Constrained Multistate Sequence Design for Nucleic Acid Reaction Pathway Engineering.

We describe a framework for designing the sequences of multiple nucleic acid strands intended to hybridize in solution via a prescribed reaction pathway. Sequence design is formulated as a multistate optimization problem using a set of target test tubes to represent reactant, intermediate, and product states of the system, as well as to model crosstalk between components. Each target test tube contains a set of desired "on-target" complexes, each with a target secondary structure and target concentration, and a set of undesired "off-target" complexes, each with vanishing target concentration. Optimization of the equilibrium ensemble properties of the target test tubes implements both a positive design paradigm, explicitly designing for on-pathway elementary steps, and a negative design paradigm, explicitly designing against off-pathway crosstalk. Sequence design is performed subject to diverse user-specified sequence constraints including composition constraints, complementarity constraints, pattern prevention constraints, and biological constraints. Constrained multistate sequence design facilitates nucleic acid reaction pathway engineering for diverse applications in molecular programming and synthetic biology. Design jobs can be run online via the NUPACK web application.

Sequence Design for a Test Tube of Interacting Nucleic Acid Strands.

We describe an algorithm for designing the equilibrium base-pairing properties of a test tube of interacting nucleic acid strands. A target test tube is specified as a set of desired "on-target" complexes, each with a target secondary structure and target concentration, and a set of undesired "off-target" complexes, each with vanishing target concentration. Sequence design is performed by optimizing the test tube ensemble defect, corresponding to the concentration of incorrectly paired nucleotides at equilibrium evaluated over the ensemble of the test tube. To reduce the computational cost of accepting or rejecting mutations to a random initial sequence, the structural ensemble of each on-target complex is hierarchically decomposed into a tree of conditional subensembles, yielding a forest of decomposition trees. Candidate sequences are evaluated efficiently at the leaf level of the decomposition forest by estimating the test tube ensemble defect from conditional physical properties calculated over the leaf subensembles. As optimized subsequences are merged toward the root level of the forest, any emergent defects are eliminated via ensemble redecomposition and sequence reoptimization. After successfully merging subsequences to the root level, the exact test tube ensemble defect is calculated for the first time, explicitly checking for the effect of the previously neglected off-target complexes. Any off-target complexes that form at appreciable concentration are hierarchically decomposed, added to the decomposition forest, and actively destabilized during subsequent forest reoptimization. For target test tubes representative of design challenges in the molecular programming and synthetic biology communities, our test tube design algorithm typically succeeds in achieving a normalized test tube ensemble defect ≤1% at a design cost within an order of magnitude of the cost of test tube analysis.

Nucleic acid sequence design via efficient ensemble defect optimization.

We describe an algorithm for designing the sequence of one or more interacting nucleic acid strands intended to adopt a target secondary structure at equilibrium. Sequence design is formulated as an optimization problem with the goal of reducing the ensemble defect below a user-specified stop condition. For a candidate sequence and a given target secondary structure, the ensemble defect is the average number of incorrectly paired nucleotides at equilibrium evaluated over the ensemble of unpseudoknotted secondary structures. To reduce the computational cost of accepting or rejecting mutations to a random initial sequence, candidate mutations are evaluated on the leaf nodes of a tree-decomposition of the target structure. During leaf optimization, defect-weighted mutation sampling is used to select each candidate mutation position with probability proportional to its contribution to the ensemble defect of the leaf. As subsequences are merged moving up the tree, emergent structural defects resulting from crosstalk between sibling sequences are eliminated via reoptimization within the defective subtree starting from new random subsequences. Using a Θ(N(3) ) dynamic program to evaluate the ensemble defect of a target structure with N nucleotides, this hierarchical approach implies an asymptotic optimality bound on design time: for sufficiently large N, the cost of sequence design is bounded below by 4/3 the cost of a single evaluation of the ensemble defect for the full sequence. Hence, the design algorithm has time complexity Ω(N(3) ). For target structures containing N ∈{100,200,400,800,1600,3200} nucleotides and duplex stems ranging from 1 to 30 base pairs, RNA sequence designs at 37°C typically succeed in satisfying a stop condition with ensemble defect less than N/100. Empirically, the sequence design algorithm exhibits asymptotic optimality and the exponent in the time complexity bound is sharp.

NUPACK: Analysis and design of nucleic acid systems.

UNLABELLED: The Nucleic Acid Package (NUPACK) is a growing software suite for the analysis and design of nucleic acid systems. The NUPACK web server (http://www.nupack.org) currently enables: ANALYSIS: thermodynamic analysis of dilute solutions of interacting nucleic acid strands. DESIGN: sequence design for complexes of nucleic acid strands intended to adopt a target secondary structure at equilibrium.Utilities: evaluation, display, and annotation of equilibrium properties of a complex of nucleic acid strands. NUPACK algorithms are formulated in terms of nucleic acid secondary structure. In most cases, pseudoknots are excluded from the structural ensemble.