Liang Huang

Synthetic circular mRNA (hereafter referred to as circRNA) reduces susceptibility to exonuclease‐mediated degradation by its covalently closed circular structure, enabling prolonged protein expression for therapeutic applications. In this circular format, protein expression from engineered circRNAs is achieved mainly through cap‐independent translation initiation, commonly mediated by internal ribosome entry site (IRES) elements whose activity is influenced by RNA structure. Consequently, the coding sequence (CDS) and other elements should be designed with consideration of inter‐region base pairing that can shift IRES folding, a constraint not explicitly addressed by existing linear mRNA CDS optimization algorithms. Here, we present circ Design, an algorithm that explicitly incorporates IRES structural deviation into circRNA sequence design while jointly optimizing codon adaptation and thermodynamic stability. In a rabies virus glycoprotein (RABV‐G) vaccine model, circ Design‐generated circRNAs showed improved stability, translation efficiency, and vaccine immunogenicity compared with benchmark sequences optimized using conventional linear mRNA CDS design strategies, with CR3 achieving a 3.5‐fold increase in neutralizing antibody titers. Polysome profiling and targeted IRES‐disruption experiments support IRES structural integrity as a critical determinant of circRNA translation performance. Together, these results establish IRES structural preservation as a mechanistic design principle for circRNA engineering and position circ Design as a rational framework for therapeutic circRNA development.

Synthetic circular mRNA (hereafter referred to as circRNA) reduces susceptibility to exonuclease‐mediated degradation by its covalently closed circular structure, enabling prolonged protein expression for therapeutic applications. In this circular format, protein expression from engineered circRNAs is achieved mainly through cap‐independent translation initiation, commonly mediated by internal ribosome entry site (IRES) elements whose activity is influenced by RNA structure. Consequently, the coding sequence (CDS) and other elements should be designed with consideration of inter‐region base pairing that can shift IRES folding, a constraint not explicitly addressed by existing linear mRNA CDS optimization algorithms. Here, we present circ Design, an algorithm that explicitly incorporates IRES structural deviation into circRNA sequence design while jointly optimizing codon adaptation and thermodynamic stability. In a rabies virus glycoprotein (RABV‐G) vaccine model, circ Design‐generated circRNAs showed improved stability, translation efficiency, and vaccine immunogenicity compared with benchmark sequences optimized using conventional linear mRNA CDS design strategies, with CR3 achieving a 3.5‐fold increase in neutralizing antibody titers. Polysome profiling and targeted IRES‐disruption experiments support IRES structural integrity as a critical determinant of circRNA translation performance. Together, these results establish IRES structural preservation as a mechanistic design principle for circRNA engineering and position circ Design as a rational framework for therapeutic circRNA development.

RNA design, also known as RNA inverse folding, aims to find RNA sequences that fold into a target secondary structure. However, recent work has shown that some target structures are provably undesignable, where no RNA sequence can fold into it as the minimum free energy (MFE) structure. In this paper, we go beyond this binary, MFE-based designability and explore a soft, probability-based designability that upperbounds the Boltzmann probability of any design and quantifies how easily or likely any design might possibly fold into the target structure. We introduce a theory of ensemble approximation and a probability decomposition framework for bounding the folding probabilities of RNA structures and motifs in an explainable way. We further develop a linear-time dynamic programming algorithm that efficiently searches over exponentially many decompositions. Combining ensemble approximation with dynamic decomposition search, our method efficiently identifies the optimal motif decomposition that yields the tightest probabilistic bound for a given structure. Our framework is applicable to any factorizable energy model or scoring function that decomposes onto loops. Applying our work, LinearDecompose, to both native and artificial RNA structures in the ArchiveII and Eterna100 datasets, we obtained much tighter probability bounds than baselines. Our work also provides anatomical tools for analyzing RNA structures and pinpointing the sources of design difficulty at the motif level. Source code and data are available at https://github.com/shanry/RNA-Undesign.

