Genetic biosensors for small-molecule products: Design and applications in high-throughput screening

Qingzhuo Wang, Shuang-Yan Tang, Sheng Yang

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PDF(285 KB)
Front. Chem. Sci. Eng. ›› 2017, Vol. 11 ›› Issue (1) : 15-26. DOI: 10.1007/s11705-017-1629-z
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REVIEW ARTICLE

Genetic biosensors for small-molecule products: Design and applications in high-throughput screening

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Abstract

Overproduction of small-molecule chemicals using engineered microbial cells has greatly reduced the production cost and promoted environmental protection. Notably, the rapid and sensitive evaluation of the in vivo concentrations of the desired products greatly facilitates the optimization process of cell factories. For this purpose, many genetic components have been adapted into in vivo biosensors of small molecules, which couple the intracellular concentrations of small molecules to easily detectable readouts such as fluorescence, absorbance, and cell growth. Such biosensors allow a high-throughput screening of the small-molecule products, and can be roughly classified as protein-based and RNA-based biosensors. This review summarizes the recent developments in the design and applications of biosensors for small-molecule products.

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Keywords

biosensor / small molecule product / transcription factor / riboswitch / high-throughput screening

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Qingzhuo Wang, Shuang-Yan Tang, Sheng Yang. Genetic biosensors for small-molecule products: Design and applications in high-throughput screening. Front. Chem. Sci. Eng., 2017, 11(1): 15‒26 https://doi.org/10.1007/s11705-017-1629-z

References

[1]
Schallmey M, Frunzke J, Eggeling L, Marienhagen J. Looking for the pick of the bunch: High-throughput screening of producing microorganisms with biosensors. Current Opinion in Biotechnology, 2014, 26: 148–154
CrossRef Google scholar
[2]
Ro D K, Paradise E M, Ouellet M, Fisher K J, Newman K L, Ndungu J M, Ho K A, Eachus R A, Ham T S, Kirby J, . Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 2006, 440(7086): 940–943
CrossRef Google scholar
[3]
Martin V J J, Pitera D J, Withers S T, Newman J D, Keasling J D. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nature Biotechnology, 2003, 21(7): 796–802
CrossRef Google scholar
[4]
Choi Y J, Lee S Y. Microbial production of short-chain alkanes. Nature, 2013, 502(7472): 571–574
CrossRef Google scholar
[5]
Dellomonaco C, Clomburg J M, Miller E N, Gonzalez R. Engineered reversal of the beta-oxidation cycle for the synthesis of fuels and chemicals. Nature, 2011, 476(7360): 355–359
CrossRef Google scholar
[6]
Enquist-Newman M, Faust A M E, Bravo D D, Santos C N S, Raisner R M, Hanel A, Sarvabhowman P, Le C, Regitsky D D, Cooper S R, . Efficient ethanol production from brown macroalgae sugars by a synthetic yeast platform. Nature, 2013, 505(7482): 239–243
CrossRef Google scholar
[7]
Becker J, Zelder O, Hafner S, Schroder H, Wittmann C. From zero to hero-design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production. Metabolic Engineering, 2011, 13(2): 159–168
CrossRef Google scholar
[8]
Lee K H, Park J H, Kim T Y, Kim H U, Lee S Y. Systems metabolic engineering of Escherichia coli for L-threonine production. Molecular Systems Biology, 2007, 3(1): 149
[9]
