scholarly journals Protocol to generate DNA aptamer coated particles and utilization for affinity-based screening with particle display

2021 ◽  
Author(s):  
Qin Yang ◽  
Ali Bashir ◽  
Jinpeng Wang ◽  
Stephan Hoyer ◽  
Wenchuan Chou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nucleic Acid ◽  
Dna Aptamer ◽  
Binding Properties ◽  
Coated Particles ◽  
High Performing ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
High Affinity ◽  
Target Molecules ◽  
Neutrophil Gelatinase

Abstract 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).

scholarly journals Machine learning guided aptamer refinement and discovery

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ali Bashir ◽  
Qin Yang ◽  
Jinpeng Wang ◽  
Stephan Hoyer ◽  
Wenchuan Chou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nucleic Acid ◽  
Random Perturbation ◽  
Therapeutic Agents ◽  
Learning Models ◽  
Binding Properties ◽  
High Performing ◽  
High Affinity ◽  
Target Molecules ◽  
Machine Learning Models

AbstractAptamers 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 propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.

In vitro selection of high-affinity nucleic acid ligands to parasite target molecules

2003 ◽  
Vol 33 (12) ◽  
pp. 1309-1317 ◽  
Author(s):  
H.Ulrich Göringer ◽  
Matthias Homann ◽  
Mihaela Lorger
Keyword(s):  
Nucleic Acid ◽  
In Vitro Selection ◽  
High Affinity ◽  
Target Molecules ◽  
Selection Of

scholarly journals The endocytic receptor megalin binds the iron transporting neutrophil-gelatinase-associated lipocalin with high affinity and mediates its cellular uptake

FEBS Letters ◽  
2004 ◽  
Vol 579 (3) ◽  
pp. 773-777 ◽  
Author(s):  
Vibeke Hvidberg ◽  
Christian Jacobsen ◽  
Roland K. Strong ◽  
Jack B. Cowland ◽  
Søren K. Moestrup ◽  
...  
Keyword(s):  
Cellular Uptake ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
High Affinity ◽  
Neutrophil Gelatinase
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