Aptamer‐Mediated Reversible Transactivation of Gene Expression by Light

Allosteric Regulation of Aptamer Affinity through Mechano-Chemical Coupling

The capacity to precisely modulate aptamer affinity is important for a wide variety of applications. However, most such engineering strategies entail laborious trial-and-error testing or require prior knowledge of an aptamer's structure and ligand-binding domain. We describe here a simple and generalizable strategy for allosteric modulation of aptamer affinity by employing a double-stranded molecular clamp that destabilizes aptamer secondary structure through mechanical tension. We demonstrate the effectiveness of the approach with a thrombin-binding aptamer and show that we can alter its affinity by as much as 65-fold. We also show that this modulation can be rendered reversible by introducing a restriction enzyme cleavage site into the molecular clamp domain and describe a design strategy for achieving even more finely-tuned affinity modulation. This strategy requires no prior knowledge of the aptamer's structure and binding mechanism and should thus be generalizable across aptamers.

A Conformational Restriction Strategy for the Control of CRISPR/Cas Gene Editing with Photoactivatable Guide RNAs

The CRISPR/Cas system is one of the most powerful tools for gene editing. However, approaches for precise control of genome editing and regulatory events are still desirable. Here, we report the spatiotemporal and efficient control of CRISPR/Cas9- and Cas12a-mediated editing with conformationally restricted guide RNAs (gRNAs). This approach relied on only two or three pre-installed photo-labile substituents followed by an intramolecular cyclization, representing a robust synthetic method in comparison to the heavily modified linear gRNAs that often require extensive screening and time-consuming optimization. This tactic could direct the precise cleavage of the genes encoding green fluorescent protein (GFP) and the vascular endothelial growth factor A (VEGFA) protein within a predefined cutting region without notable editing leakage in live cells. We also achieved light-mediated myostatin (MSTN) gene editing in embryos, wherein a new bow-knot-type gRNA was constructed with excellent OFF/ON switch efficiency. Overall, our work provides a significant new strategy in CRISPR/Cas editing with modified circular gRNAs to precisely manipulate where and when genes are edited.

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures.

We show that two-layer neural networks, Restricted Boltzmann Machines (RBM), can be successfully trained on sequence ensemble datasets from selection-amplification experiments. We train the RBM using datasets from aptamer selection experiments on thrombin protein, and show that the model can successfully generalize to the test set to predict binders and non-binders. The log-likelihood assigned to a sequence by the RBM is correlated with the sequence fitness as quantified by the amplification between different rounds of selection. We further show that that the model is interpretable and by inspecting the weights of the model, we can identify structural motifs that are characteristic of the good binders. We explore the usage of the RBMs to identify which of the possible protein exosites the aptamers bind to. We show that the RBM can also be used for unsupervised clustering. Finally, we use RBMs to generate novel aptamers, and we experimentally verify predicted binding and non-binding sequences generated from the RBM.