Kind S, Neubauer S, Becker J, Yamamoto M, Volkert M, von Abendroth G, Zelder O, Wittmann C. From zero to hero—production of bio-based nylon from renewable resources using engineered Corynebacterium glutamicum. Metabolic Engineering, 2014, 25: 113–123
CrossRef Google scholar
[10]
Zhang Y X, Perry K, Vinci V A, Powell K, Stemmer W P C, del Cardayre S B. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, 415(6872): 644–646
CrossRef Google scholar
[11]
Wang H H, Isaacs F J, Carr P A, Sun Z Z, Xu G, Forest C R, Church G M. Programming cells by multiplex genome engineering and accelerated evolution. Nature, 2009, 460(7257): 894–898
CrossRef Google scholar
[12]
Cobb R E, Chao R, Zhao H M. Directed evolution: Past, present, and future. AIChE Journal. American Institute of Chemical Engineers, 2013, 59(5): 1432–1440
CrossRef Google scholar
[13]
Alper H, Miyaoku K, Stephanopoulos G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nature Biotechnology, 2005, 23(5): 612–616
CrossRef Google scholar
[14]
Jantama K, Haupt M J, Svoronos S A, Zhang X L, Moore J C, Shanmugam K T, Ingram L O. Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnology and Bioengineering, 2008, 99(5): 1140–1153
CrossRef Google scholar
[15]
Dietrich J A, McKee A E, Keasling J D. High-throughput metabolic engineering: Advances in small-molecule screening and selection. Annual Review of Biochemistry, 2010, 79(1): 563–590
CrossRef Google scholar
[16]
Kim Y, Ingram L O, Shanmugam K T. Construction of an Escherichia coli K-12 mutant for homoethanologenic fermentation of glucose or xylose without foreign genes. Applied and Environmental Microbiology, 2007, 73(6): 1766–1771
CrossRef Google scholar
[17]
Zhou S, Iverson A G, Grayburn W S. Engineering a native homoethanol pathway in Escherichia coli B for ethanol production. Biotechnology Letters, 2008, 30(2): 335–342
CrossRef Google scholar
[18]
Solem C, Dehli T, Jensen P R. Rewiring Lactococcus lactis for ethanol production. Applied and Environmental Microbiology, 2013, 79(8): 2512–2518
CrossRef Google scholar
[19]
Shen C R, Lan E I, Dekishima Y, Baez A, Cho K M, Liao J C. Driving forces enable high-titer anaerobic L-butanol synthesis in Escherichia coli. Applied and Environmental Microbiology, 2011, 77(9): 2905–2915
CrossRef Google scholar
[20]
Lim J H, Seo S W, Kim S Y, Jung G Y. Model-driven rebalancing of the intracellular redox state for optimization of a heterologous n-butanol pathway in Escherichia coli. Metabolic Engineering, 2013, 20: 49–55
CrossRef Google scholar
[21]
Yim H, Haselbeck R, Niu W, Pujol-Baxley C, Burgard A, Boldt J, Khandurina J, Trawick J D, Osterhout R E, Stephen R, . Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nature Chemical Biology, 2011, 7(7): 445–452
CrossRef Google scholar
[22]
Ida Y, Hirasawa T, Furusawa C, Shimizu H. Utilization of saccharomyces cerevisiae recombinant strain incapable of both ethanol and glycerol biosynthesis for anaerobic bioproduction. Applied Microbiology and Biotechnology, 2013, 97(11): 4811–4819
CrossRef Google scholar
[23]
Zhang X, Jantama K, Moore J C, Shanmugam K T, Ingram L O. Production of L-alanine by metabolically engineered Escherichia coli. Applied Microbiology and Biotechnology, 2007, 77(2): 355–366
CrossRef Google scholar
[24]
Jantama K, Zhang X, Moore J C, Shanmugam K T, Svoronos S A, Ingram L O. Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C. Biotechnology and Bioengineering, 2008, 101(5): 881–893
CrossRef Google scholar
[25]
Klein-Marcuschamer D, Ajikumar P K, Stephanopoulos G. Engineering microbial cell factories for biosynthesis of isoprenoid molecules: Beyond lycopene. Trends in Biotechnology, 2007, 25(9): 417–424
