Machine learning guided aptamer refinement and discovery

Ali Bashir, Qin Yang, Jinpeng Wang, Stephan Hoyer, Wenchuan Chou, Cory McLean, Geoff Davis, Qiang Gong, Zan Armstrong, Junghoon Jang, Hui Kang, Annalisa Pawlosky, Alexander Scott, George E. Dahl, Marc Berndl, Michelle Dimon () and B. Scott Ferguson ()
Additional contact information
Ali Bashir: Google Research
Qin Yang: Aptitude Medical Systems Inc.
Jinpeng Wang: Aptitude Medical Systems Inc.
Stephan Hoyer: Google Research
Wenchuan Chou: Aptitude Medical Systems Inc.
Cory McLean: Google Research
Geoff Davis: Google Research
Qiang Gong: Aptitude Medical Systems Inc.
Zan Armstrong: Google Research
Junghoon Jang: Aptitude Medical Systems Inc.
Hui Kang: Aptitude Medical Systems Inc.
Annalisa Pawlosky: Google Research
Alexander Scott: Aptitude Medical Systems Inc.
George E. Dahl: Google Research
Marc Berndl: Google Research
Michelle Dimon: Google Research
B. Scott Ferguson: Aptitude Medical Systems Inc.

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-22555-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22555-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-22555-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().