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Constraining proximity-based drugs, such as proteolysis targeting chimeras (PROTACs), into their bioactive conformation can significantly impact their selectivity and potency. However, traditional methods for achieving this often involve complex and time-consuming synthetic procedures. Here, we introduced an alternative approach by demonstrating DNA-templated spatially controlled PROTACs (DTACs), which leverage the programmability of nucleic acid-based self-assembly for efficient synthesis and offer precise control over inhibitors’ spacing and orientation. The resulting constructs revealed distance- and orientation-dependent selectivity and degradation potency for the Cyclin D1–CDK4/6 protein complex in cancer cells. Notably, the optimal construct DTAC-V1 demonstrated unprecedented synchronous degradation of the entire Cyclin D1–CDK4/6 complex, leading to robust G1-phase cell cycle arrest and effective inhibition of cancer cell proliferation. Furthermore, in a xenograft mouse model, DTAC-V1 exhibited potent therapeutic efficacy by effectively degrading Cyclin D1–CDK4/6 and suppressing tumor growth, underscoring its potential as an anticancer agent. Overall, our findings demonstrate the feasibility of DTAC as a rapid, scalable, and modular platform for the spatial control of functional inhibitors for optimal effectiveness, making it a promising method for proximity-based therapeutics. 

Abstract DNA origami information storage is a promising alternative to silicon-based data storage, offering a molecular cryptography technique concealing information within DNA origami. Routing, sliding, and interlacing staple strands lead to a large 700-bit key size. Practical DNA data storage requires high information density, robust security, and accurate and rapid information retrieval. Consequently, advanced readout techniques and large encryption key sizes are essential. Here, we report an enhanced DNA origami cryptography protocol in 2D and 3D DNA origami, increasing the encryption key size. We employ all-DNA-based steganography with fast readout through high-speed DNA-PAINT super-resolution imaging. By combining DNA-PAINT data with unsupervised clustering, we achieve an accuracy of up to 89%, despite the flexibility in the 3D DNA origami shown by oxDNA simulation. Furthermore, we propose criteria that ensure complete information retrieval for the DNA origami cryptography. Our findings show that DNA-based cryptography is a secure and versatile solution for storing information. 

GPUs are used in many settings to accelerate large-scale scientific computation, including simulation, computational biology, and molecular dynamics. However, optimizing codes to run efficiently on GPUs requires developers to have both detailed understanding of the application logic and significant knowledge of parallel programming and GPU architectures. This paper shows that an automated GPU program optimization tool, GEVO, can leverage evolutionary computation to find code edits that reduce the runtime of three important applications, multiple sequence alignment, agent-based simulation and molecular dynamics codes, by 28.9%, 29%, and 17.8% respectively. The paper presents an in-depth analysis of the discovered optimizations, revealing that (1) several of the most important optimizations involve significant epistasis, (2) the primary sources of improvement are application-specific, and (3) many of the optimizations generalize across GPU architectures. In general, the discovered optimizations are not straightforward even for a GPU human expert, showcasing the potential of automated program optimization tools to both reduce the optimization burden for human domain experts and provide new insights for GPU experts. 

Understanding the mechanisms by which single-stranded RNA viruses regulate capsid assembly around their RNA genomes has become increasingly important for the development of both antiviral treatments and drug delivery systems. In this study, we investigate the effects of RNA-induced allostery in a single-stranded RNA virus—Levivirus bacteriophage MS2 assembly—using the computational methods of the Dynamic Flexibility Index and the Dynamic Coupling Index. We demonstrate that not only does asymmetric binding of RNA to a symmetric MS2 coat protein dimer increase the flexibility of the distant FG-loop, inducing a conformational change to an asymmetric dimer, but also RNA binding reorganizes long-distance communications, making all the other positions extremely sensitive to the fluctuation of the ordered FG-loop. Additionally, we find that a point mutation in the FG-loop, W82R, leads to the loss of this asymmetry in communications, likely being a leading cause for assembly-deficient dimers. Lastly, this dominant communication that enhances its dynamic coupling with all the distal positions is not only a property of the dimer but is also exhibited by all the observed capsid intermediates. This strong dynamic coupling allows for unidirectional signal transduction that drives the formation of the experimentally observed capsid intermediates and fully assembled capsid. 

We introduce oxNA, a new model for the simulation of DNA–RNA hybrids that is based on two previously developed coarse-grained models—oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes, including their structure, persistence length, and force-extension characteristics. By parameterizing the DNA–RNA hydrogen bonding interaction, we fit the model’s thermodynamic properties to experimental data using both average-sequence and sequence-dependent parameters. To demonstrate the model’s applicability, we provide three examples of its use—calculating the free energy profiles of hybrid strand displacement reactions, studying the resolution of a short R-loop, and simulating RNA-scaffolded wireframe origami. 

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. We use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization. 

The self-assembly of colloidal diamond (CD) crystals is considered as one of the most coveted goals of nanotechnology, both from the technological and fundamental points of view. 

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”—given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms largely rely on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs are also investigated and recently been shown to outperform free energy–based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework that can easily incorporate an arbitrary number of rewards (free energy or nonparametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions of nonpseudoknotted secondary structures than the structure prediction algorithm used, thus validating the promise of the approach. Summary of Contribution: ExpertRNA is a new algorithm inspired by a biological problem. It is applied to solve the problem of secondary structure prediction for RNA molecules given an input sequence. The computational contribution is given by the design of a multibranch, multiexpert rollout algorithm that enables the use of several state-of-the-art approaches as base heuristics and allowing several experts to evaluate partial candidate solutions generated, thus avoiding assuming the reward being optimized by an RNA molecule when folding. Our implementation allows for the effective use of parallel computational resources as well as to control the size of the rollout tree as the algorithm progresses. The problem of RNA secondary structure prediction is of primary importance within the biology field because the molecule structure is strongly related to its functionality. Whereas the contribution of the paper is in the algorithm, the importance of the application makes ExpertRNA a showcase of the relevance of computationally efficient algorithms in supporting scientific discovery.