
Merges a tuple (s, v) into a single 'torch. 'torch.Tensor", where the vector channels are flattened and appended to the scalar channels.
54
55
57
In_nodes, cono, dl -> In nodes, n_conf d_s] 8 s.view (s. [0], s.shape[1)
38
shapee
59
# v: [n_nodes, n_conf, d, 3] -> [n_nodes, __conf d_vx3] (.view(v.shapell
v.shape[1l torch. vl, -1)
[2]
Listing 1: PyG-style pseudocode for multi-state GVP-GNN layer. We update node features for
each conformational state independently while maintaining permutation equivariance the updated feature tensors along both the first (no. of nodes) and second (no. of conformations) axes.
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Histogram of sequence lengths
Histogram of no. of structures per unique se sequence Distribution: 2:86 9.39, Nax: 267, Min.
Distrbution: 684.9 1072.8. Max: 4455, Min:
Sequences structure
1450
303
2520
600
2020
1020
1520
400
1000
2000 3000
405n
30
Sesuence eogth
Wimthrr of smmcttrrs per sequeecc
(a) Sequence length. The dataset is long-tailed terms of RNA sequence length, with many short sequences including aptamers, riboswitches, ribozymes, and iRNAs (fewer than 200 nucleotides). The dataset also includes several longer ribosomal RNAs (thousands of nucleotides).
(b) Number of structures per sequence. The dataset covers wide range RNA conformation ensembles, with on average structures per sequence. There are multiple structures available for 1,547 sequences. The remaining 2,676 sequences have one correspond- ing structure.
Histogram of avg. pairwise RMSD per sequence Distribution: 1,33A 1.89, Max: 18.35A, Min. 0.00A
350
200
200
20
220
10
12.5
10
107
30'
Arg. pa relas IM10 among atruciures secuence
(c) Average pairwise RMSD per sequence. For 1, 1,547 sequences with multiple structures, there significant structural diversity among conformations. On average, the pairwise C4' RMSD among the set structures for sequence is greater than I
(d) Bivariate distribution for sequence length vs. avg. RMSD. The joint plot illustrates how structural diversity (measured by avg. pairwise RMSD) varies across sequence lengths. We notice similar structural variations regardless of sequence length.
Figure 13: RNASolo data statistics. We plot histograms visualise the diversity of RNAs available
in terms of (a) sequence length, (b) number of structures available per sequence, as well as (c) structural variation among conformations for those RNA that have multiple structures. The bivariate distribution plot (d) for sequence length vs. average pairwise RMSD illustrates structural diversity regardless of sequence lengths.
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E FAQS ON USING GRNADE
How to chose the number of states to provide as input to gRNAde? general, this would depend on the design objective. For instance, designing riboswitches may necessitate multi-state design, while a single-state pipeline may be more sensible for locking an aptamer into its bound conformation (Yesselman al., 2019). Note that may be possible benefit from multi-state gRNAde models even when performing single-state design by using slightly noised variations of the same backbone structure as an input conformational ensemble.
How to prioritise or chose amongst designed sequences? We have currently provided 3 types of evaluation metrics: native sequence recovery, structural seff-consistency scores and perplexity, towards this end. We suspect that recovery may not be the ideal choice, except for design scenarios where we require certain regions of the RNA sequence to be conserved or native-like. Self-consistency scores may provide an overall more holistic evaluation metric as they accounts for alternative base pairings which still lead to similar structures as well as better capture the recovery of structural motifs responsible for functionality. However, structural self-consistency scores inherit the limitations of the structure prediction methods used as part of their computation. For instance, computing the self- consistency
between RNA backbone and its own native sequence provides upper bounds on the maximum score that designs can obtain under a given structure prediction method. Lastly, gRNAde's 's perplexity estimates the likelihood of sequence given backbone and can be useful for ranking designs and mutants in RNA engineering campaigns (especially for design scenarios where structure prediction tools are not performant).
In real-world design scenarios, we can pair gRNAde with another machine learning model (an 'oracle') for ranking or predicting the suitability of designed sequences for the objective (for instance, binding affinity or some other notion of fitness). We hope to conduct further experimental validation of gRNAde designs in the wet lab in order to better understand these tradeoff'.