CrossRef Google scholar
[26]
Santos C N S, Stephanopoulos G. Melanin-based high-throughput screen for L-tyrosine production in Escherichia coli. Applied and Environmental Microbiology, 2008, 74(4): 1190–1197
CrossRef Google scholar
[27]
DeLoache W C, Russ Z N, Narcross L, Gonzales A M, Martin V J, Dueber J E. An enzyme-coupled biosensor enables (S)-reticuline production in yeast from glucose. Nature Chemical Biology, 2015, 11(7): 465–471
CrossRef Google scholar
[28]
Binder S, Schendzielorz G, Stabler N, Krumbach K, Hoffmann K, Bott M, Eggeling L. A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level. Genome Biology, 2012, 13(5): 1
CrossRef Google scholar
[29]
Lin H, Tao H, Cornish V W. Directed evolution of a glycosynthase via chemical complementation. Journal of the American Chemical Society, 2004, 126(46): 15051–15059
CrossRef Google scholar
[30]
Baker K, Bleczinski C, Lin H, Salazar-Jimenez G, Sengupta D, Krane S, Cornish V W. Chemical complementation: A reaction-independent genetic assay for enzyme catalysis. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(26): 16537–16542
CrossRef Google scholar
[31]
Frommer W B, Davidson M W, Campbell R E. Genetically encoded biosensors based on engineered fluorescent proteins. Chemical Society Reviews, 2009, 38(10): 2833–2841
CrossRef Google scholar
[32]
Lalonde S, Ehrhardt D W, Frommer W B. Shining light on signaling and metabolic networks by genetically encoded biosensors. Current Opinion in Plant Biology, 2005, 8(6): 574–581
CrossRef Google scholar
[33]
Okumoto S, Looger L L, Micheva K D, Reimer R J, Smith S J, Frommer W B. Detection of glutamate release from neurons by genetically encoded surface-displayed FRET nanosensors. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(24): 8740–8745
CrossRef Google scholar
[34]
de Lorimier R M, Smith J J, Dwyer M A, Looger L L, Sali K M, Paavola C D, Rizk S S, Sadigov S, Conrad D W, Loew L, . Construction of a fluorescent biosensor family. Protein Science, 2002, 11(11): 2655–2675
CrossRef Google scholar
[35]
Fehr M, Lalonde S, Lager I, Wolff M W, Frommer W B. In vivo imaging of the dynamics of glucose uptake in the cytosol of COS-7 cells by fluorescent nanosensors. Journal of Biological Chemistry, 2003, 278(21): 19127–19133
CrossRef Google scholar
[36]
Fehr M, Takanaga H, Ehrhardt D W, Frommer W B. Evidence for high-capacity bidirectional glucose transport across the endoplasmic reticulum membrane by genetically encoded fluorescence resonance energy transfer nanosensors. Molecular and Cellular Biology, 2005, 25(24): 11102–11112
CrossRef Google scholar
[37]
Kaper T, Lager I, Looger L L, Chermak D, Frommer W B. Fluorescence resonance energy transfer sensors for quantitative monitoring of pentose and disaccharide accumulation in bacteria. Biotechnology for Biofuels, 2008, 1(1): 1
CrossRef Google scholar
[38]
Fehr M, Frommer W B, Lalonde S. Visualization of maltose uptake in living yeast cells by fluorescent nanosensors. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(15): 9846–9851
CrossRef Google scholar
[39]
Deuschle K, Okumoto S, Fehr M, Looger L L, Kozhukh L, Frommer W B. Construction and optimization of a family of genetically encoded metabolite sensors by semirational protein engineering. Protein Science, 2005, 14(9): 2304–2314
CrossRef Google scholar
[40]
Okada S, Ota K, Ito T. Circular permutation of ligand-binding module improves dynamic range of genetically encoded FRET-based nanosensor. Protein Science, 2009, 18(12): 2518–2527
CrossRef Google scholar
[41]
