A computational study of alternate SELEX

Discrete stochastic models of SELEX: Aptamer capture probabilities and protocol optimization

Antibodies are important biomolecules that are often designed to recognize target antigens. However, they are expensive to produce and their relatively large size prevents their transport across lipid membranes. An alternative to antibodies is aptamers, short ([Formula: see text] bp) oligonucleotides (and amino acid sequences) with specific secondary and tertiary structures that govern their affinity to specific target molecules. Aptamers are typically generated via solid phase oligonucleotide synthesis before selection and amplification through Systematic Evolution of Ligands by EXponential enrichment (SELEX), a process based on competitive binding that enriches the population of certain strands while removing unwanted sequences, yielding aptamers with high specificity and affinity to a target molecule. Mathematical analyses of SELEX have been formulated in the mass action limit, which assumes large system sizes and/or high aptamer and target molecule concentrations. In this paper, we develop a fully discrete stochastic model of SELEX. While converging to a mass-action model in the large system-size limit, our stochastic model allows us to study statistical quantities when the system size is small, such as the probability of losing the best-binding aptamer during each round of selection. Specifically, we find that optimal SELEX protocols in the stochastic model differ from those predicted by a deterministic model.

A system for multiplexed selection of aptamers with exquisite specificity without counterselection

Aptamers have the capacity to discriminate between structurally similar small molecules. However, generating such highly specific aptamers has proven challenging using conventional processes based on counterselection against nontarget molecules. In this work, we describe a high-throughput screening platform that can characterize the specificity of millions of aptamers toward a group of structurally related molecules in a single experiment and generate exquisitely specific aptamers without any counterselection. As exemplars, we generated aptamers with high affinity and specificity toward three structurally related kynurenine metabolites using our platform. Our platform can be readily adapted to other small-molecule targets and should therefore accelerate the development of aptamer reagents with exquisite specificity.

Aptamers have proven to be valuable tools for the detection of small molecules due to their remarkable ability to specifically discriminate between structurally similar molecules. Most aptamer selection efforts have relied on counterselection to eliminate aptamers that exhibit unwanted cross-reactivity to interferents or structurally similar relatives to the target of interest. However, because the affinity and specificity characteristics of an aptamer library are fundamentally unknowable a priori, it is not possible to determine the optimal counterselection parameters. As a result, counterselection experiments require trial-and-error approaches that are inherently inefficient and may not result in aptamers with the best combination of affinity and specificity. In this work, we describe a high-throughput screening process for generating high-specificity aptamers to multiple targets in parallel while also eliminating the need for counterselection. We employ a platform based on a modified benchtop sequencer to conduct a massively parallel aptamer screening process that enables the selection of highly specific aptamers against multiple structurally similar molecules in a single experiment, without any counterselection. As a demonstration, we have selected aptamers with high affinity and exquisite specificity for three structurally similar kynurenine metabolites that differ by a single hydroxyl group in a single selection experiment. This process can easily be adapted to other small-molecule analytes and should greatly accelerate the development of aptamer reagents that achieve exquisite specificity for their target analytes.

Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution

Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland's Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland's theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.

Controlling uncertainty in aptamer selection

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 stochastic hybrid model that describes the evolutionary selection of aptamers 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 strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.

Aptamers in analytics

Nucleic acid aptamers are promising alternatives to antibodies in analytics. They are generally obtained through an iterative SELEX protocol that enriches a population of synthetic oligonucleotides to a subset that can recognize the chosen target molecule specifically and avidly. A wide range of targets is recognized by aptamers. Once identified and optimized for performance, aptamers can be reproducibly synthesized and offer other key features, like small size, low cost, sensitivity, specificity, rapid response, stability, and reusability. This makes them excellent options for sensory units in a variety of analytical platforms including those with electrochemical, optical, and mass sensitive transduction detection. Many novel sensing strategies have been developed by rational design to take advantage of the tendency of aptamers to undergo conformational changes upon target/analyte binding and employing the principles of base complementarity that can drive the nucleic acid structure. Despite their many advantages over antibodies, surprisingly few aptamers have yet been integrated into commercially available analytical devices. In this review, we discuss how to select and engineer aptamers for their identified application(s), some of the challenges faced in developing aptamers for analytics and many examples of their reported successful performance as sensors in a variety of analytical platforms.