Paradigms for computational nucleic acid design

被引:144
作者
Dirks, RM
Lin, M
Winfree, E
Pierce, NA [1 ]
机构
[1] CALTECH, Dept Bioengn, Pasadena, CA 91125 USA
[2] CALTECH, Dept Chem, Pasadena, CA 91125 USA
[3] CALTECH, Dept Phys, Pasadena, CA 91125 USA
[4] CALTECH, Dept Comp Sci, Pasadena, CA 91125 USA
[5] CALTECH, Dept Computat & Neural Syst, Pasadena, CA 91125 USA
[6] CALTECH, Dept Appl & Computat Math, Pasadena, CA 91125 USA
基金
美国国家科学基金会;
关键词
D O I
10.1093/nar/gkh291
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR-based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free-energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are refined further. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape.
引用
收藏
页码:1392 / 1403
页数:12
相关论文
共 54 条
[1]   Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots [J].
Akutsu, T .
DISCRETE APPLIED MATHEMATICS, 2000, 104 (1-3) :45-62
[2]   RNAsoft:: a suite of RNA secondary structure prediction and design software tools [J].
Andronescu, M ;
Aguirre-Hernández, R ;
Condon, A ;
Hoos, HH .
NUCLEIC ACIDS RESEARCH, 2003, 31 (13) :3416-3422
[3]   INVITRO RECOMBINATION AND TERMINAL ELONGATION OF RNA BY Q-BETA REPLICASE [J].
BIEBRICHER, CK ;
LUCE, R .
EMBO JOURNAL, 1992, 11 (13) :5129-5135
[4]   Solution of a 20-variable 3-SAT problem on a DNA computer [J].
Braich, RS ;
Chelyapov, N ;
Johnson, C ;
Rothemund, PWK ;
Adleman, L .
SCIENCE, 2002, 296 (5567) :499-502
[5]   Strand design for biomolecular computation [J].
Brenneman, A ;
Condon, A .
THEORETICAL COMPUTER SCIENCE, 2002, 287 (01) :39-58
[6]   Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays [J].
Brenner, S ;
Johnson, M ;
Bridgham, J ;
Golda, G ;
Lloyd, DH ;
Johnson, D ;
Luo, SJ ;
McCurdy, S ;
Foy, M ;
Ewan, M ;
Roth, R ;
George, D ;
Eletr, S ;
Albrecht, G ;
Vermaas, E ;
Williams, SR ;
Moon, K ;
Burcham, T ;
Pallas, M ;
DuBridge, RB ;
Kirchner, J ;
Fearon, K ;
Mao, J ;
Corcoran, K .
NATURE BIOTECHNOLOGY, 2000, 18 (06) :630-634
[7]   SYNTHESIS FROM DNA OF A MOLECULE WITH THE CONNECTIVITY OF A CUBE [J].
CHEN, JH ;
SEEMAN, NC .
NATURE, 1991, 350 (6319) :631-633
[8]   De novo protein design: Fully automated sequence selection [J].
Dahiyat, BI ;
Mayo, SL .
SCIENCE, 1997, 278 (5335) :82-87
[9]   Computer search algorithms in protein modification and design [J].
Desjarlais, JR ;
Clarke, ND .
CURRENT OPINION IN STRUCTURAL BIOLOGY, 1998, 8 (04) :471-475
[10]   DE-NOVO DESIGN OF THE HYDROPHOBIC CORES OF PROTEINS [J].
DESJARLAIS, JR ;
HANDEL, TM .
PROTEIN SCIENCE, 1995, 4 (10) :2006-2018