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Nucleic acid design is the process of generating a set ofnucleic acid base sequences that will associate into a desired conformation. Nucleic acid design is central to the fields ofDNA nanotechnology andDNA computing.[2] It is necessary because there are many possiblesequences of nucleic acid strands that will fold into a givensecondary structure, but many of these sequences will have undesired additional interactions which must be avoided. In addition, there are manytertiary structure considerations which affect the choice of a secondary structure for a given design.[3][4]
Nucleic acid design has similar goals toprotein design: in both, the sequence of monomers isrationally designed to favor the desired folded or associated structure and to disfavor alternate structures. However, nucleic acid design has the advantage of being a much computationally simpler problem, since the simplicity of Watson-Crickbase pairing rules leads to simpleheuristic methods which yield experimentally robust designs. Computational models forprotein folding requiretertiary structure information whereas nucleic acid design can operate largely on the level ofsecondary structure. However, nucleic acid structures are less versatile than proteins in their functionality.[2][5]
Nucleic acid design can be considered the inverse ofnucleic acid structure prediction. In structure prediction, the structure is determined from a known sequence, while in nucleic acid design, a sequence is generated which will form a desired structure.[2]
Thestructure of nucleic acids consists of a sequence ofnucleotides. There are four types of nucleotides distinguished by which of the fournucleobases they contain: in DNA these areadenine (A),cytosine (C),guanine (G), andthymine (T). Nucleic acids have the property that two molecules will bind to each other to form adouble helix only if the two sequences arecomplementary, that is, they can form matching sequences ofbase pairs. Thus, in nucleic acids the sequence determines the pattern of binding and thus the overall structure.[5]
Nucleic acid design is the process by which, given a desired target structure or functionality, sequences are generated for nucleic acid strands which will self-assemble into that target structure. Nucleic acid design encompasses all levels ofnucleic acid structure:
One of the greatest concerns in nucleic acid design is ensuring that the target structure has the lowest free energy (i.e. is the mostthermodynamically favorable) whereas misformed structures have higher values of free energy and are thus unfavored.[2]These goals can be achieved through the use of a number of approaches, includingheuristic, thermodynamic, and geometrical ones. Almost all nucleic acid design tasks are aided by computers, and a number of software packages are available for many of these tasks.
Two considerations in nucleic acid design are that desired hybridizations should have melting temperatures in a narrow range, and any spurious interactions should have very low melting temperatures (i.e. they should be very weak).[5] There is also a contrast between affinity-optimizing "positive design", seeks to minimize the energy of the desired structure in an absolute sense, and specificity-optimizing "negative design", which considers the energy of the target structure relative to those of undesired structures. Algorithms which implement both kinds of design tend to perform better than those that consider only one type.[2]
Heuristic methods use simple criteria which can be quickly evaluated to judge the suitability of different sequences for a given secondary structure. They have the advantage of being much less computationally expensive than theenergy minimization algorithms needed for thermodynamic or geometrical modeling, and being easier to implement, but at the cost of being less rigorous than these models.
Sequence symmetry minimization is the oldest approach to nucleic acid design and was first used to design immobile versions of branched DNA structures. Sequence symmetry minimization divides the nucleic acid sequence into overlapping subsequences of a fixed length, called the criterion length. Each of the 4N possible subsequences of length N is allowed to appear only once in the sequence. This ensures that no undesired hybridizations can occur which have a length greater than or equal to the criterion length.[2][3]
A related heuristic approach is to consider the "mismatch distance", meaning the number of positions in a certain frame where the bases are notcomplementary. A greater mismatch distance lessens the chance that a strong spurious interaction can happen.[5] This is related to the concept ofHamming distance ininformation theory. Another related but more involved approach is to use methods fromcoding theory toconstruct nucleic acid sequences with desired properties.
Information about thesecondary structure of a nucleic acid complex along with its sequence can be used to predict thethermodynamic properties of the complex.
When thermodynamic models are used in nucleic acid design, there are usually two considerations: desired hybridizations should have melting temperatures in a narrow range, and any spurious interactions should have very low melting temperatures (i.e. they should be very weak). TheGibbs free energy of a perfectly matched nucleic acid duplex can be predicted using anearest neighbor model. This model considers only the interactions between a nucleotide and its nearest neighbors on the nucleic acid strand, by summing the free energy of each of the overlapping two-nucleotide subwords of the duplex. This is then corrected for self-complementary monomers and forGC-content. Once the free energy is known, themelting temperature of the duplex can be determined. GC-content alone can also be used to estimate the free energy and melting temperature of a nucleic acid duplex. This is less accurate but also much less computationally costly.[5]
Software for thermodynamic modeling of nucleic acids includesNupack,[6][7]mfold/UNAFold,[8] andVienna.[9]
A related approach, inverse secondary structure prediction, usesstochastic local search which improves a nucleic acid sequence by running astructure prediction algorithm and the modifying the sequence to eliminate unwanted features.[5]
Geometrical models of nucleic acids are used to predicttertiary structure. This is important because designed nucleic acid complexes usually contain multiple junction points, which introduces geometric constraints to the system. These constraints stem from the basicstructure of nucleic acids, mainly that thedouble helix formed by nucleic acid duplexes has a fixed helicity of about 10.4base pairs per turn, and isrelatively stiff. Because of these constraints, the nucleic acid complexes are sensitive to the relative orientation of themajor and minor grooves at junction points. Geometrical modeling can detectstrain stemming from misalignments in the structure, which can then be corrected by the designer.[4][11]
Geometric models of nucleic acids forDNA nanotechnology generally use reduced representations of the nucleic acid, because simulating every atom would be very computationally expensive for such large systems. Models with three pseudo-atoms per base pair, representing the two backbone sugars and the helix axis, have been reported to have a sufficient level of detail to predict experimental results.[11] However, models with five pseudo-atoms per base pair, explicitly including the backbone phosphates, are also used.[12]
Software for geometrical modeling of nucleic acids includesGIDEON,[11]Tiamat,[13]Nanoengineer-1,andUNIQUIMER 3D.[14]Geometrical concerns are especially of interest in the design ofDNA origami, because the sequence is predetermined by the choice of scaffold strand. Software specifically for DNA origami design has been made, includingcaDNAno[15]andSARSE.[16]
Nucleic acid design is used inDNA nanotechnology to design strands which will self-assemble into a desired target structure. These include examples such asDNA machines, periodic two- and three-dimensional lattices, polyhedra, andDNA origami.[2] It can also be used to create sets of nucleic acid strands which are "orthogonal", or non-interacting with each other, so as to minimize or eliminate spurious interactions. This is useful inDNA computing, as well as for molecular barcoding applications inchemical biology andbiotechnology.[5]