Why not average single-state logits over multiple states for multi-state design? ProteinMPNN (Dauparas et al., 2022) proposes to average logits from multiple backbones for multi-state protein design. Here is simple example to highlight issues with such approach: Consider two states A and B, and choice of labels X, Y. and Z. For state A: X, Y. Z are assigned probabilities 75%, 20% 5%. For state B: X. Y, Z are assigned probabilities 5%, 20%, 75%. Logically, label Y is the only one that is compatible with both states. However, averaging the probabilitics would lead to label X or being more likely to be sampled in designs. As alternative, gRNAde is based multi-state GNNs which can take as input one or more backbone structures and generate sequences conditioned on the conformational ensemble directly.
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F COMPARISON TO CONTEMPORANEOUS WORK
Independently of our work, Tan et al. (2023) also developed RDssggn, a deep learning- 3D RNA inverse folding model. While these papers were developed concurrently in 2023, RDesign was first to be accepted for formal publication at ICLR 2024, Here, we want to highlight key difference between gRNAde and RDesign, as well as technica/reproduucbiitty concerns with Tan et al. (2023).
Experimental validation: gRNAde designs work in the wet lab and have a significantly higher experimental success rate than Rosetta. RDesign's methodology restricts practical applicability for real-world usage (see subsequent points on sampling).
Methodology differences and issues with RDesign:
New capabilities: gRNAde enables explicit multi-state design to generate sequences conditioned on multiple backbone structures, which is not possible with Rosetta nor RDesign. We have also demonstrated the utility of gRNAde's perplexity for zero-shot ranking of mutants in RNA engineering campaigns.
Decoding: gRNAde uses an autoregressive decoder with rotation -euttiir-nuivaiiantt GNN layers. while RDesign uses a non-autoregressive (one-shot) decoder with rotation invarriant layers. In our ablation study in Appendix B, we found autoregressive decoding to show signifi- cantly higher 21) and 3D self-consistency scores than non-autoregressive decoding, even though non- autoerrrrrgresvvee decoding lead to higher sequence recovery. We also found that equivariant GNN layers improve performance over invariant layers.
Sampling: As of 01/08/2024, RDesign does not implement any sampling operation during inference, despite having a method called 'sample' in its model class. This means that any design produced by RDesign is deterministic and not diverse, restricting its practical usage. This also means that the metric reported as 'recovery' in the RDesign paper does not follow the standard definition in the protein design community. (Typically, computing recovery requires drawing samples from the probability distribution learnt by the model, whereas the RDesign paper reports classification accuracy.)
Evaluation and data splitting differences:
Evaluation metrics: RDesign's 's evaluation measures native sequence recovery, only. We have additionally introduced structural self-consistency metrics at the 2D and 3D level, which have been shown to better correlate with experimental success in protein design.
Perplexity: We found gRNAde's perplexity to be correlated with sequence and structural recovery, as well as demonstrated its utility for zero-shot ranking of mutants in RNA engi- neering. On the other hand, RDesign does not report perplexity and claims that perplexity is an unsuitable metric for RNA design. RDesign directly outputting probability distributions and not using any sampling (see previous point) can be one possible explanation of why RDesign models produce unsual perplexity values (Appendix E.3 of their paper, well as looking through the training logs released for their best checkpoint: training perplexity is close to 1, while validation perplexity is more than 22, suggesting overfitting and extremely poor generalisation.
Data splitring: While both studies use structural clustering to evaluate generalisation structurally dissimilar RNAs, RDesign's test splits are determined randomly without justification of whether the RNAS used for the test set are scientifically relevant. Our experiments use an expert curated test split of high-quality structured RNAs from Das et al. (2010) to fairly compare gRNAde to Rosetta, as well as a new split based on conformational flexibility to benchmark multi-state design.
Usage and reproducibility issues with RDesign:
We release open source training and inference code as well as model checkpoints to enable complete reproducibility. We also release Colab notebooks and detailed tutorials to make gRNAde broadly applicable and useful in real- world RNA design campaigns.
