Computational tools for aptamer identification and optimization
TrAC - Trends in Analytical Chemistry
; 157 (no pagination), 2022.
Article
in English
| EMBASE | ID: covidwho-2235992
ABSTRACT
Aptamers are single-stranded DNA or RNA oligonucleotides that can selectively bind to a specific target. They are generally obtained by SELEX, but the procedure is challenging and time-consuming. Moreover, the identified aptamers tend to be insufficient in stability, specificity, and affinity. Thus, only a handful of aptamers have entered the practical use stage. Recently, computational approaches have demonstrated a significant capacity to assist in the discovery of high-performance aptamers. This review discusses the advances achieved in several aspects of computational tools in this field, as well as the new progress in machine learning and deep learning, which are used in aptamer identification and optimization. To illustrate these computationally aided processes, aptamer selections against SARS-CoV-2 are discussed in detail as a case study. We hope that this review will aid and motivate researchers to develop and utilize more computational techniques to discover ideal aptamers effectively. Copyright © 2022 Elsevier B.V.
Aptamer identification; Aptamer optimization; Bioinformatics; covid-19; Deep learning; Machine learning; amino acid sequence; base pairing; binding affinity; bone turnover; computer aided design; computer model; crystal structure; drug design; glycosylation; hydrogen bond; medicinal chemistry; molecular docking; molecular dynamics; molecular model; nonhuman; nucleotide sequence; phylogenetic tree; protein binding; protein secondary structure; protein tertiary structure; quantitative structure activity relation; review; sequence alignment; sequence homology; Severe acute respiratory syndrome coronavirus 2; training; X ray crystallography; aptamer; gold nanoparticle; single stranded DNA/ec [Endogenous Compound]
Full text:
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Collection:
Databases of international organizations
Database:
EMBASE
Language:
English
Journal:
TrAC - Trends in Analytical Chemistry
Year:
2022
Document Type:
Article
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