Aptamer Affinity Maturation by Resampling and Microarray Selection

Kinghorn, Andrew B.; Dirkzwager, Roderick M.; Liang, Shuailong; Cheung, Yiu-ming; Fraser, Lewis A.; Shiu, Simon Chi‐Chin; Tang, Marco S. L.; Tanner, Julian A.

doi:10.1021/acs.analchem.6b01635

Cited by 31 publications

(24 citation statements)

References 28 publications

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“…In 2016 Kinghorn et al reported a novel strategy of aptamer affinity maturation by library resampling from SELEX sequence data [ 118 ]. This approach relies on the principle that classical SELEX is unlikely to select the best possible aptamer, but is highly likely to select family members of the best possible aptamer.…”

Section: In Silico Aptamer Optimizationmentioning

confidence: 99%

“…While many microarray aptamer optimization processes use mutation and recombination, taking many small evolutionary steps to arrive at an optimum, Resample takes all possible selection sequence information and takes a single evolutionary leap to an optimum. In this way, a single Resample diversification round can be used to hone in on the sequence space containing the best possible aptamer sequence [ 118 ].…”

Section: In Silico Aptamer Optimizationmentioning

confidence: 99%

“…Nonaka et al achieved binding improvement of an order of magnitude by using a low-cost method, albeit labor-intensive in SPR measurements [ 117 ]. Kinghorn et al achieved binding improvement of an order of magnitude with a low-cost method that can be performed in two days, excluding microarray shipping time [ 118 ]. Many aptamer optimization studies are stand-alones without follow-up or verification by other research groups.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

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Aptamer Bioinformatics

Kinghorn

Fraser

Liang

et al. 2017

IJMS

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129

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Aptamers are short nucleic acid sequences capable of specific, high-affinity molecular binding. They are isolated via SELEX (Systematic Evolution of Ligands by Exponential Enrichment), an evolutionary process that involves iterative rounds of selection and amplification before sequencing and aptamer characterization. As aptamers are genetic in nature, bioinformatic approaches have been used to improve both aptamers and their selection. This review will discuss the advancements made in several enclaves of aptamer bioinformatics, including simulation of aptamer selection, fragment-based aptamer design, patterning of libraries, identification of lead aptamers from high-throughput sequencing (HTS) data and in silico aptamer optimization.

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Section: In Silico Aptamer Optimizationmentioning

confidence: 99%

Section: In Silico Aptamer Optimizationmentioning

confidence: 99%

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

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Aptamer Bioinformatics

Kinghorn

Fraser

Liang

et al. 2017

IJMS

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129

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“…We have previously developed a DNA aptamer against the malaria diagnostic biomarker Plasmodium falciparum lactate dehydrogenase (PfLDH) . We have shown how the aptamer can be used in complex samples, integrated within a 3D printed module to make a portable point‐of‐care device .…”

Section: Introductionmentioning

confidence: 99%

Aptamer‐Mediated Protein Molecular Recognition Driving a DNA Tweezer Nanomachine

et al. 2016

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Nucleic acid-mediated nanomachines have significant potential in biomedical applications but new approaches that link molecular recognition of proteins to change in nucleic acid structure and function are required. Here, a split DNA aptamer is integrated into G-quadruplex tweezers, which close in the presence of the malaria biomarker protein Plasmodium falciparum lactate dehydrogenase (PfLDH). Closing of the tweezers enables G-quadruplex hemin mediated peroxidase activity, which can be observed colorimetrically. The PfLDH aptamer is split within an asymmetric internal loop and incorporated into the tweezers maintaining aptamer binding capability. Spacing between the G-quadruplex structure and split aptamer, together with extent of complementarity, is found to be critical for optimization to enhance catalytic performance. The integrated split aptamer is observed to maintain the high specificity to Plasmodium falciparum lactate dehydrogenase of the parent aptamer. Split aptamer approaches have significant potential to functionalize nucleic acid nanostructures for protein molecular recognition. www.adv-biosys.com

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“…Platt et al [7], Knight et al [8], and Rowe et al [9] used microarrays to both evolve aptamers and gain insight into an aptamer binding landscape. Additionally, DNA microarray data has been applied to aptamer specificity landscapes [10], fitness landscape morphology [11], and aptamer affinity maturation [12]. In comparison, the possible sequence space coverage using HTS is much greater, with Illumina's HiSeq HTS capable of yielding sequence data for more than 2 Complexity 70 million sequences from a single lane [13].…”

mentioning

confidence: 99%

Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding

Kinghorn

Tanner

2017

Complexity

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Aptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the vast complexity of aptamer selection. Simulation of aptamer selection has been used to characterise and optimise the selection process; however, the absence of a good model for aptamer-target binding limits this field of study. Here, we generate theoretical fitness landscapes which appear to more accurately represent aptamer-target binding. The method used to generate these landscapes, selective phenome growth, is a new approach in which phenotypic contributors are added to a genotype/phenotype interaction map sequentially in such a way so as to increase the fitness of a selected fit sequence. In this way, a landscape is built around the selected fittest sequences. Comparison to empirical aptamer microarray data shows that our theoretical fitness landscapes more accurately represent aptamer ligand binding than other theoretical models. These improved fitness landscapes have potential for the computational analysis and optimisation of other complex systems.

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Aptamer Affinity Maturation by Resampling and Microarray Selection

Cited by 31 publications

References 28 publications

Aptamer Bioinformatics

Aptamer Bioinformatics

Aptamer‐Mediated Protein Molecular Recognition Driving a DNA Tweezer Nanomachine

Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding

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