As of 01/08/2024, RDesign's codebase did not provide any installation instructions or training code to reproduce their work. None of the model, dataset, and trainer classes had any documentation regarding the expected data format, expected inputs and their shapes, etc. The dataset files released by RDesign were found to be corrupted (we tried loading them on a Macbook and two different Linux servers), all of which produced a Runt i time Froo stating "Invalid magic number; corrupt file".
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Improved performance of gRNAde over RDesign:
As of 01/08/2024, despite the reproducibility issues and lack of documentation in the code, we were able to renerse-engineer the best model checkpoint released with DDsigg and run inference our single-state evaluation set. In Figure 2a and Table 2, we find that RDesign significantly underperforms gRNAde as well as Rosetta, obtaining an overall recovery rate of 43% compared 45% for Rosetta and 56% for gRNAde. In Figure 6, see that gRNAde outperforms RDesign on all of the high-quality structured RNAs of interest identified by Das al. (2010).
In our ablation studies Appendix B, we fairly compare the performance of gRNAde's autoregressive decoder and equivariant GNN layers with -on-autoregressvve and invari- ant GNN variants (which are what RDesign uses in their architecture). This is direct, apples-to-apples comparison of key architectural differences between gRNAde and RDe- sign in order to uncover the source of improvement. We find that gRNAde variants with non-autoregressive decoding can improve sequence recovery but that autoregressive de- coding has significantly higher 2D and 3D celf-connistency scores (which we care about more in real-world design scenarios). We also find that equivariant GNN layers improve performance over invariant GNN layers.
We believe our study brings significant new contributions and insights as well as resources to the community, and that there space for multiple papers offering different perspectives on the same topic. At the same time, we have found it challenging to reproduce Tan et al. (2023) due to several methodology and reproducibility issues, which want highlight via this section.
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Reviews:
[{"review_id": "3L0lQ64YKy", "reviewer": "Reviewer_peU4", "summary": "The manuscript presents gRNAde, an approach for RNA inverse design conditioned on the 3D structure of RNA (conformational ensemble). The method uses geometric deep learning using a graph neural network which receives a set of backbone graphs derived from the 3D structure and predicts a set of RNA sequences (2D sequences of nucleotides (G,A,C,U).  \n\nThe approach is similar to ProteinMPNN (Daupara 2022) that solved a similar inverse problem for proteins. \n\nThe two key related works are Rosetta (Leman 2020) and RDesign (Tan et al. 2023), a comparison to the latter is presented in pages 16-17. In particular, RDesign is another deep learning based method for inverse RNA design. Among various issues that the authors highlight in the appendix, it appears that the proposed method is open source, outperforms this baseline, and can offer sampling, which is attractive for producing multiple solutions.", "questions": "- Please explain what happens to the other non-successful examples from Eterna which are not successful. What is the intuition of why the proposed method (as well as Rosetta) does not work? Is this due to challenging structural motifs, sequence patterns, or other type of structural features? Are there inputs that gRNAde will be more suited for in comparison to other methods, and vice versa?  \n\n- Would the method generalize to unseen datasets/sources? \n\n- In Figure 17, various properties of RNA sequences are presented for the dataset used (sequence length, number of structures/sequences, average pairwise RMSD per sequence, bivariate distribution for sequence length vs. avg. RMSD). Do these properties affect performance results when the model is evaluated?\n\n- Are there any biases of the model, e.g., to certain pairs of nucleotides, e.g., CG pairs, see (Frnakenstein, Lyngsoe 2012)?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The methodology is evaluated using both in silico and wet lab approaches. For in silico evaluation, three metrics (sequence recovery, self consistency, and perplexity) are reported. These metrics are described on page 16. The “wet lab” experiments are run on Eterna, an independent online computational RNA design platform, and showed an impressive performance improvement over Rosetta in the OpenKnot score.\n\nThe authors also create their own dataset using RNASolo (Adamczyk 2022). The authors should clarify this as an additional contribution if it is, or otherwise explain what are the modifications introduced to RNASolo. Will this new dataset also be released?