Exploring the Mutational Robustness of Nucleic Acids by Searching Genotype Neighborhoods in Sequence Space

Mechanisms of ssDNA aptamer binding to Cd2+ in aqueous solution: A molecular dynamics study

ssDNA aptamers have been increasingly used to detect heavy metal ions as recognition elements due to their high affinity and specificity. However, the specific recognition and binding mechanisms between aptamers and most heavy metals were still unclear, which limits the development of aptamer-based detection methods. In this work, the interaction mechanisms of CD-2-1 aptamers with Cd2+ in aqueous solutions were investigated using molecular dynamic simulations. The most stable complex was found where Cd2+ binding at aptamer's stem-loop junction and preferred at the phosphate backbone or bases. Noteworthily, two binding modes of Cd2+ combining aptamer in aqueous solution were discovered: direct and indirect. In the former mode, Cd2+ directly coordinated O atoms of bases. For the latter, Cd2+ connected to bases with coordinated water molecules as bridges. Electrostatic interaction was found to be the main driving force, and differences of residues role between two binding modes were elucidated. Buffer molecules in aqueous solutions can stabilize aptamer-Cd2+ complex by hydrogen bonds. This study revealed the specific interaction mechanisms of aptamer with Cd2+ at an atomic level, which provided theoretical references for aptamer-based Cd2+ detection methods establishment as well as an efficient technical route of screening potential aptamers for heavy metal ions.

In Silico discovery of aptamers with an enhanced library design strategy

With advances in force fields and algorithms, robust tools have been developed for molecular simulation of three-dimensional structures of nucleic acids and investigation of aptamer-target interactions. The traditional aptamer discovery technique, Systematic Evolution of Ligands by EXponential enrichment (SELEX), continues to suffer from high investment and low return, while in vitro screening by simulated SELEX remains a challenging task, where more accurate structural modeling and enhanced sampling limit the large-scale application of the method. Here, we proposed a modified aptamer enhanced library design strategy to facilitate the screening of target-binding aptamers. In this strategy, a comprehensive analysis of the original complexes and the target secondary structure were used to construct an enhanced initial library for screening. Our enhanced sequence library design strategy based on the target secondary structure explored a certain sequence space while ensuring the accuracy of the structural conformation and the calculation method. In an enhanced library of only a few dozen sequences, four sequences showed a similar or better binding free energy than the original aptamer, with consistently high binding stability over three rounds of multi-timescale simulations, ranging from - 30.27 to - 32.25 kcal/mol. Consequently, the enhanced library strategy based on the target secondary structure is shown to have very significant potential as a new aptamer design and optimization strategy.

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.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information

Aptamers are short single-stranded RNA/DNA molecules that bind to specific target molecules. Aptamers with high binding-affinity and target specificity are identified using an in vitro procedure called high throughput systematic evolution of ligands by exponential enrichment (HT-SELEX). However, the development of aptamer affinity reagents takes a considerable amount of time and is costly because HT-SELEX produces a large dataset of candidate sequences, some of which have insufficient binding-affinity. Here, we present RNA aptamer Ranker (RaptRanker), a novel in silico method for identifying high binding-affinity aptamers from HT-SELEX data by scoring and ranking. RaptRanker analyzes HT-SELEX data by evaluating the nucleotide sequence and secondary structure simultaneously, and by ranking according to scores reflecting local structure and sequence frequencies. To evaluate the performance of RaptRanker, we performed two new HT-SELEX experiments, and evaluated binding affinities of a part of sequences that include aptamers with low binding-affinity. In both datasets, the performance of RaptRanker was superior to Frequency, Enrichment and MPBind. We also confirmed that the consideration of secondary structures is effective in HT-SELEX data analysis, and that RaptRanker successfully predicted the essential subsequence motifs in each identified sequence.

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

Molecular evolution can be conceptualized as a walk over a “fitness landscape”, or the function of fitness (e.g., catalytic activity) over the space of all possible sequences. Understanding evolution requires knowing the structure of the fitness landscape and identifying the viable evolutionary pathways through the landscape. However, the fitness landscape for any catalytic biomolecule is largely unknown. The evolution of catalytic RNA is of special interest because RNA is believed to have been foundational to early life. In particular, an essential activity leading to the genetic code would be the reaction of ribozymes with activated amino acids, such as 5(4H)-oxazolones, to form aminoacyl-RNA. Here we combine in vitro selection with a massively parallel kinetic assay to map a fitness landscape for self-aminoacylating RNA, with nearly complete coverage of sequence space in a central 21-nucleotide region. The method (SCAPE: sequencing to measure catalytic activity paired with in vitro evolution) shows that the landscape contains three major ribozyme families (landscape peaks). An analysis of evolutionary pathways shows that, while local optimization within a ribozyme family would be possible, optimization of activity over the entire landscape would be frustrated by large valleys of low activity. The sequence motifs associated with each peak represent different solutions to the problem of catalysis, so the inability to traverse the landscape globally corresponds to an inability to restructure the ribozyme without losing activity. The frustrated nature of the evolutionary network suggests that chance emergence of a ribozyme motif would be more important than optimization by natural selection.

In silicopost-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen

High affinity DNA aptamers against carcinoembryonic antigen were selected and verified by using anin silicoapproach and experimental characterizations.