Academic literature on the topic 'Nucleic Acids, RNA Folding, Structure Prediction, RNA Evolution'

Abstract DNA, RNA and proteins are major biological macromolecules that coevolve and adapt to environments as components of one highly interconnected system. We explore here sequence/structure determinants of mechanisms of adaptation of these molecules, links between them, and results of their mutual evolution. We complemented statistical analysis of genomic and proteomic sequences with folding simulations of RNA molecules, unraveling causal relations between compositional and sequence biases reflecting molecular adaptation on DNA, RNA and protein levels. We found many compositional peculiarities related to environmental adaptation and the life style. Specifically, thermal adaptation of protein-coding sequences in Archaea is characterized by a stronger codon bias than in Bacteria. Guanine and cytosine load in the third codon position is important for supporting the aerobic life style, and it is highly pronounced in Bacteria. The third codon position also provides a tradeoff between arginine and lysine, which are favorable for thermal adaptation and aerobicity, respectively. Dinucleotide composition provides stability of nucleic acids via strong base-stacking in ApG dinucleotides. In relation to coevolution of nucleic acids and proteins, thermostability-related demands on the amino acid composition affect the nucleotide content in the second codon position in Archaea.

Sequence folding is known to determine the spatial structure and catalytic function of proteins and nucleic acids. We show here that folding also plays a key role in enhancing the evolutionary stability of the intermolecular recognition necessary for the prevalent mode of catalytic action in replication, namely, in trans, one molecule catalyzing the replication of another copy, rather than itself. This points to a novel aspect of why molecular life is structured as it is, in the context of life as it could be: folding allows limited, structurally localized recognition to be strongly sensitive to global sequence changes, facilitating the evolution of cooperative interactions. RNA secondary structure folding, for example is shown to be able to stabilize the evolution of prolonged functional sequences, using only a part of this length extension for intermolecular recognition, beyond the limits of the (cooperative) error threshold. Such folding could facilitate the evolution of polymerases in spatially heterogeneous systems. This facilitation is, in fact, vital because physical limitations prevent complete sequence-dependent discrimination for any significant-size biopolymer substrate. The influence of partial sequence recognition between biopolymer catalysts and complex substrates is investigated within a stochastic, spatially resolved evolutionary model of trans catalysis. We use an analytically tractable nonlinear master equation formulation called PRESS (McCaskill et al., Biol. Chem. 382: 1343–1363), which makes use of an extrapolation of the spatial dynamics down from infinite dimensional space, and compare the results with Monte Carlo simulations.

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

The secondary structure for nucleic acids provides a level of description that is both abstract enough to allow for efficient algorithms and realistic enough to provide a good approximate to the thermodynamic and kinetics properties of RNA structure formation. The secondary structure model has furthermore been successful in explaining salient features of RNA evolution in nature and in the test tube. In this contribution we review the computational chemistry of RNA secondary structures using a simplified algorithmic approach for explanation.

While all anticoagulants have, to a certain extent, novel properties, the development of agents that inhibit specific coagulation proteases through structural affinity and can be inhibited themselves by the concomitant production of antidotes (drug–antidote pair construct) has the potential to revolutionize the field. With the evolution of our thinking toward hemostasis and thrombosis has come new pharmacologic constructs for safe and effective treatment. Aptamers are single-stranded nucleic acids that inhibit a protein’s function by folding into a specific three-dimensional structure that defines high-affinity binding to the target protein (White et al., 2000). The term aptamer (from the Latin aptus, “to fit”) was coined by Ellington and Szostak (1990) following their pioneering work published originally in Nature. Based on iterative selection techniques, aptamers that bind essentially any protein or small molecule can be generated. A high-affinity, specific inhibitor that interacts with functional groups (on both the nucleic acid and the protein) can be constructed if a small amount of pure target is available. The initiation point for aptamer development is a combinatorial library composed of single-stranded nucleic acids (RNA, DNA, or modified RNA), typically containing 20 to 40 randomized positions (1024 different sequences). Isolation of high-affinity nucleic acid ligands involves a process known as SELEX (systemic evolution of ligands by exponential enrichment). The starting library is incubated with the protein of interest. Nucleic acid molecules that adopt conformations that allow target protein binding are subsequently partitioned from other sequences (that do not bind the protein). The bound sequences are removed and amplified by reverse transcription and polymerase chain reaction (PCR) (for RNA-based libraries) or PCR alone (for DNA-based libraries). After repeating the process several times, the selected ligands are secured and evaluated for binding affinity and ability to inhibit activity (of the target protein). Postselection optimization steps typically include (1) reduction in aptamer length (from a starting molecule of 80–100 nucleotides to 40 nucleotides); (2) enhanced stability in biologic systems (achieved by substitution of ribonucleotides with 2-amino, 2´-fluoro, or 2´-0-alkyl nucleotides and protection from exonuclease digestion by 3´ end capping); and (3) reduced renal clearance (achieved by increasing the molecules’ mo lecular weight through site-specific addition of polyethylene glycol moieties or other hydrophobic groups.