mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
- URL: http://arxiv.org/abs/2605.31296v1
- Date: Fri, 29 May 2026 13:32:39 GMT
- Title: mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
- Abstract summary: We present mRNA generation via unrolled trajectories and informed latent updates (mRNAutilus)<n>mRNAutilus is a framework for simultaneous codon optimization and de novo UTR design directly from sequence.<n>We demonstrate generality in therapeutic settings, including prime editing (PEMax) and programmable proteome modulation.
- Score: 3.5240650455251625
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regions (UTRs), and their coupling jointly determine stability, translation efficiency, and protein expression. Here, we present mRNA generation via unrolled trajectories and informed latent updates (mRNAutilus), a framework for simultaneous codon optimization and de novo UTR design directly from sequence. mRNAutilus combines a masked discrete diffusion model trained on millions of full-length mRNAs with Monte Carlo Tree Guidance to generate Pareto-efficient sequences under multiple functional objectives, using lightweight regressors over model embeddings to predict half-life, translation efficiency, and protein abundance. Unlike recent methods that design coding sequences and UTRs separately or rely on post hoc assembly and screening, mRNAutilus generates complete transcripts in a single process optimized across properties. Across diverse targets, zero-shot mRNAs encoding P. pyralis luciferase achieve over 400-fold higher expression than wild-type and outperform commercial and machine learning-designed baselines, including zero-shot generative approaches. Zero-shot SARS-CoV-2 Spike mRNAs exceed clinically used and commercial constructs and match or surpass lab-optimized designs with improved durability. We further demonstrate generality in therapeutic settings, including prime editing (PEMax) and programmable proteome modulation, where mRNAutilus-designed constructs enhance expression of peptide-guided E3 ligases (uAbs) for beta-catenin degradation. These results establish a sequence-based, multi-objective framework for generating functional mRNAs tailored to diverse biological applications.
Related papers
- Protein-Conditioned Multi-Objective Reinforcement Learning for Full-Length mRNA Design [2.9395143551178333]
We propose ProMORNA, a multi-objective generation framework that produces complete mRNA transcripts directly from a target sequence.<n>Our approach begins by training a BART-style encoder-decoder model on over 6 million natural protein-mRNA pairs.<n>As a case study, we evaluated ProMORNA on the widely used firefly luciferase target, excluding it from both our supervised training data and the prompt pool.
arXiv Detail & Related papers (2026-05-02T16:05:51Z) - RiboSphere: Learning Unified and Efficient Representations of RNA Structures [57.40815107640066]
RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.<n>We introduce emphRiboSphere, a framework that learns emphdiscrete geometric representations of RNA by combining vector quantization with flow matching.
arXiv Detail & Related papers (2026-03-20T04:36:35Z) - RNAGenScape: Property-guided Optimization and Interpolation of mRNA Sequences with Manifold Langevin Dynamics [14.76553653030291]
RNAGenScape is a property-guided manifold Langevin dynamics framework that iteratively updates mRNA sequences within a learned latent manifold.<n>It supports property-guided optimization and smooth design between sequences, while remaining robust under scarce and undersampled data.<n> RNAGenScape establishes a paradigm for controllable mRNA design and latent space exploration in mRNA sequence modeling.
arXiv Detail & Related papers (2025-10-14T19:55:41Z) - Equi-mRNA: Protein Translation Equivariant Encoding for mRNA Language Models [0.0]
We introduce Equi-mRNA, the first codon-level equivariant mRNA language model that explicitly encodes synonymous codon symmetries as cyclic subgroups of 2D Special Orthogonal matrix (SO(2))<n>On downstream property-prediction tasks including expression, stability, and riboswitch switching, Equi-mRNA delivers up to approximately 10% improvements in accuracy.<n> Equi-mRNA establishes a new biologically principled paradigm for mRNA modeling, with significant implications for the design of next-generation therapeutics.
arXiv Detail & Related papers (2025-08-20T22:42:10Z) - A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors [12.26159226306187]
We introduce textbfRNop, a novel deep learning-based method for mRNA optimization.<n>We collect a large-scale dataset containing over 3 million sequences and design four specialized loss functions, the GPLoss, CAILoss, tAILoss, and MFELoss.<n>RNop ensures high sequence fidelity, achieves significant computational throughput up to 47.32 sequences/s, and yields optimized mRNA sequences.
arXiv Detail & Related papers (2025-05-29T08:21:11Z) - Regulatory DNA sequence Design with Reinforcement Learning [56.20290878358356]
We propose a generative approach that leverages reinforcement learning to fine-tune a pre-trained autoregressive model.<n>We evaluate our method on promoter design tasks in two yeast media conditions and enhancer design tasks for three human cell types.
arXiv Detail & Related papers (2025-03-11T02:33:33Z) - Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics [3.2508287756500165]
mRNA-based vaccines have become a major focus in the pharmaceutical industry.<n> optimizing mRNA sequences for those properties remains a complex challenge.<n>We present Helix-mRNA, a structured state-space-based and attention hybrid model to address these challenges.
arXiv Detail & Related papers (2025-02-19T14:51:41Z) - Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification [55.98854157265578]
Life-Code is a comprehensive framework that spans different biological functions.<n>We propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences.<n>Life-Code achieves state-of-the-art results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation.
arXiv Detail & Related papers (2025-02-11T06:53:59Z) - DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures.
DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals.
Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
arXiv Detail & Related papers (2024-10-17T17:20:24Z) - Latent Diffusion Models for Controllable RNA Sequence Generation [33.38594748558547]
RNA is a key intermediary between DNA and protein, exhibiting high sequence diversity and complex three-dimensional structures.
We develop a latent diffusion model for generating and optimizing discrete RNA sequences of variable lengths.
Empirical results confirm that RNAdiffusion generates non-coding RNAs that align with natural distributions across various biological metrics.
arXiv Detail & Related papers (2024-09-15T19:04:50Z) - BEACON: Benchmark for Comprehensive RNA Tasks and Language Models [60.02663015002029]
We introduce the first comprehensive RNA benchmark BEACON (textbfBEnchmtextbfArk for textbfCOmprehensive RtextbfNA Task and Language Models).<n>First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications.<n>Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models.<n>Third, we investigate the vital RNA language model components
arXiv Detail & Related papers (2024-06-14T19:39:19Z) - Accurate RNA 3D structure prediction using a language model-based deep learning approach [50.193512039121984]
RhoFold+ is an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences.<n>RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction.
arXiv Detail & Related papers (2022-07-04T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.