Abstract
Aptamers are short nucleic acid sequences capable of specific, high-affinity molecular binding. They are isolated via SELEX (Systematic Evolution of Ligands by Exponential Enrichment), an evolutionary process that involves iterative rounds of selection and amplification before sequencing and aptamer characterization. As aptamers are genetic in nature, bioinformatic approaches have been used to improve both aptamers and their selection. This review will discuss the advancements made in several enclaves of aptamer bioinformatics, including simulation of aptamer selection, fragment-based aptamer design, patterning of libraries, identification of lead aptamers from high-throughput sequencing (HTS) data and in silico aptamer optimization.
Highlights
Aptamers are short nucleic acid sequences capable of specific, high-affinity molecular binding [1,2]
This review encompasses a broad range of aptamer bioinformatics approaches including simulation of aptamer selection, aptamer selection by molecular dynamics, patterning of libraries, identification of lead aptamers from high-throughput sequencing (HTS) data, and in silico aptamer optimization
One particular application is to improve the efficacy of aptamer selections by computationally solving the three-dimensional structures of nucleic acids (NAs) and their targets, and simulating the physical forces involved in NA docking to a target
Summary
Aptamers are short nucleic acid sequences capable of specific, high-affinity molecular binding [1,2]. Aptamers are among the simplest of genetic entities, having both genotypic and phenotypic properties and being capable of heredity in an in vitro selection experiment. Their combinatorial complexity poses many questions and problems that are well suited to computational analysis. This review encompasses a broad range of aptamer bioinformatics approaches including simulation of aptamer selection, aptamer selection by molecular dynamics, patterning of libraries, identification of lead aptamers from high-throughput sequencing (HTS) data, and in silico aptamer optimization. SpEeLrfEoXrmceydclbeesfoarree speeqrufeonrmcinedg abnedfoarpetasmeqeurecnhcainragctaenridzaatipotnam(leefrt cahrraorwacteexriitzinatgiocnyc(llee)f.t arrow exiting cycle)
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