Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding

Abstract

Aptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the vast complexity of aptamer selection. Simulation of aptamer selection has been used to characterise and optimise the selection process; however, the absence of a good model for aptamer-target binding limits this field of study. Here, we generate theoretical fitness landscapes which appear to more accurately represent aptamer-target binding. The method used to generate these landscapes, selective phenome growth, is a new approach in which phenotypic contributors are added to a genotype/phenotype interaction map sequentially in such a way so as to increase the fitness of a selected fit sequence. In this way, a landscape is built around the selected fittest sequences. Comparison to empirical aptamer microarray data shows that our theoretical fitness landscapes more accurately represent aptamer ligand binding than other theoretical models. These improved fitness landscapes have potential for the computational analysis and optimisation of other complex systems.

Aptamer Bioinformatics

“…Altenberg’s selective genome growth NK landscape represents gene regulation very well. However, due to the increasing returns of the selection system, an extremely high pleiotropy is attributed to a handful of digits [ 26 ]. This highly aggregated pleiotropy is biologically appropriate and accurate for describing gene regulation.…”

“…To overcome this problem Kinghorn and Tanner recently devised the method “selective phenome growth”, which generates fitness landscapes with low aggregated pleiotropy that more appropriately represent aptamer binding [ 26 ]. The selective phenome growth process involves phenotypic contributors being added to a genotype/phenotype interaction map sequentially in such a way as to increase the fitness of a selected “fit sequence”.…”

“…In this way, a fitness landscape is built around the selected “fittest sequence”. The fitness landscapes obtained were compared to empirical aptamer microarray data and were shown to more accurately represent aptamer ligand binding than other theoretical models ( Figure 2 ) [ 26 ]. The selective phenome growth model has not yet been utilized in the simulation of SELEX, only described and validated as a model that more accurately represents aptamer binding.…”

“…The mean path divergence analysis, a measure of landscape smoothness, shows that the novel phenome selected landscape is more similar to empirical microarray binding data than the previous genome selected landscape model. Figure adapted from Kinghorn et al [ 26 ].…”

An aptasensor using ceria electrodeposited-screen-printed carbon electrode for detection of epithelial sodium channel protein as a hypertension biomarker

“…An aptamer is a specific RNA or ssDNA oligonucleotide that can interact with a protein. Generally, aptamer base sequences can be obtained through a selection process using an in vitro method known as the systematic evolution of ligands by enrichment exponential (SELEX) [ 10 , 11 ]. Aptamers have relatively small sizes compared to antibodies, which are relatively large [ 12 ].…”