Design Toolbox Research Articles

Reconfigurable DNA Nanocage for Protein Encapsulation and Regulation.

Creating nanomachines capable of precisely capturing, organizing, and regulating the activity of target biomolecules holds profound significance for advancing nanotechnology and therapeutics. Here, we develop a multistage reconfigurable DNA nanocage that can enclose and modulate proteins through multivalent interactions, activated by specific molecular signals. By strategically designing and manipulating the strut architecture of the DNA nanocages, we can achieve precise control over their reconfiguration among pyramid, square, and linear branch shapes. Additionally, we demonstrated its ability to capture thrombin and effectively inhibit its coagulation activity by incorporating two thrombin-targeting aptamers into the designed arms of the DNA nanocage. The activity of thrombin can be recovered by rearranging the conformation of the DNA nanocage and exposing the protein, thereby activating the coagulation process. This approach enriches the design toolbox for dynamic nanomachines and inspires a new strategy for protein encapsulation and regulation with potential future therapeutic applications.

P19-91 The Safe and Sustainable by Design toolbox: guiding the user across the innovation stages through the SSbD Wizard

P19-91 The Safe and Sustainable by Design toolbox: guiding the user across the innovation stages through the SSbD Wizard

Design of intrinsically disordered protein variants with diverse structural properties.

Intrinsically disordered proteins (IDPs) perform a broad range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Computational design of IDPs with specific conformational properties has, however, been difficult because of their substantial dynamics and structural complexity. We describe a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs that differ in compaction, long-range contacts, and propensity to phase separate. We experimentally tested and validated our designs and analyzed the sequence features that determine conformations. We show how our results are captured by a machine learning model, enabling us to speed up the algorithm. Our work expands the toolbox for computational protein design and will facilitate the design of proteins whose functions exploit the many properties afforded by protein disorder.

Enhancing structural control in covalent organic frameworks through steric interaction-driven linker design.

Covalent Organic Frameworks (COFs) exhibiting kagome (kgm) structures are promising crystalline porous materials with two distinct pores. However, there are no reliable synthetic methods to exclusively target the kgm over the polymorphic square-lattice (sql) structure. To address this, we introduce a linker design strategy featuring bulky functional groups, which through steric interactions can hinder the sql net formation, thereby leading to a kgm structure. By rigid attachment of the methyl benzoate groups to a tetradentate COF linker, steric interactions with neighbouring linkers depending on the pore size become possible. The steric interaction was tuned by varying the complementary bidentate linear linker lengths, where the shorter phenylenediamine linker leads to steric hindrance and the formation of the kgm lattice, while with the longer benzidine linker, steric interaction is reduced leading to the sql lattice. Thus, control over the net can be exerted through steric interaction strengths. Additionally, structural analysis revealed the formation of the kgm COF with an unusual ABC stacking, leading to pearl string type pores instead of two distinct pore sizes. This COF system shows that steric interaction-driven design enhances control over COF structures, expanding the design toolbox, but also provides valuable insights into network formation and polymorphism.

A Design Tool for Battery/Supercapacitor Hybrid Energy Storage Systems Based on the Physical–Electrochemical Degradation Battery Model BaSiS

A design toolbox has been developed for hybrid energy storage systems (HESSs) that employ both batteries and supercapacitors, primarily focusing on optimizing the system sizing/cost and mitigating battery aging. The toolbox incorporates the BaSiS model, a non-empirical physical–electrochemical degradation model for lithium-ion batteries that enables accurate simulations of battery performance and degradation under realistic operating conditions. The paper presents a detailed description of the parameterization, and validation process for the battery model, emphasizing the high accuracy and strong reliability of the battery aging prediction. The HESS design toolbox can be used to investigate the impact of various battery/supercapacitor configurations and energy management algorithms on the design, battery degradation, and system investment cost of the hybrid storage system. To illustrate the effectiveness of the design toolbox, a case study on Dynamic Moderation frequency support in the UK grid was conducted. For this use case, the application of hybrid storage energy systems is well suited due to the highly dynamic power regulation requirements in island grids with low inertia. By utilizing the fast response of supercapacitors, the stress on the battery caused by short-term high-power peaks can be significantly alleviated. In this way, the hybrid storage system effectively reduces either the battery size or the battery aging rate. In summary, this research highlights the crucial role of a comprehensive analysis in the design of hybrid energy storage systems, addressing both battery aging and overall system costs. The design toolbox can provide transparency regarding the design space and assist in determining the most suitable HESS configuration for a given application.

