Fitness Landscapes of Functional RNAs

Exploring the Landscape of Aptamers: From Cross-Reactive to Selective to Specific, High-Affinity Receptors for Cocaine

We reported over 20 years ago MNS-4.1, the first DNA aptamer with a micromolar affinity for cocaine. MNS-4.1 is based on a structural motif that is very common in any random pool of oligonucleotides, and it is actually a nonspecific hydrophobic receptor with wide cross-reactivity with alkaloids and steroids. Despite such weaknesses preventing broad applications, this aptamer became widely used in proof-of-concept demonstrations of new formats of biosensors. We now report a series of progressively improved DNA aptamers recognizing cocaine, with the final optimized receptors having low nanomolar affinity and over a thousand-fold selectivity over the initial cross-reactants. In the process of optimization, we tested different methods to eliminate cross-reactivities and improve affinity, eventually achieving properties that are comparable to those of the reported monoclonal antibody candidates for the therapy of overdose. Multiple aptamers that we now report share structural motifs with the previously reported receptor for serotonin. Further mutagenesis studies revealed a palindromic, highly adaptable, broadly cross-reactive hydrophobic motif that could be rebuilt through mutagenesis, expansion of linker regions, and selections into receptors with exceptional affinities and varying specificities.

Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts

An important question in evolutionary biology is whether (and in what ways) genotype–phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because the RNA secondary structure (SS) can be analyzed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data are available, it offers a good model system for studying the role of bias. For quite short RNA (length L≤126), it has recently been shown that natural and random RNA types are structurally very similar, suggesting that bias strongly constrains evolutionary dynamics. Here, we extend these results with emphasis on much larger RNA with lengths up to 3000 nucleotides. By examining both abstract shapes and structural motif frequencies (i.e., the number of helices, bonds, bulges, junctions, and loops), we find that large natural and random structures are also very similar, especially when contrasted to typical structures sampled from the spaces of all possible RNA structures. Our motif frequency study yields another result, where the frequencies of different motifs can be used in machine learning algorithms to classify random and natural RNA with high accuracy, especially for longer RNA (e.g., ROC AUC 0.86 for L = 1000). The most important motifs for classification are the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within ‘junk’ DNA regions.

Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning

A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral networks has been lacking in recent experimental studies of fitness landscapes. This suggests that evolution could be constrained globally. Here, we demonstrate that a deep learning-guided evolutionary algorithm can efficiently identify neutral genotypes within the sequence space of an RNA ligase ribozyme. Furthermore, we measure the activities of all 2¹⁶ variants connecting two active ribozymes that differ by 16 mutations and analyze mutational interactions (epistasis) up to the 16th order. We discover an extensive network of neutral paths linking the two genotypes and reveal that these paths might be predicted using only information from lower-order interactions. Our experimental evaluation of over 120,000 ribozyme sequences provides important empirical evidence that neutral networks can increase the accessibility and predictability of the fitness landscape.

Neutral networks, which are sets of genotypes connected via single mutations that share the same phenotype, are important for evolvability. Here, the authors provide experimental evidence of a neutral network in an RNA enzyme using a high-throughput assay and deep learning.

The major evolutionary transitions and codes of life

Major evolutionary transitions as well as the evolution of codes of life are key elements in macroevolution which are characterized by increase in complexity Major evolutionary transitions ensues by a transition in individuality and by the evolution of a novel mode of using, transmitting or storing information. Here is where codes of life enter the picture: they are arbitrary mappings between different (mostly) molecular species. This flexibility allows information to be employed in a variety of ways, which can fuel evolutionary innovation. The collation of the list of major evolutionary transitions and the list of codes of life show a clear pattern: codes evolved prior to a major evolutionary transition and then played roles in the transition and/or in the transformation of the new individual. The evolution of a new code of life is in itself not a major evolutionary transition but allow major evolutionary transitions to happen. This could help us to identify new organic codes.

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.

Molecular Fitness Landscapes from High-Coverage Sequence Profiling

The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes.

Aminoacyl-tRNA synthetase evolution and sectoring of the genetic code

The genetic code sectored via tRNA charging errors, and the code progressed toward closure and universality because of evolution of aminoacyl-tRNA synthetase (aaRS) fidelity and translational fidelity mechanisms. Class I and class II aaRS folds are identified as homologs. From sequence alignments, a structurally conserved Zn-binding domain common to class I and class II aaRS was identified. A model for the class I and class II aaRS alternate folding pathways is posited. Five mechanisms toward code closure are highlighted: 1) aaRS proofreading to remove mischarged amino acids from tRNA; 2) accurate aaRS active site specification of amino acid substrates; 3) aaRS-tRNA anticodon recognition; 4) conformational coupling proofreading of the anticodon-codon interaction; and 5) deamination of tRNA wobble adenine to inosine. In tRNA anticodons there is strong wobble sequence preference that results in a broader spectrum of contacts to synonymous mRNA codon wobble bases. Adenine is excluded from the anticodon wobble position of tRNA unless it is modified to inosine. Uracil is generally preferred to cytosine in the tRNA anticodon wobble position. Because of wobble ambiguity when tRNA reads mRNA, the maximal coding capacity of the three nucleotide code read by tRNA is 31 amino acids + stops.

Constructive Approaches for Understanding the Origin of Self-Replication and Evolution

The mystery of the origin of life can be divided into two parts. The first part is the origin of biomolecules: under what physicochemical conditions did biomolecules such as amino acids, nucleotides, and their polymers arise? The second part of the mystery is the origin of life-specific functions such as the replication of genetic information, the reproduction of cellular structures, metabolism, and evolution. These functions require the coordination of many different kinds of biological molecules. A direct strategy to approach the second part of the mystery is the constructive approach, in which life-specific functions are recreated in a test tube from specific biological molecules. Using this approach, we are able to employ design principles to reproduce life-specific functions, and the knowledge gained through the reproduction process provides clues as to their origins. In this mini-review, we introduce recent insights gained using this approach, and propose important future directions for advancing our understanding of the origins of life.