Serganov A, Nudler E. A decade of riboswitches. Cell, 2013, 152(1-2): 17–24
CrossRef Google scholar
[42]
Yang J, Seo S W, Jang S, Shin S I, Lim C H, Roh T Y, Jung G Y. Synthetic RNA devices to expedite the evolution of metabolite-producing microbes. Nature Communications, 2013, 4: 7
CrossRef Google scholar
[43]
Wachsmuth M, Findeiss S, Weissheimer N, Stadler P F, Morl M. De novo design of a synthetic riboswitch that regulates transcription termination. Nucleic Acids Research, 2013, 41(4): 2541–2551
CrossRef Google scholar
[44]
Trausch J J, Ceres P, Reyes F E, Batey R T. The structure of a tetrahydrofolate-sensing riboswitch reveals two ligand binding sites in a single aptamer. Structure (London, England), 2011, 19(10): 1413–1423
CrossRef Google scholar
[45]
Desai S K, Gallivan J P. Genetic screens and selections for small molecules based on a synthetic riboswitch that activates protein translation. Journal of the American Chemical Society, 2004, 126(41): 13247–13254
CrossRef Google scholar
[46]
Win M N, Smolke C D. Higher-order cellular information processing with synthetic RNA devices. Science, 2008, 322(5900): 456–460
CrossRef Google scholar
[47]
Michener J K, Smolke C D. High-throughput enzyme evolution in Saccharomyces cerevisiae using a synthetic RNA switch. Metabolic Engineering, 2012, 14(4): 306–316
CrossRef Google scholar
[48]
Eckdahl T T, Campbell A M, Heyer L J, Poet J L, Blauch D N, Snyder N L, Atchley D T, Baker E J, Brown M, Brunner E C, . Programmed evolution for optimization of orthogonal metabolic output in bacteria. PLoS One, 2015, 10(2): 0118322
CrossRef Google scholar
[49]
Ellington A D, Szostak J W. In vitro selection of RNA molecules that bind specific ligands. Nature, 1990, 346(6287): 818–822
CrossRef Google scholar
[50]
Win M N, Smolke C D. A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(36): 14283–14288
CrossRef Google scholar
[51]
Ouellet J. RNA Fluorescence with light-up aptamers. Frontiers in Chemistry, 2016, 4: 29
[52]
Nakayama S, Luo Y, Zhou J, Dayie T K, Sintim H O. Nanomolar fluorescent detection of c-di-GMP using a modular aptamer strategy. Chemical Communications, 2012, 48(72): 9059–9061
CrossRef Google scholar
[53]
Wang X C, Wilson S C, Hammond M C. Next-generation RNA-based fluorescent biosensors enable anaerobic detection of cyclic di-GMP. Nucleic Acids Research, 2016, 44(17): e139–e139
CrossRef Google scholar
[54]
Kellenberger C A, Hammond M C. In vitro analysis of riboswitch-Spinach aptamer fusions as metabolite-sensing fluorescent biosensors. Methods in Enzymology, 2015, 550: 147–172
CrossRef Google scholar
[55]
Paige J S, Nguyen-Duc T, Song W, Jaffrey S R. Fluorescence imaging of cellular metabolites with RNA. Science, 2012, 335(6073): 1194
CrossRef Google scholar
[56]
Su Y, Hickey S F, Keyser S G, Hammond M C. In vitro and in vivo enzyme activity screening via RNA-based fluorescent biosensors for S-adenosyl-L-homocysteine (SAH). Journal of the American Chemical Society, 2016, 138(22): 7040–7047
CrossRef Google scholar
[57]
Kellenberger C A, Chen C, Whiteley A T, Portnoy D A, Hammond M C. RNA-based fluorescent biosensors for live cell imaging of second messenger cyclic di-AMP. Journal of the American Chemical Society, 2015, 137(20): 6432–6435
CrossRef Google scholar
[58]
Binder S, Siedler S, Marienhagen J, Bott M, Eggeling L. Recombineering in corynebacterium glutamicum combined with optical nanosensors: A general strategy for fast producer strain generation. Nucleic Acids Research, 2013, 41(12): 6360–6369
CrossRef Google scholar
[59]