One‐Pot Catalytic Approaches: Building a New Toolbox for Macromolecular Design

AbstractOne‐pot processes have emerged as a powerful strategy in (macro)molecular synthesis: integrating a multicatalytic process maximizes efficiency, reduces waste, improves profitability, and provides versatile tools for designing of more sustainable processes without compromising selectivity and activity. This review article provides a critical overview of the application of one‐pot transformations in polymerization, with the goal of synthesizing polymers with tailored structures and functions for a wide range of applications. Recent advances in one‐pot polymerization techniques are highlighted, including examples from controlled polymerization methods such as atom transfer radical polymerization, reversible addition‐fragmentation chain transfer polymerization, and ring‐opening polymerization, among others. Special emphasis is placed on the design and optimization of reaction conditions, catalyst systems, and monomer combinations to achieve precise control over polymer structure and functionality.

Universal paramyxovirus vaccine design by stabilizing regions involved in structural transformation of the fusion protein

The Paramyxoviridae family encompasses medically significant RNA viruses, including human respiroviruses 1 and 3 (RV1, RV3), and zoonotic pathogens like Nipah virus (NiV). RV3, previously known as parainfluenza type 3, for which no vaccines or antivirals have been approved, causes respiratory tract infections in vulnerable populations. The RV3 fusion (F) protein is inherently metastable and will likely require prefusion (preF) stabilization for vaccine effectiveness. Here we used structure-based design to stabilize regions involved in structural transformation to generate a preF protein vaccine antigen with high expression and stability, and which, by stabilizing the coiled-coil stem region, does not require a heterologous trimerization domain. The preF candidate induces strong neutralizing antibody responses in both female naïve and pre-exposed mice and provides protection in a cotton rat challenge model (female). Despite the evolutionary distance of paramyxovirus F proteins, their structural transformation and local regions of instability are conserved, which allows successful transfer of stabilizing substitutions to the distant preF proteins of RV1 and NiV. This work presents a successful vaccine antigen design for RV3 and provides a toolbox for future paramyxovirus vaccine design and pandemic preparedness.

Hysteresis-free, fatigue-resistant and self-adhesive conductive hydrogel electronics towards multimodal wearable application

Conductive hydrogel-based electronic skins (e-skins) have attracted great attentions in health monitoring, human-machine interactions, and soft devices. Yet, broad challenges still remain in manufacturing of high-performance units, ingenious assembly of devices and developing of multimodal applications. Here, a typical strategy of combined covalent crosslinking and dynamic non-covalent interaction (multiple hydrogen bonds and electrostatic complexation) is used to prepare the ultra-durable polyacrylic acid (PAA)/ chitosan (CS)/ Ti3C2Tx (MXene) conductive hydrogel. It exhibits matched modulus for human skin, subtle mechanical hysteresis behavior (η=0.27% of 200% stretch, energy loss coefficient), great fatigue-resistant stability (>99%, 400% strain), robust interface adhesion (∼30 kPa) and essential biocompatibility. Benefited of such high performance, a series of hydrogel-based devices are further assembled to demonstrate the multi-mode and multi-scenario applications, including the strain and temperature sensors, triboelectric nanogenerator. Clearly, thereby this work provides a new toolbox for the rational design of high-performance hybrid hydrogel, and points out a general route for further hydrogel-based device assembly and multimodal applications.

