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.
Highlights
Selective phenome growth can be seen as randomly selecting one sequence as the fittest member of a prospective landscape and evolving the interaction map and the fitness landscape around this fittest sequence. In this way phenotypic contributors are sequentially added in such a way that each increases the fitness of the selected fittest sequence
We have described a method for generating genotypephenotype interaction maps with lower aggregated pleiotropy vectors which yield smooth fitness landscapes
We have removed the one phenotype per gene paradigm that is the norm for NK model literature and used the varying number of phenotypic contributors to tune our new fitness landscapes
Summary
PCR bias may distort this copy number/binding correlation but, by using a motif based statistical framework such as MPBind [15], the binding potential of aptamers can be predicted, eliminating error from PCR bias Both DNA microarrays and HTS led to major breakthroughs in understanding library sequence space fitness and selection, these techniques are only capable of analysing a small fraction of a given library’s sequence space. This phenomenon of increasing returns of gene control is biologically appropriate and accurate for a system describing a group of genomes, but when describing the binding of an aptamer to an analyte this high aggregated pleiotropy is not biologically appropriate. Comparison is made between selective phenome growth landscapes and aptamer binding landscapes
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