Catalytic Gold Nanoparticle Assembly Programmed by DNAzyme Circuits

Assembled gold nanoparticle (AuNP) superstructures can generate unique physicochemical characteristics and be used in various applications, thus becoming an attractive research field. Recently, several DNA-assisted gold nanoparticle assembly methods have been rigorously developed that typically require a non-catalytic equimolar molecular assembly to guarantee the designed assembly. Although efficient and accurate, exploring such non-catalytic nanoparticle assemblies in the complex cellular milieu under low trigger concentrations remains challenging. Therefore, developing a catalytic method that facilitates gold nanoparticle assemblies with relatively low DNA trigger concentrations is desirable. In this report, a catalytic method to program gold nanoparticle assemblies by DNAzyme circuits is presented, where only a small number of DNA triggers are able to induce the production of a large number of the desired nanoparticle assemblies. The feasibility of using logic DNAzyme circuits to control catalytic nanoparticle assemblies is experimentally verified. Additionally, catalytic AuNP assembly systems are established with cascading and feedback functions. The work provides an alternative research direction to enrich the tool library of nanoparticle assembly and their application in biosensing and nanomedicine.

Study on DNA Storage Encoding Based IAOA under Innovation Constraints

With the informationization of social processes, the amount of related data has greatly increased, making traditional storage media unable to meet the current requirements for data storage. Due to its advantages of a high storage capacity and persistence, deoxyribonucleic acid (DNA) has been considered the most prospective storage media to solve the data storage problem. Synthesis is an important process for DNA storage, and low-quality DNA coding can increase errors during sequencing, which can affect the storage efficiency. To reduce errors caused by the poor stability of DNA sequences during storage, this paper proposes a method that uses the double-matching and error-pairing constraints to improve the quality of the DNA coding set. First, the double-matching and error-pairing constraints are defined to solve problems of sequences with self-complementary reactions in the solution that are prone to mismatch at the 3' end. In addition, two strategies are introduced in the arithmetic optimization algorithm, including a random perturbation of the elementary function and a double adaptive weighting strategy. An improved arithmetic optimization algorithm (IAOA) is proposed to construct DNA coding sets. The experimental results of the IAOA on 13 benchmark functions show a significant improvement in its exploration and development capabilities over the existing algorithms. Moreover, the IAOA is used in the DNA encoding design under both traditional and new constraints. The DNA coding sets are tested to estimate their quality regarding the number of hairpins and melting temperature. The DNA storage coding sets constructed in this study are improved by 77.7% at the lower boundary compared to existing algorithms. The DNA sequences in the storage sets show a reduction of 9.7-84.1% in the melting temperature variance, and the hairpin structure ratio is reduced by 2.1-80%. The results indicate that the stability of the DNA coding sets is improved under the two proposed constraints compared to traditional constraints.

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.