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

KIAM Astrodynamics Toolbox for spacecraft orbital motion design

The KIAM Astrodynamics Toolbox, a new software library for designing spacecraft orbital motion, is introduced. The toolbox is developed at the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences using Fortran and Python languages, thus combining the computational speed and the flexibility of program design. The library can be useful for space flight mechanics specialists, as well as for students in relevant educational programs.

Passivating Blunt‐Ended Helices to Control Monodispersity and Multi‐Subunit Assembly of DNA Origami Structures

Deoxyribonucleic acid (DNA) origami enables the synthesis of bespoke nanoscale structures suitable for diverse applications. Effective design requires preventing uncontrolled aggregation, while still facilitating directed multi‐subunit assembly. Uncontrolled aggregation is often caused by base‐stacking interactions between arrays of blunt‐ended helices, a problem which is typically mitigated by incorporating disordered single‐stranded DNA (ssDNA) (like scaffold loops or poly‐thymine brushes) at the end of double‐stranded DNA (dsDNA) helices. Such disordered regions are ubiquitous in DNA origami structures yet their optimal design requirements in various chemical environments remains unclear. This study systematically investigates scaffold loops and poly‐nucleotide brushes for aggregation prevention and control of multi‐subunit assembly, examining length, sequence, and MgCl2 concentration dependencies. Additionally, a novel strategy is introduced using orthogonal double‐stranded DNA helices to create a steric shield against base stacking. The results reveal key considerations. Poly‐thymine brushes excel in achieving monodispersity across diverse conditions whereas scaffold loops aid directed multi‐subunit assembly. Orthogonal DNA helices remove the need for disordered regions altogether, preventing aggregation over a broad range of MgCl2 concentrations and facilitating controlled multi‐subunit assembly. This study expands the toolbox for DNA origami design and enables a more informed approach for achieving control of monodispersity and multi‐subunit assembly in DNA origami structures.

A Neural Implant Design Toolbox for Nonhuman Primates.

This paper describes an in-house method of 3D brain and skull modeling from magnetic resonance imaging (MRI) tailored for nonhuman primate (NHP) neurosurgical planning. This automated, computational software-based technique provides an efficient way of extracting brain and skull features from MRI files as opposed to traditional manual extraction techniques using imaging software. Furthermore, the procedure provides a method for visualizing the brain and craniotomized skull together for intuitive, virtual surgical planning. This generates a drastic reduction in time and resources from those required by past work, which relied on iterative 3D printing. The skull modeling process creates a footprint that is exported into modeling software to design custom-fit cranial chambers and headposts for surgical implantation. Custom-fit surgical implants minimize gaps between the implant and the skull that could introduce complications, including infection or decreased stability. By implementing these pre-surgical steps, surgical and experimental complications are reduced. These techniques can be adapted for other surgical processes, facilitating more efficient and effective experimental planning for researchers and, potentially, neurosurgeons.

Nanostructure design of 3D printed materials through macromolecular architecture.

Polymerization-induced microphase separation (PIMS) has been previously combined with 3D printing to develop customized nanostructured materials with a wide range of functional applications. In traditional PIMS, monofunctional, linear macromolecular chain transfer agents (macroCTAs) are used to develop macroCTA-b-P(monomer-stat-crosslinker) networks that self-assemble into unique disordered nanostructures. In this work, we designed a significantly altered network structure by utilizing linear macroCTAs with pendant CTA groups, which provides a novel network upon polymerization (i.e., branched copolymers, [macroCTA-graft-[P(monomer-stat-crosslinker)] n ]-b-P(monomer-stat-crosslinker)). Intriguingly, this method leads to the development of alternative disordered morphologies where the internal nanostructure can be precisely controlled. By systematically varying the number of pendant CTA groups, we demonstrate controlled transitions in macroCTA domain continuity, nanodomain size, and phase interface sharpness. These tunable properties translate to adjustable mechanical and swelling behaviors in the resulting 3D printed objects, ultimately enabling the fabrication of smart 4D materials (swelling-induced actuators and temperature-responsive shape-morphing objects). This research significantly expands the design toolbox for 3D printed PIMS materials, providing increased flexibility in the development of advanced materials with specific nanostructures and functionalities.

