Periodic Reporting for period 1 - PRONANO (Programmable Nanomatter)
Reporting period: 2023-01-01 to 2025-06-30
Despite spectacular progress in nanotechnology research in the past several decades, thus far most research in nanoscale machines and structures has resulted in single devices. Conspicuously missing are truly modular programmable molecular systems that are designed to take molecular inputs (DNA, RNA, protein or small molecule) and perform distinct tasks such as molecular manufacturing or therapeutic action as a function of these inputs. The goal of this project is to move the field of nanotechnology to realization of cooperating and computing nanostructures by developing the theoretical and experimental frameworks necessary to realize nanoscale autonomous units: a modular framework of self assembling nanoparticles that can perform logic operation as a function of binding to other particles. Our design takes inspiration from the biological signalling process of allostery: A binding of a trigger to one region determines the binding or catalytic action of a distant region within the molecule or complex. The nanoscale units will implement logic gates which cooperatively select the desired kinetic assembly pathway based on an input. We will first design the nanoparticles computationally using simplified kinetic models, followed by their experimental realization using DNA nanotechnology. We will design a swarm of nanoparticles that will be programmed to assemble into multiple distinct possible structures, where the choice of target structure will be induced by presenting an external signal (e.g. a protein or a DNA strand). As opposed to other proposed models of nanorobotics, this abstract model is sufficiently tractable to be realized with available experimental techniques in DNA nanotechnology, thus bridging theoretical modeling with experimental realization in the lab. The outcome will be a platform for realizing programmable nanomatter: a theoretical and experimental design framework that finds the optimal set of interactions and nanoparticle types that encode a set of possible nanostructures and trigger assembly through controlled kinetic pathways upon presentation of a molecular signal. The project will create a universal programmable sets of nanoparticles with encoded behavior that leads to self-assembly of distinct complex structures due to arbitrary molecular input. It will create a platform at the nanoscale level and open new venues in nanoconstruction such as templating 3D electronic circuits, molecular factories (artificial ribosome), and interfacing nanotechnology with living organisms (such as active nanomatter reaction to specific proteins on viral or bacterial surfaces).
The long-term goal of the project is to advance the field of bionanotechnology to achieve more complex actions at nanoscale, paving way towards nanorobotic devices that can interact with their environment to complete various tasks, such as nanoscale fabrication, diagnostics, or therapeutics. In the work carried out so far, we have focused on theoretical foundations of making such devices, and also carried out some experimental exploration of realization of such systems.
Our achievements so far include development of patchy particle representation to study self-assembly of complex nanostructures (we selected polyhedral capsids as a model system). We used multiscale modelling to design patchy particle potential that represents arbitrary self-assembling particle. In the context of our work, the particle is intended to be a DNA origami nanostructure. We used simulations to better understand the kinetic traps encountered during assembly and to do so, we extended our previously developed methodology, called SAT-assembly. In this framework, we specify the desired target shape that we wish to assemble in terms of which nanoparticle region interacts with what other particle region. We also specify a list of undesired states that we saw in the simulation and we prefer to avoid as they prevent the system from assembling into the desired shape. Our algorithm then designs interactions between particles in such a way they avoid these misassembled states. We have used this model to in-silico design and assemble polyhedral capsid shape, a highly coveted shape for nanoscale engineered assembly.
We have also introduced the concept of “signal-passing” nanoparticles, inspired by protein allostery in nature. In our model, the particles have interaction sites that can be either active or inactive. Their on/off state is controlled by the state of other patches in the system: if something is bound to a distant patch, it can activate or deactivate ability of some other patch to bind. We designed several examples to show what behaviour is achievable with these allostery-mimetic assemblies, namely: assembling complex shapes with fewer building blocks than what would otherwise be needed with static interaction sites. We also showed that allostery mimetic assembly can improve the yield of complex multicomponent self-assembled shapes that would otherwise were too difficult or impossible to assemble from the same components without the allosteric control. Finally, we showed how the allostery-mimetic building blocks can be used to design multifarious structures: a system of nanoparticles (nanomatter) that will receive an input signal and selectively fold into one possible target shape from multiple stored ones in the system. We developed simulation tools to help design them. We also developed a proposed implementation of this mechanism with DNA nanotechnology.
Our achievements so far include development of patchy particle representation to study self-assembly of complex nanostructures (we selected polyhedral capsids as a model system). We used multiscale modelling to design patchy particle potential that represents arbitrary self-assembling particle. In the context of our work, the particle is intended to be a DNA origami nanostructure. We used simulations to better understand the kinetic traps encountered during assembly and to do so, we extended our previously developed methodology, called SAT-assembly. In this framework, we specify the desired target shape that we wish to assemble in terms of which nanoparticle region interacts with what other particle region. We also specify a list of undesired states that we saw in the simulation and we prefer to avoid as they prevent the system from assembling into the desired shape. Our algorithm then designs interactions between particles in such a way they avoid these misassembled states. We have used this model to in-silico design and assemble polyhedral capsid shape, a highly coveted shape for nanoscale engineered assembly.
We have also introduced the concept of “signal-passing” nanoparticles, inspired by protein allostery in nature. In our model, the particles have interaction sites that can be either active or inactive. Their on/off state is controlled by the state of other patches in the system: if something is bound to a distant patch, it can activate or deactivate ability of some other patch to bind. We designed several examples to show what behaviour is achievable with these allostery-mimetic assemblies, namely: assembling complex shapes with fewer building blocks than what would otherwise be needed with static interaction sites. We also showed that allostery mimetic assembly can improve the yield of complex multicomponent self-assembled shapes that would otherwise were too difficult or impossible to assemble from the same components without the allosteric control. Finally, we showed how the allostery-mimetic building blocks can be used to design multifarious structures: a system of nanoparticles (nanomatter) that will receive an input signal and selectively fold into one possible target shape from multiple stored ones in the system. We developed simulation tools to help design them. We also developed a proposed implementation of this mechanism with DNA nanotechnology.
We introduced a new paradigm for nanoscale assembly, the use of “signal-passing” allostery-mimetic nanoparticles. This will inspire realization of more complex engineered systems at nanoscale.