RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics

Danqi Liao; Chen Liu; Xingzhi Sun; Dié Tang; Haochen Wang; Scott Youlten; Srikar Krishna Gopinath; Haejeong Lee

q-bio.QMOct 14, 2025

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics

Authors: Danqi Liao, Chen Liu, Xingzhi Sun, Dié Tang, Haochen Wang, Scott Youlten, Srikar Krishna Gopinath, Haejeong Lee, +3 more

Organizations: Yale University

Abstract

Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains challenging due to limited data, complex sequence-function relationships, and the narrow space of biologically viable sequences. Generative methods that drift away from the data manifold can yield sequences that fail to fold, translate poorly, or are otherwise nonfunctional. We present RNAGenScape, a property-guided manifold Langevin dynamics framework for mRNA sequence generation that operates on a learned manifold of real data. By performing iterative local optimization constrained to this manifold, RNAGenScape preserves favorable computational viability proxies, accesses reliable guidance, and discourages excursions into regions poorly supported by the training data. The framework integrates three components: (1) an autoencoder jointly trained with a property predictor to learn a property-organized latent manifold, (2) a denoising autoencoder that projects updates back onto the manifold, and (3) a property-guided Langevin dynamics procedure that performs optimization along the manifold. Across three real-world mRNA datasets spanning two orders of magnitude in size, RNAGenScape increases median oracle-predicted property gain by up to 267% and success rate by up to 31%, while maintaining good biological viability proxy measures and achieving competitive inference efficiency compared to existing generative approaches.

Figures & tables

Figure 1: Illustration of manifold Langevin dynamics for optimization on the manifold of real data.

Figure 2: Schematic of RNAGenScape . a, We encode a given input mRNA sequence into a latent manifold organized by property, and iteratively optimize the sequence along the property landscape. The manifold projector $\Psi$ encourages the optimization to stay on or near the manifold. Notably, we can decode the intermediate optimization result after every step. b, The organized latent manifold is created by jointly optimizing reconstruction and property prediction objectives. c, The manifold projector is trained to project perturbed data points back to the manifold. d, For undersampled mRNA manifolds, we use SUGAR [ 42 ] to learn key dimensions in the manifold and fill undersampled regions. e, During optimization, the manifold projector brings off-manifold points back to the manifold.

Figure 3: Overview of the experimental design. The same procedure is repeated for every mRNA dataset. a, We first fine-tune and evaluate a pre-trained oracle model that predicts target properties from mRNA sequences. b, We then train each sequence optimization method using the training and validation sets. c, The trained methods are applied to optimize mRNA sequences starting from unseen test samples. d, Finally, the oracle model is used to evaluate property improvement and success rate.

Method	$\mathcal{M}$	Property Optimization				Biological Viability Checks
		$+$ Property		$-$ Property		Initiation Interference	Initiation Context	Structural Stability	Structural Confidence
		$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	uORF OOF AUG % $\downarrow$	Kozak Similarity % $\uparrow$	Minimum Free Energy $\downarrow$	Mean pLDDT $\uparrow$
OpenVaccine (n $\approx$ 2k) , property to optimize: stability
Data in the test set	—	—	—	—	—	$1.75$ $\pm~0.12$	$28.70$ $\pm~0.62$	$-29.82$ $\pm~0.66$	$42.18$ $\pm~0.24$
DiffAb [ 50 ]	✗	$+0.49$ $\pm~0.09$	$72.5$ $\pm~3.4$	$-0.20$ $\pm~0.16$	$60.1$ $\pm~7.3$	$1.52$ $\pm~0.55$	$22.13$ $\pm~5.68$	$-14.41$ $\pm~3.89$	$35.57$ $\pm~3.00$
IgLM [ 66 ]	✗	$+0.59$ $\pm~0.02$	$80.6$ $\pm~0.9$	$+0.43$ $\pm~0.06$	$21.4$ $\pm~3.0$	$2.01$ $\pm~0.16$	$28.04$ $\pm~2.70$	$-15.68$ $\pm~0.51$	$38.00$ $\pm~0.77$

Table 1: Our proposed RNAGenScape generates mRNA sequences that achieve strong property improvement while maintaining favorable computational viability proxies.

