Methods for the top-down design of nucleic acid nanostructures of arbitrary geometry based on target shape of spherical or non-spherical topology are described. The methods facilitate 3D molecular programming of lipids, proteins, sugars, and RNAs based on a DNA scaffold of arbitrary 2D or 3D shape. Geometric objects are rendered as node-edge networks of parallel nucleic acid duplexes, and a nucleic acid scaffold routed throughout the network using a spanning tree formula. Nucleic acid nanostructures produced according to top-down design methods are also described. In some embodiments, the nanostructures include single-stranded nucleic acid scaffold, DX crossovers, and staple strands. In other embodiments, the nanostructures include single-stranded nucleic acid scaffold, PX crossovers and no staples. Modified nanostructures include chemically modified nucleotides and conjugated to other molecules are described.

Nucleic acid molecule comprising a coding sequence and a region of increased folding energy upstream of a stop codon are provided. Expression vectors and cells comprising the nucleic acid molecule are also provided. Methods for optimizing a coding sequence comprising increasing folding energy in a region upstream of the stop codon are also provided.

Methods of identifying RNA-protein interaction sites are provided. Systems for identifying RNA-protein interaction sites are provided. Systems for identifying secondary structures are provided. Methods of identifying secondary structures are provided. Methods of identifying RNA-binding proteins are provided.

A method for creating a strategy, which is configured to determine a placement of nucleotides within a primary RNA structure as a function of a detail of a predefined secondary structure. The method includes the following steps: initializing the strategy; providing a task representation, the task representation including structural restrictions of the secondary RNA structure and sequential restrictions of the primary RNA structure; determining a primary candidate RNA sequence with the aid of the strategy as a function of the task representation; adapting the strategy with the aid of a reinforcement learning algorithm in such a way that a total loss is optimized.

A method for creating a strategy, which is configured to determine a placement of nucleotides within a primary RNA structure as a function of a detail of a predefined secondary structure. The method includes the following steps: initializing the strategy; providing a task representation, the task representation including structural restrictions of the secondary RNA structure and sequential restrictions of the primary RNA structure; determining a primary candidate RNA sequence with the aid of the strategy as a function of the task representation; adapting the strategy with the aid of a reinforcement learning algorithm in such a way that a total loss is optimized.

Disclosed herein are optimized guide RNAs (gRNAs) that have increased target binding specificity and reduced off-target binding. Further disclosed herein are methods of designing and using the optimized gRNAs.

Disclosed herein are optimized guide RNAs (gRNAs) that have increased target binding specificity and reduced off-target binding. Further disclosed herein are methods of designing and using the optimized gRNAs.

Co-infection of Pseudomonas aeruginosa and Staphylococcus aureus, exacerbates the virulence gene expression as well as shows higher antibacterial resistance than when they cause infections individually thereby making the infection extremely difficult to combat. A method and system for combating infections due to Pseudomonas aeruginosa and Staphylococcus aureus has been provided. The system provides strategies to combat pathogenic infections caused by multi-drug resistant (MDR) and extensively drug resistant (XDR) strains of Pseudomonas aeruginosa and Staphylococcus aureus. The strategy involves identifying potential target sites, which can be utilized to compromise its multiple virulence or essential functions at the same time. The idea utilizes the fact that a conserved stretch of nucleotide sequence occurring multiple times on a pathogen genome encoding virulence factors or in vicinity of genes essential for pathogen survival encoded within the genome of the candidate pathogen can be targeted to disrupt the overall genetic machinery of the pathogen.

A messenger RNA (mRNA) vaccine has emerged as a promising direction to combat the COVID-19 pandemic. This requires an mRNA sequence that is stable and highly productive in protein expression, features to benefit from greater mRNA secondary structure folding stability and optimal codon usage. Sequence design remains challenging due to the exponentially many synonymous mRNA sequences encoding the same protein. The present disclosure presents embodiments of a linear-time approximation (LinearDesign) reducing the design to an intersection between a Stochastic Context Free Grammar (SCFG) and a Deterministic Finite Automaton (DFA). Embodiments of the LinearDesign may implement an mRNA sequence design using much reduced time with very limited loss. Various methodologies, e.g., finding alternative sequences based on k-best parsing or directly incorporating codon optimality, are presented for incorporating the codon optimality into the design. Embodiments of the LinearDesign may provide efficient computational tools to speed up and improve mRNA vaccine development.

The present disclosure relates to methods of data encryption and data storage using molecular systems. The present disclosure also relates to molecular systems and methods for solving a polynomial time problem. Benefits of the methods and systems disclosed herein can include providing for the secure storage and retrieval of large amounts of encrypted data in a stable molecular system having random-access capability. Benefits of the methods and systems disclosed herein can include providing molecular computing systems that can solve complex polynomial time problems.