Evolving methods for rational de novo design of functional RNA molecules

Abstract

Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.

Structure‐Switching RNAs: From Gene Expression Regulation to Small Molecule Detection

“…[ 78 ] Synthetic sequences exploiting natural concepts such as Rho‐independent termination can also be appended to synthetic aptamers in a computer‐assisted manner as recently done with different model molecules. [ 79 ] Finally, the expression platform can be developed using a selection strategy where, [ 80 ] for example, the region spacing the aptamer and the RBS (or the terminator stem‐loop) is randomized and subjected to a dual genetic selection, [73b] or to a fluorescence‐based screening [73a] . Once developed and implemented in living bacteria, such RNA devices can be used to reprogram cell behavior to make them able to sense and track small molecules, [ 81 ] change cell morphology, [66b] or even report on the presence of a target metabolite.…”

Partial RNA Design

“…RNA design describes the problem of generating RNA sequences with certain properties to fulfill desired functions [Hammer et al, 2019]. The design endeavors often start from carefully selected RNA fragments with known functions to combine these so-called motifs and achieve new functionality [Wachsmuth et al, 2012, Li et al, 2018, Nozawa et al, 2019].…”

“…The designed construct is then evaluated in a wet laboratory experiment proofing the desired functionality. To save costs, however, computational methods are employed to reduce the number of initial candidates for experimental validation [Hammer et al, 2019].…”

“…Thus, restricting this space to a reasonable subspace can drastically speed up the search for good candidates. It is common knowledge that primary- and secondary structure features are useful restrictions, while the design goals might vary depending on the particular application [Hammer et al, 2019]. To optimally support RNA practitioners, an algorithm should thus be able to process any primary- and secondary structure restrictions, efficiently explore large design spaces, consider different design goals, and provide as many candidates as needed, optimally with different lengths and compositions.…”

Exponentially few RNA structures are designable

“…). While the former methods are limited in their scope of applications by their extreme computational demands, methods of the latter category have encountered numerous applied successes [23], and enjoy a growing popularity. Historic objectives of design include the adoption, by the produced sequence, of a structure having energy as close as possible to the Minimal Free-Energy (MFE) achieved by the sequence.…”