Tuning dipolar and multipolar resonances of chiral silicon nanostructures for control of near field superchirality.

Chiral materials display a property called optical activity, which is the capability to interact differentially with left and right circularly polarised light. This leads to the ability to manipulate the polarisation state of light, which has a broad range of applications spanning from energy efficient displays to quantum technologies. Both synthesised and engineered chiral nanomaterials are exploited in such devices. The design strategy for optimising the optical activity of a chiral material is typically based on maximising a single parameter, the electric dipole-magnetic dipole response. Here we demonstrate an alternative approach of controlling optical activity by manipulating both the dipole and multipolar response of a nanomaterial. This provides an additional parameter for material design, affording greater flexibility. The exemplar systems used to illustrate the strategy are nanofabricated chiral silicon structures. The multipolar response of the structures, and hence their optical activity, can be controlled simply by varying their height. This phenomenon allows optical activity and the creation of so called superchiral fields, with enhanced asymmetries, to be controlled over a broader wavelength range, than is achievable with just the electric dipole-magnetic dipole response. This work adds to the material design toolbox providing a route to novel nanomaterials for optoelectronics and sensing applications.

MARS Plus: An Improved Molecular Design Tool for Complex Compounds Involving Ionic, Stereo, and Cis-Trans Isomeric Structures.

MARS (Molecular Assembling and Representation Suite) (Hsu et al. J. Chem. Inf. Model. 2019, 59, 3703-3713) is a toolbox for the molecular design of organic molecules. MARS uses integer arrays to represent the elements and connectivity between elements of a molecule. It provides a collection of operations to manipulate the elemental composition and connectivity of a molecule (or a pair of molecules), enabling the creation of novel chemical compounds. In this work, the original MARS is extended to handle complex molecular structures, including geometric (cis-trans) isomers, stereo isomers, cyclic compounds, and ionic species. The extended version of MARS, referred to as MARS+, has a more comprehensive coverage of the chemical space and therefore can explore molecules with a greater chemical and physical diversity. Compared to other molecular design tools, MARS+ is designed to perform all possible manipulations on a given molecule or a pair of molecules. Molecular structure manipulation can be conducted in either a controlled or a random fashion. Furthermore, every structure manipulation has a counterpart so that the operation can be reversed. Nearly any possible chemical structure can be generated with MARS+ via a combination of molecular operations. The capabilities of MARS+ are examined by the design of new ionic liquids (ILs). The results show that MARS+ is a useful tool for computer-aided molecular design (CAMD) and molecular structure enumeration.

Schrödinger's Red Beyond 65,000Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer.

Freeform nanostructures have the potential to support complex resonances and their interactions, which are crucial for achieving desired spectral responses. However, the design optimization of such structures is nontrivial and computationally intensive. Furthermore, the current "black box" design approaches for freeform nanostructures often neglect the underlying physics. Here, a hybrid data-efficient neural optimizer for resonant nanostructures by combining a reinforcement learning algorithm and Powell's local optimization technique is presented. As a case study, silicon nanostructures with a highly-saturated red color are designed and experimentally demonstrated. Specifically, color coordinates of (0.677, 0.304) in the International Commission on Illumination (CIE) chromaticity diagram- close to the ideal Schrödinger's red, with polarization independence, high reflectance (>85%), and a large viewing angle (i.e., up to ± 25°) is achieved. The remarkable performance is attributed to underlying generalized multipolar interferences within each nanostructure rather than the collective array effects. Based on that, pixel size down to ≈400nm, corresponding to a printing resolution of 65000 pixels per inch is demonstrated. Moreover, the proposed design model requires only ≈300 iterations to effectively search a thirteen-dimensional (13D) design space - an order of magnitude more efficient than the previously reported approaches. The work significantly extends the free-form optical design toolbox for high-performance flat-optical components and metadevices.

Toolbox of Characterized Genetic Parts for Staphylococcus aureus.

Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.

FastPtx: a versatile toolbox for rapid, joint design of pTx RF and gradient pulses using Pytorch's autodifferentiation.

With modern optimization methods, free optimization of parallel transmit pulses together with their gradient waveforms can be performed on-line within a short time. A toolbox which uses PyTorch's autodifferentiation for simultaneous optimization of RF and gradient waveforms is presented and its performance is evaluated. MR measurements were performed on a 9.4T MRI scanner using a 3D saturated single-shot turboFlash sequence for [Formula: see text] mapping. RF pulse simulation and optimization were done using a Python toolbox and a dedicated server. An RF- and Gradient pulse design toolbox was developed, including a cost function to balance different metrics and respect hardware and regulatory limits. Pulse performance was evaluated in GRE and MPRAGE imaging. Pulses for non-selective and for slab-selective excitation were designed. Universal pulses for non-selective excitation reduced the flip angle error to an NRMSE of (12.3±1.7)% relative to the targeted flip angle in simulations, compared to (42.0±1.4)% in CP mode. The tailored pulses performed best, resulting in a narrow flip angle distribution with NRMSE of (8.2±1.0)%. The tailored pulses could be created in only 66s, making it feasible to design them during an experiment. A 90° pulse was designed as preparation pulse for a satTFL sequence and achieved a NRMSE of 7.1%. We showed that both MPRAGE and GRE imaging benefited from the pTx pulses created with our toolbox. The pTx pulse design toolbox can freely optimize gradient and pTx RF waveforms in a short time. This allows for tailoring high-quality pulses in just over a minute.

IDESS: a toolbox for identification and automated design of stochastic gene circuits.

One of the main causes hampering predictability during the model identification and automated design of gene circuits in synthetic biology is the effect of molecular noise. Stochasticity may significantly impact the dynamics and function of gene circuits, specially in bacteria and yeast due to low mRNA copy numbers. Standard stochastic simulation methods are too computationally costly in realistic scenarios to be applied to optimization-based design or parameter estimation. In this work, we present IDESS (Identification and automated Design of Stochastic gene circuitS), a software toolbox for optimization-based design and model identification of gene regulatory circuits in the stochastic regime. This software incorporates an efficient approximation of the Chemical Master Equation as well as a stochastic simulation algorithm-both with GPU and CPU implementations-combined with global optimization algorithms capable of solving Mixed Integer Nonlinear Programming problems. The toolbox efficiently addresses two types of problems relevant in systems and synthetic biology: the automated design of stochastic synthetic gene circuits, and the parameter estimation for model identification of stochastic gene regulatory networks. IDESS runs under the MATLAB environment and it is available under GPLv3 license at https://doi.org/10.5281/zenodo.7788692.

Acoustic Streaming-Induced Multimodal Locomotion of Bubble-Based Microrobots.

Acoustically-driven bubbles at the micron scale can generate strong microstreaming flows in its surrounding fluidic medium. The tunable acoustic streaming strength of oscillating microbubbles and the diversity of the generated flow patterns enable the design of fast-moving microrobots with multimodal locomotion suitable for biomedical applications. The acoustic microrobots holding two coupled microbubbles inside a rigid body are presented; trapped bubbles inside the L-shaped structure with different orifices generatevarious streaming flows, thus allowing multiple degrees of freedom in locomotion. The streaming pattern and mean streaming speed depend on the intensity and frequency of the acoustic wave, which can trigger four dominant locomotion modes in the microrobot, denoted as translational and rotational, spinning, rotational, and translational modes. Next, the effect of various geometrical and actuation parameters on the control and navigation of the microrobot is investigated. Furthermore, the surface-slipping multimodal locomotion, flow mixing, particle manipulation capabilities, the effective interaction of high flow rates with cells, and subsequent cancerous cell lysing abilities of the proposed microrobot are demonstrated. Overall, these results introduce a design toolbox for the next generation of acoustic microrobots with higher degrees of freedom with multimodal locomotion in biomedical applications.