Controlling uncertainty in aptamer selection

TL;DR

This paper introduces a stochastic hybrid model to understand and control the uncertainties in aptamer selection via SELEX, revealing how chance influences high-affinity ligand survival and proposing strategies to improve selection efficiency.

Contribution

The study develops a novel stochastic hybrid model for aptamer selection, providing insights into the role of randomness and environmental factors in optimizing SELEX outcomes.

Findings

Single high-affinity ligands can significantly influence population dynamics.

Survival of high-affinity ligands is highly dependent on chance.

Adjusting environmental parameters like target concentration can improve selection efficiency.

Abstract

The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a novel stochastic hybrid model that describes the evolutionary selection of aptamers in order to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify new strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.