RNA design aims to find a sequence that can fold into a target secondary structure. It can create artificial RNA molecules for specific functions, with wide applications in medicine. It is computationally challenging due to two levels of combinatorial explosion: the exponentially large design space and the exponentially many competing structures per design. Popular methods such as local search cannot keep up with these combinatorial explosions. We instead employ two techniques from machine learning, continuous optimization and Monte-Carlo sampling. We start from a distribution over all valid sequences, and use gradient descent to improve the expectation of an arbitrary objective function. We define novel coupled-variable distributions to model the correlation between nucleotides. We then use sampling to approximate the objective, estimate the gradient, and select the final candidate. Our work consistently outperforms state-of-the-art methods in key metrics including Boltzmann probability and ensemble defect, especially on long and hard-to-design structures.

Sign language translation from text to video plays a crucial role in enabling effective communication for Deaf and hard--of--hearing individuals. A major challenge lies in generating accurate and natural body poses and movements that faithfully convey intended meanings. Prior methods often neglect the anatomical constraints and coordination patterns of human skeletal motion, resulting in rigid or biomechanically implausible outputs. To address this, we propose a novel approach that explicitly models the relationships among skeletal joints--including shoulders, arms, and hands--by incorporating geometric constraints on joint positions, bone lengths, and movement dynamics. During training, we introduce a parent-relative reweighting mechanism to enhance finger flexibility and reduce motion stiffness. Additionally, bone-pose losses and bone-length constraints enforce anatomically consistent structures. Our method narrows the performance gap between the previous best and the ground-truth oracle by 56.51%, and further reduces discrepancies in bone length and movement variance by 18.76% and 5.48%, respectively, demonstrating significant gains in anatomical realism and motion naturalness.

Motivation: The task of designing optimized messenger RNA (mRNA) sequences has received much attention in recent years, thanks to breakthroughs in mRNA vaccines during the COVID-19 pandemic. Because most previous work aimed to minimize the minimum free energy (MFE) of the mRNA in order to improve stability and protein expression, which only considers one particular structure per mRNA sequence, millions of alternative conformations in equilibrium are neglected. More importantly, we prefer an mRNA to populate multiple stable structures and be flexible among them during translation when the ribosome unwinds it. Results: Therefore, we consider a new objective to minimize the ensemble free energy of an mRNA, which includes all possible structures in its Boltzmann ensemble. However, this new problem is much harder to solve than the original MFE optimization. To address the increased complexity of this problem, we introduce EnsembleDesign, a novel algorithm that employs continuous relaxation to optimize the expected ensemble free energy over a distribution of candidate sequences. EnsembleDesign extends both the lattice representation of the design space and the dynamic programming algorithm from LinearDesign to their probabilistic counterparts. Our algorithm consistently outperforms LinearDesign in terms of ensemble free energy, especially on long sequences. Interestingly, as byproducts, our designs also enjoy lower average unpaired probabilities (which correlates with degradation) and flatter Boltzmann ensembles (more flexibility between conformations). Availability and implementation: Our code is available on: https://github.com/LinearFold/EnsembleDesign.

RNA design aims to find an RNA sequence that can fold into a given target structure, which enables the creation of artificial RNA molecules with specific function, and has numerous applications in medicine. Computationally, it is particularly challenging due to two levels of combinatorial explosion: the exponentially large design space and the exponentially many competing structures for each design. As a result, heuristic methods such as local search have been popular for this task, but they cannot keep up with combinatorial explosion. We instead borrow two techniques from machine learning, continuous relaxation and Monte-Carlo sampling, to the RNA design problem. We formulate RNA design as continuous optimization, which starts with a distribution over all valid candidate sequences, and uses gradient descent to improve the expectation of an arbitrary objective function. We define novel sequence distributions using coupled variables to model the correlation between nucleotides. To make it universally applicable to any objective function, we use sampling to approximate the expected objective function, to estimate the gradient, and to select the final candidate. Compared to the state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is at http://github.com/weiyutang1010/ncrna_design.