\n\nThe paper is well written, polished and includes beautiful diagrams. The code is released open source (and added to the submission).", "weaknesses": "The authors should present a comprehensive technical comparison to other methods (e.g., Rosetta) not just RDesign (Tan et al. 2023) to enable better understanding of the proposed functionality. Specifically, please briefly summarize the technical components of Rosetta, and explain how gRNAde graph deep learning framework is different. \n\nThe authors should focus their discussion on the technical similarities and differences to previous work, without negativity (“This means that any design produced by RDesign is deterministic and not diverse, making it useless in practical scenarios. All recovery metric results reported in the RDesign paper are also incorrect due to this issue (at least based on how recovery is computed in protein design).“ (Page 16). There is no wet lab comparison to RDesign, and the authors should explain why this is not possible (e.g., using the re-engineered checkpoint that was used to compute some performance comparisons using recovery reported in Table 2 and Figure 2a).", "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": "TbfU6284kG", "reviewer": "Reviewer_qxae", "summary": "This paper develops an inverse folding model for RNA (structure -> sequence), analogous to proteinMPNN for protein sequences. Unlike prior work, it is able to consume multiple conformations as input. The model performs well compared to popular baselines such as Rosetta on a wide variety of evaluation tasks.", "questions": "I don't know much about the landscape of tools for this sort of design. My impression is that current versions of Rosetta do not support RNA design. Why is that? Is it because it was found that Rosetta did not perform well for it or simply that there wasn't support to maintain these capabilities in the software? Is it a strong enough baseline to be compare against?\n\nIt would be good to discuss the methodological similarities/differences vs. RDesign in the main paper too. Given the details in the appendix, I know this is a complex topic. Can you provide a short piece of text that you'd include in a new version of the main paper describing the overlap?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The paper is well written, well motivated, and the results are convincing. As a reviewer, I found the \"COMPARISON TO CONTEMPORANEOUS WORK\" section in the appendix highly useful. Thank you for including this. The paper has a remarkable number of results. The analysis in the appendix about model ablations would typically appear in the main paper, but there simply wasn't space.", "weaknesses": "I don't have enough background on RNA to understand some details of the evaluation, but I am familiar with analogous evaluations for proteins and am under the impression that the evaluation is quite comprehensive. It would be great if other reviewers with more RNA background could assess these details.", "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": "OQAuDWwGbl", "reviewer": "Reviewer_47od", "summary": "This paper presents gRNAde, a geometric deep learning-based approach for RNA inverse design task focused on creating RNA sequences conditioned on provided 3D conformations. gRNAde supports single- and multi-state RNA designs. The method has been compared against physics based tools such as Rosetta software with gRNAde showing better performance in multiple applications with faster processing times. The paper also shows gRNAde's potential for zero-shot learning in RNA fitness landscapes.", "questions": "I have already left suggestions for further experiments that may help address some of the concerns in the weaknesses section. To summarize: \n1. Does the model incorporate RNA modifications, cellular localization, or folding kinetics? Can the authors add some discussion on how might this enhance performance given the limited experimental training data or if they can provide some analysis for the same, if possible? \n2. Apart from sequence recovery and perplexity, did the authors analyze structural stability and functional motifs that affect RNA viability?\n3. Did the authors experiment with different RNA backbone representations, and what impact did this choice have on the results?\n4. Can the authors analyze failure cases to improve understanding of gRNAde's limitations?\n5. Have the authors evaluated how varying RNA sequence lengths affect performance?\n6. Will the authors benchmark against the RNAInvBench paper (Cole et al., 2024) to assess the role of pseudoknots in their predictions as its importance has been highlighted as a crictical aspect to compare RNA inverse design models in RNAInvBench paper?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper studies an important problem of RNA inverse design inspired by similar works in protein space. Given that RNA 3D experimental data is much more limited and the structure of RNA being much more flexible than proteins, innovative approaches for this problem are desirable. The method although builds on existing architectures, employs sensible design suitable for RNA modeling (e.g.