DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.

PLoS computational biology
Authors
Abstract

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.

Year of Publication
2023
Journal
PLoS computational biology
Volume
19
Issue
7
Pages
e1010774
Date Published
07/2023
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1010774
PubMed ID
37406007
Links