\mathcal{M}

denotes whether the method takes the manifold structure into consideration. In property optimization results,

\Delta

denotes the median change in property and

\%

denotes success rate (percentage of mRNAs improved). In the biological viability checks, uORF OOF AUG denote out-of-frame start codon in the upstream open reading frame, Kozak similarity is the similarity to the consensus Kozak sequence, and mean pLDDT is a structural-confidence score averaged over nucleotides.

Method	$\mathcal{M}$	Property Optimization
		$+$ Property		$-$ Property
		$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$
Zebrafish (n $\approx$ 55k) , property to optimize: translation efficiency
DiffAb [ 50 ]	✗	$+0.25$ $\pm~0.04$	$82.7$ $\pm~\phantom{0}3.0$	$+0.00$ $\pm~0.03$	$49.6$ $\pm~\phantom{0}4.4$
IgLM [ 66 ]	✗	$+0.03$ $\pm~0.00$	$56.5$ $\pm~\phantom{0}0.6$	$-0.01$ $\pm~0.00$	$52.5$ $\pm~\phantom{0}1.0$
NOS-C [ 26 ]	✗	$+0.07$ $\pm~0.02$	$77.6$ $\pm~\phantom{0}8.4$	$-0.04$ $\pm~0.04$	$67.2$ $\pm~14.8$

Table 2: Reproducing results in Table 1 using third-party checkpoints which fine-tuned UTR-LM on external data as the oracles for translation efficiency ( UTR-LM-TE ) and ribosome loading ( UTR-LM-MRL ). These oracles are not fine-tuned on our datasets. Despite potential distribution shifts that limit direct comparison of predicted property values with those in Table 1 , consistent gains suggest that our improvements are not solely due to shared predictor-oracle biases.

	DiffAb	IgLM	NOS-C	NOS-D	PCD	EM	MFM	gg-dWJS	MPGD	RNAGenScape
	[ 50 ]	[ 66 ]	[ 26 ]	[ 26 ]	[ 73 ]	[ 4 ]	[ 34 ]	[ 32 ]	[ 28 ]	(ours)
OpenVaccine (n $\approx$ 2k)	$0.424\pm 0.015$	$0.583\pm 0.005$	$0.421\pm 0.001$	$0.417\pm 0.001$	$0.546\pm 0.001$	$0.416\pm 0.002$	$0.433\pm 0.009$	$0.479\pm 0.048$	$0.412\pm 0.002$	$\textbf{0.411}\pm 0.023$
Zebrafish (n $\approx$ 55k)	$0.643\pm 0.002$	$0.642\pm 0.001$	$0.637\pm 0.004$	$0.635\pm 0.004$	$0.687\pm 0.001$	$0.653\pm 0.004$	$0.640\pm 0.003$	$0.638\pm 0.003$	$0.642\pm 0.000$	$\textbf{0.632}\pm 0.001$
Ribosome (n $\approx$ 260k)	$0.501\pm 0.001$	$0.509\pm 0.000$	$0.502\pm 0.001$	$0.497\pm 0.001$	$0.524\pm 0.000$	$0.510\pm 0.002$	$0.509\pm 0.003$	$0.504\pm 0.002$	$0.513\pm 0.001$	$\textbf{0.491}\pm 0.005$

Table 3: Mean normalized Hamming distance between generated sequences and their nearest favorable-property test-set references. Distances lie in

[0,1]

, with lower values being better.

Table 7

	OpenVaccine (n $\approx$ 2k)				Zebrafish (n $\approx$ 55k)				Ribosome-loading (n $\approx$ 260k)
	$+$ property		$-$ property		$+$ property		$-$ property		$+$ property		$-$ property
SUGAR ratio	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$
0	$+0.44$	$73.2$	$-0.97$	86.2	$+0.14$	$54.7$	$-0.98$	$73.0$	$+0.83$	$83.7$	$-0.74$	$67.9$
0.01	$+0.08$	$56.9$	$-0.62$	$67.5$	$+0.69$	$70.7$	$-1.20$	80.0	$+0.53$	$78.3$	$-0.20$	$51.3$
0.1	$+0.20$	$58.1$	$-0.51$	$74.0$	$+0.65$	$70.8$	$-0.93$	$75.3$	$+0.49$	$69.6$	$-1.18$	$71.4$
1.0	$+\textbf{0.92}$	83.6	$-0.77$	$84.2$	$+\textbf{0.79}$	70.9	$-\textbf{1.25}$	$77.3$	$+\textbf{0.93}$	88.8	$-\textbf{1.93}$	92.8

Table 6: Ablation study on the data augmentation ratio of SUGAR.