Schendzielorz G, Dippong M, Grunberger A, Kohlheyer D, Yoshida A, Binder S, Nishiyama C, Nishiyama M, Bott M, Eggeling L. Taking control over control: Use of product sensing in single cells to remove flux control at key enzymes in biosynthesis pathways. ACS Synthetic Biology, 2014, 3(1): 21–29
CrossRef Google scholar
[60]
Lange C, Mustafi N, Frunzke J, Kennerknecht N, Wessel M, Bott M, Wendisch V F. Lrp of Corynebacterium glutamicum controls expression of the brnFE operon encoding the export system for L-methionine and branched-chain amino acids. Journal of Biotechnology, 2012, 158(4): 231–241
CrossRef Google scholar
[61]
Mustafi N, Grunberger A, Kohlheyer D, Bott M, Frunzke J. The development and application of a single-cell biosensor for the detection of L-methionine and branched-chain amino acids. Metabolic Engineering, 2012, 14(4): 449–457
CrossRef Google scholar
[62]
Mahr R, Gatgens C, Gatgens J, Polen T, Kalinowski J, Frunzke J. Biosensor-driven adaptive laboratory evolution of L-valine production in Corynebacterium glutamicum. Metabolic Engineering, 2015, 32: 184–194
CrossRef Google scholar
[63]
Mustafi N, Grunberger A, Mahr R, Helfrich S, Noh K, Blombach B, Kohlheyer D, Frunzke J. Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains. PLoS One, 2014, 9(1): e85731
CrossRef Google scholar
[64]
Bogner M, Ludewig U. Visualization of arginine influx into plant cells using a specific FRET-sensor. Journal of Fluorescence, 2007, 17(4): 350–360
CrossRef Google scholar
[65]
Mohsin M, Ahmad A. Genetically-encoded nanosensor for quantitative monitoring of methionine in bacterial and yeast cells. Biosensors & Bioelectronics, 2014, 59: 358–364
CrossRef Google scholar
[66]
Mohsin M, Abdin M Z, Nischal L, Kardam H, Ahmad A. Genetically encoded FRET-based nanosensor for in vivo measurement of leucine. Biosensors & Bioelectronics, 2013, 50: 72–77
CrossRef Google scholar
[67]
Wang J M, Gao D F, Yu X L, Li W, Qi Q S. Evolution of a chimeric aspartate kinase for L-lysine production using a synthetic RNA device. Applied Microbiology and Biotechnology, 2015, 99(20): 8527–8536
CrossRef Google scholar
[68]
Liu Y N, Li Q G, Zheng P, Zhang Z D, Liu Y F, Sun C M, Cao G Q, Zhou W J, Wang X W, Zhang D W, . Developing a high-throughput screening method for threonine overproduction based on an artificial promoter. Microbial Cell Factories, 2015, 14(1): 1
CrossRef Google scholar
[69]
Zaslaver A, Bren A, Ronen M, Itzkovitz S, Kikoin I, Shavit S, Liebermeister W, Surette M G, Alon U. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nature Methods, 2006, 3(8): 623–628
CrossRef Google scholar
[70]
Mahr R, von Boeselager R F, Wiechert J, Frunzke J. Screening of an Escherichia coli promoter library for a phenylalanine biosensor. Applied Microbiology and Biotechnology, 2016, 100(15):6739–6753
[71]
Dietrich J A, Shis D L, Alikhani A, Keasling J D. Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis. ACS Synthetic Biology, 2013, 2(1): 47–58
CrossRef Google scholar
[72]
Szmidt-Middleton H L, Ouellet M, Adams P D, Keasling J D, Mukhopadhyay A. Utilizing a highly responsive gene, yhjX, in E. coli based production of 1,4-butanediol. Chemical Engineering Science, 2013, 103: 68–73
CrossRef Google scholar
[73]
Uchiyama T, Miyazaki K. Product-induced gene expression, a product-responsive reporter assay used to screen metagenomic libraries for enzyme-encoding genes. Applied and Environmental Microbiology, 2010, 76(21): 7029–7035
CrossRef Google scholar
[74]
van Sint Fiet S, van Beilen J B, Witholt B. Selection of biocatalysts for chemical synthesis. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(6): 1693–1698
