Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we present a step-by-step protocol for utilizing particle display to select DNA aptamers for a 25 kDa protein biomarker neutrophil gelatinase-associated lipocalin (NGAL).
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Posted 30 Apr, 2021
Posted 30 Apr, 2021
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we present a step-by-step protocol for utilizing particle display to select DNA aptamers for a 25 kDa protein biomarker neutrophil gelatinase-associated lipocalin (NGAL).
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