Panel of Figure 4 — Figure 4: RNAGenScape optimization is step-efficient and remains stable over a range of optimization steps.

Figure 6: A sample latent space trajectory of RNAGenScape over 10 optimization steps. The trajectory follows a smooth path with steady improvement in the target property. a, The trajectory in the PHATE space and the corresponding 3D structures. b, Intermediate results of RNAGenScape and competing methods midway through property optimization (at the 5 th step out of 10 steps).

Figure 7: Conceptual comparison of sampling and guidance mechanisms relevant to our property-guided optimization problem. a, Unguided reverse diffusion. b, Diffusion posterior sampling [ 17 ] adds guidance to the reverse diffusion. c, Manifold Preserving Guided Diffusion (MPGD) [ 28 ] constrains guidance through a local manifold projection before reverse diffusion. d, Guided Langevin sampling in energy-based models [ 4 ] combine guidance with energy descent; contours depict the learned energy landscape. e, RNAGenScape alternates property-guided updates and a SUGAR-assisted denoising projection in the OAE latent space. The distinct projection mechanisms used by MPGD and RNAGenScape are represented in the same color since they share the same high-level concept, whereas implementation details and stochastic perturbations are omitted from the illustration.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	$\lambda_{\text{Recon}}$ / $\lambda_{\text{Pred}}$	Noise levels	Langevin (step size, temperature)
OpenVaccine	5.0 / 1.0	{0.5, 0.2, 0.1}	$1\times 10^{-2},\,1\times 10^{-3}$
Zebrafish	5.0 / 1.0	{0.5, 0.2, 0.1}	$1\times 10^{-3},\,1\times 10^{-3}$
Ribosome	5.0 / 1.0	{0.5, 0.2, 0.1}	$1\times 10^{-3},\,1\times 10^{-3}$

Appendix

Table S1: Hyperparameters used for different datasets. All runs use latent dimension

128

, denoising autoencoder manifold projector with

\mathrm{sugar}_{w}{=}1

, and

100

Langevin steps.

	Property Prediction
	Pearson Corr. $\uparrow$	Spearman Corr. $\uparrow$
OpenVaccine (n $\approx$ 2k)	0.62	0.73
Zebrafish (n $\approx$ 55k)	0.82	0.81
Ribosome (n $\approx$ 260k)	0.93	0.91

Appendix

Table S2: The UTR-LM oracle for property evaluation.

region	$N$	density	nearest-neighbor distance	grad norm	prediction variance
on-manifold, dense	500	$39.1\pm 8.6$	$6.273\pm 0.428$	$0.398\pm 0.146$	$0.127\pm 0.103$
on-manifold, sparse	500	$\phantom{0}9.1\pm 3.2$	$8.254\pm 0.669$	$0.345\pm 0.119$	$0.182\pm 0.142$
off-manifold	500	$\phantom{0}0.0\pm 0.0$	$399.3\pm 25.7$	$0.192\pm 0.130$	$0.583\pm 0.430$

Appendix

Table S3: Property-gradient analysis in the OAE latent space on the Ribosome dataset. Reported values are the within-region mean and standard deviation averaged over five random seeds. Gradient norms are the raw

\ell_{2}

norms

\|\nabla_{z}f(z)\|_{2}

of the OAE property head.