CrossRef Google scholar
[75]
Raman S, Rogers J K, Taylor N D, Church G M. Evolution-guided optimization of biosynthetic pathways. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(50): 17803–17808
CrossRef Google scholar
[76]
Chen W, Zhang S, Jiang P X, Yao J, He Y Z, Chen L C, Gui X W, Dong Z Y, Tang S Y. Design of an ectoine-responsive AraC mutant and its application in metabolic engineering of ectoine biosynthesis. Metabolic Engineering, 2015, 30: 149–155
CrossRef Google scholar
[77]
Mukherjee K, Bhattacharyya S, Peralta-Yahya P. GPCR-based chemical biosensors for medium-chain fatty acids. ACS Synthetic Biology, 2015, 4(12): 1261–1269
CrossRef Google scholar
[78]
Tang S Y, Cirino P C. Design and application of a mevalonate-responsive regulatory protein. Angewandte Chemie International Edition, 2011, 50(5): 1084–1086
CrossRef Google scholar
[79]
Tang S Y, Qian S, Akinterinwa O, Frei C S, Gredell J A, Cirino P C. Screening for enhanced triacetic acid lactone production by recombinant Escherichia coli expressing a designed triacetic acid lactone reporter. Journal of the American Chemical Society, 2013, 135(27): 10099–10103
CrossRef Google scholar
[80]
Hichri I, Barrieu F, Bogs J, Kappel C, Delrot S, Lauvergeat V. Recent advances in the transcriptional regulation of the flavonoid biosynthetic pathway. Journal of Experimental Botany, 2011, 62(8): 2465–2483
CrossRef Google scholar
[81]
Siedler S, Stahlhut S G, Malla S, Maury J, Neves A R. Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli. Metabolic Engineering, 2014, 21: 2–8
CrossRef Google scholar
[82]
Marin A M, Souza E M, Pedrosa F O, Souza L M, Sassaki G L, Baura V A, Yates M G, Wassem R, Monteiro R A. Naringenin degradation by the endophytic diazotroph Herbaspirillum seropedicae SmR1. Microbiology, 2013, 159(1): 167–175
CrossRef Google scholar
[83]
Teran W, Felipe A, Segura A, Rojas A, Ramos J L, Gallegos M T. Antibiotic-dependent induction of Pseudomonas putida DOT-T1E TtgABC efflux pump is mediated by the drug binding repressor TtgR. Antimicrobial Agents and Chemotherapy, 2003, 47(10): 3067–3072
CrossRef Google scholar
[84]
Jenison R D, Gill S C, Pardi A, Polisky B. High-resolution molecular discrimination by RNA. Science, 1994, 263(5152): 1425–1429
CrossRef Google scholar
[85]
Thompson K M, Syrett H A, Knudsen S M, Ellington A D. Group I aptazymes as genetic regulatory switches. BMC Biotechnology, 2002, 2(1): 1
CrossRef Google scholar
[86]
Chou H H, Keasling J D. Programming adaptive control to evolve increased metabolite production. Nature Communications, 2013, 4: 8
CrossRef Google scholar
[87]
Park Y H, Koo H M, Moon J O, Kim S J, Kim H J, Lee J K. L-Lysine-inducible promoter. US 07851198, <Date>Dec 14 2010</Date>, 2010
[88]
Wang Y, Li Q, Zheng P, Guo Y, Wang L, Zhang T, Sun J, Ma Y. Evolving the L-lysine high-producing strain of Escherichia coli using a newly developed high-throughput screening method. Journal of Industrial Microbiology & Biotechnology, 2016, 43(9): 1227–1235
CrossRef Google scholar
[89]
Kim Y S, Gu M B. Advances in aptamer screening and small molecule aptasensors. Biosensors Based on Aptamers and Enzymes, 2014, 140: 29–67
CrossRef Google scholar
[90]
Ruscito A, DeRosa M C. Small-molecule binding aptamers: Selection strategies, characterization, and applications. Frontiers in Chemistry, 2016, 4: 14
[91]
McKeague M, Derosa M C. Challenges and opportunities for small molecule aptamer development. Journal of Nucleic Acids, 2012, 2012: 748913
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