- using a three-bead (pseudotorsional) representation of the RNA backbone, minimizing the high-dimensional torsional space). \n2. gRNAde introduces an innovative multi-state GNN that models conformational ensembles—distinct structural states of RNA—by encoding them as a geometric multi-graph. The model's ability to simultaneously consider multiple conformations (or states) enhances its accuracy in predicting sequences that can adopt different functional forms.\n3. Benchmarking against Rosetta and other tools shows higher higher sequence recovery rate and speedup for gRNAde showcasing its usability for for high-throughput scenarios.\n4. The paper is well-written and easy to follow.", "weaknesses": "Although the paper explores an interesting problem and presents a sensible approach, there are some aspects which can be improved.\n\n\n1. I did not find any discussion on if the model explicitly incorporates any biological nuances such as RNA modifications, cellular localization, or folding kinetics, which are essential for in vivo RNA functionality. Given that experimental training data is limited for RNA, such information may enhance performance especially that Physics based models often use such information as well. Some discussion around this can be helpful.\n\n2. The evaluation focuses primarily on sequence recovery and perplexity. However, there are other aspects such as structural stability, functional motifs, etc. that affect RNA design which if not accounted for may produce RNA designs that are theoretically correct but biologically unviable. Did the authors perform any analysis of these aspects in their designed RNAs?\n\n3. There are various ways to extract or represent the RNA backbone in molecular modeling and structural biology without clear consensus (see Richardson et al., 2008, RNA backbone: Consensus all-angle conformers and modular string nomenclature). Did the authors experiment with other backbone representations to see the impact of the particular choice of backbone representations?\n\n4. From evaluation perspective, I have several suggestions which may help understanding the performance of gRNAde:\n (a) I did not see any analysis on failure cases, thus limiting the understanding of scenarios where gRNAde may perform poorly, hindering its reliability and robustness.\n(b) Additionally, an evaluation of how varying RNA sequence lengths affect the performance may lead to better understanding of the pros and cons of gRNAde.\n(c) Finally, pseudoknots are important in RNA secondary structure, which can lead to incomplete or inaccurate folding predictions, as pseudoknots play a critical role in the functional conformation of RNA molecules. The RNAInvBench paper (Cole et al., 2024) includes the pseudoknot-free and pseudoknot-inclusive benchmarks for RNA inverse design. It will be good to also compare against this benchmark.", "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": "Z8dgt0nsce", "reviewer": "Reviewer_sLwi", "summary": "This paper is dedicated to solving the inverse folding problem of RNA. Given multiple 3D conformations of an RNA, the method designs the corresponding RNA sequence. The experimental section demonstrates the effectiveness of this approach.", "questions": "None", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This is an interesting paper. The biggest difference from past methods is that this paper can take into account the flexibility of RNA structures, accept conformations of multiple RNAs simultaneously, and designs a reasonable model. I agree that this is a valid innovation point. This inclines me to accept the paper.", "weaknesses": "Although this paper contains some very interesting innovations, several key issues have led me to give it the current rating. I hope the authors can fix these problems. If these issues can be resolved, I will consider improving my rating.\n\n1. In the first sentence of the abstract, the authors claim: \"Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure $\\textbf{without considering 3D geometry}$ and conformational diversity.\" I believe that the phrase \"without considering 3D geometry\" is factually incorrect. Because RDesign[1] performs inverse folding based on the 3D structure of RNA, it should take 3D geometry into account. The authors should provide a reasonable explanation or correct all descriptions in the paper that claim \"without considering 3D geometry.\"\n\n[1] RDESIGN: HIERARCHICAL DATA-EFFICIENT REPRESENTATION LEARNING FOR TERTIARY STRUCTURE-BASED RNA DESIGN. $\\textbf{ICLR 2024}$\n\n2. The experimental section lacks a comparison with RDESIGN, which is also deep-learning-based. Moreover, in the abstract, gRNAde should be compared with RDESIGN rather than Rosetta, which would be more reasonable. Figure 3(a) shows the performance of gRNAde under different numbers of conformations. This experiment also needs to be compared with RDESIGN. Although RDESIGN can only accept one conformation, the authors should use the result of RDESIGN with a single conformation as a baseline for comparison.", "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 (Spotlight)