	OpenVaccine (n $\approx$ 2k)				Zebrafish (n $\approx$ 55k)				Ribosome-loading (n $\approx$ 260k)
	$+$ property		$-$ property		$+$ property		$-$ property		$+$ property		$-$ property
$\Psi$ design	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$
$k$ NN ( $k{=}1$ )	$+0.01$	$53.2$	$+0.00$	$50.3$	$-0.22$	$45.8$	$-0.42$	$61.5$	$+0.05$	$55.5$	$-0.09$	$56.7$
$k$ NN ( $k{=}5$ )	$+0.06$	$56.2$	$+0.05$	$45.4$	$-0.22$	$46.0$	$-0.37$	$59.7$	$-1.05$	$\phantom{0}9.7$	$-1.17$	$92.4$
$k$ NN ( $k{=}10$ )	$+0.10$	$58.6$	$+0.07$	$43.1$	$-0.22$	$45.7$	$-0.29$	$58.2$	$-1.09$	$\phantom{0}8.9$	$-1.21$	$92.5$
DAE, single-step	$+\textbf{0.93}$	88.3	$-\textbf{0.79}$	87.4	$+0.24$	$58.1$	$-0.83$	$72.1$	$+0.75$	$84.4$	$-0.99$	$74.4$

Appendix

Table S4: Ablation study on the design of the manifold projector

\Psi

. A simple

k

NN retractor is competitive in some settings but does not consistently support optimization in both directions. With a denoising autoencoder (DAE) as

\Psi

, multi-step denoising outperforms single-step denoising on Zebrafish and Ribosome-loading , and stays reasonably close on OpenVaccine .

	OpenVaccine (n $\approx$ 2k)				Zebrafish (n $\approx$ 55k)				Ribosome-loading (n $\approx$ 260k)
	$+$ property		$-$ property		$+$ property		$-$ property		$+$ property		$-$ property
$\lambda_{\text{Recon}}$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$
0.0	$-1.29$	$16.2$	$-2.40$	$91.0$	$-0.79$	$33.9$	$-0.34$	$54.9$	$+0.05$	$54.0$	$-0.21$	$57.3$
0.1	$+1.00$	$66.8$	$-1.60$	$88.5$	$-0.11$	$51.3$	$-1.08$	$76.1$	$+0.39$	$77.5$	$-1.20$	$90.6$
0.5	$+0.93$	$87.2$	$-\textbf{2.92}$	99.4	$+0.51$	$63.7$	$-1.07$	$76.7$	$+0.85$	$88.7$	$-0.98$	$69.5$
1.0	$+\textbf{1.11}$	91.4	$-1.31$	$87.8$	$+\textbf{1.13}$	81.2	$-1.09$	$77.2$	$+0.71$	$85.4$	$-0.88$	$69.6$

Appendix

Table S5: Ablation study on the reconstruction weight

\lambda_{\text{Recon}}

. Although

\lambda_{\text{Recon}}=1.0

outperforms

\lambda_{\text{Recon}}=5.0

on OpenVaccine in both optimization directions, we decide to use

\lambda_{\text{Recon}}=5.0

across all datasets to maintain a consistent hyperparameter setting.

Method	Property Optimization
	$+$ Property		$-$ Property
	$\Delta$ $\uparrow$	% $\uparrow$	$\Delta$ $\downarrow$	% $\uparrow$
OpenVaccine (n $\approx$ 2k)
VAE [ 36 ]	$+0.23$	$56.5$	$+0.23$	$43.5$
DDPM [ 30 ]	$-0.33$	$35.1$	$-0.33$	$64.9$
LDM [ 61 ]	$-0.41$	$32.4$	$-0.41$	$67.6$

Appendix

Table S6: RNAGenScape achieves better property optimization than de novo generative methods and classic optimization algorithms that operate on our learned data manifold.

Figure S1: Latent space interpolation trajectories from 5 sources to 4 targets. Each trajectory is shown as a line fading from bright to dark in a consistent color. RNAGenScape produces smooth and coherent paths on the manifold between arbitrary input-target mRNA pairs.

Figure S2: Latent space $\ell_{2}$ distances during interpolation show smooth and monotonic transition from the source to the target. Results are averaged over all data samples.

Figure S3: More examples of latent manifold trajectories. Trajectories in PHATE space and corresponding 3D structures are shown.

Figure S4: More examples of latent manifold trajectories. Trajectories in PHATE space and corresponding 3D structures are shown.

Figure S5: More examples of latent manifold trajectories. Trajectories in PHATE space and corresponding 3D structures are shown.

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