Molecular recognition of RNA: challenges for modelling interactions and plasticity

Abstract

There is growing interest in molecular recognition processes of RNA because of RNA's widespread involvement in biological processes. Computational approaches are increasingly used for analysing and predicting binding to RNA, fuelled by encouraging progress in developing simulation, free energy and docking methods for nucleic acids. These developments take into account challenges regarding the energetics of RNA-ligand binding, RNA plasticity, and the presence of water molecules and ions in the binding interface. Accordingly, we will detail advances in force field and scoring function development for molecular dynamics (MD) simulations, free energy computations and docking calculations of nucleic acid complexes. Furthermore, we present methods that can detect moving parts within RNA structures based on graph-theoretical approaches or normal mode analysis (NMA). As an example of the successful use of these developments, we will discuss recent structure-based drug design approaches that focus on the bacterial ribosomal A-site RNA as a drug target.

Synthesis, antibacterial studies, and molecular modeling studies of 3,4-dihydropyrimidinone compounds

“…binding interaction provides the key features to synthesize the novel and potent rRNA binding small molecules and could diminish constancy of bacterial protein synthesis [2,6]. The specific rRNA binding affinity of novel dihydropyrimidinone (DHPM) compounds were validated by combined approach of computational simulation.…”

Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids

“…Possible reasons for failing to dock to RNA are an inappropriate treatment of electrostatic interactions or disregarding RNAÀligand contacts mediated by water. 12 Consequently, we selected a subset of 17 holo structures for our evaluation data set where docking the ligands back into the bound RNA structures ("redocking") was successful in all cases. These structures comprise six different classes of RNAÀligand complexes (16S E. coli rRNA, aptamer RNA, 23S H. marismortui rRNA, 16S T. thermophilus rRNA, HIV-1 TAR RNA, and thi-box riboswitch RNA).…”

“…16 Analogous to proteinÀligand docking, 17 three major classes of approaches are conceivable. 12 First, plasticity can be implicitly considered applying a soft-docking strategy with attenuated repulsive forces between target and ligand, but the range of possible movements that can be covered this way is rather limited. Second, only shifts of a few nucleotides are modeled, which assumes a rigid RNA backbone.…”

Targeting Nucleic Acids using Dynamic Combinatorial Chemistry

“…[4,5] The study of small molecules that recognize and bind with high affinity and selectivity to duplex, triplex, and quadruplex DNA, as well as hairpins, bulges, and RNA loops has attracted significant interest because of the involvement of many of these structures in disease. [5][6][7][8][9][10][11] Medicinal and synthetic chemists have incorporated the features present in natural products to design new ligands in order to elucidate the rules that govern nucleic acid recognition. [5,12,13] While significant progress has been made in the design of synthetic molecules that bind to the minor groove of DNA, [12] the design of sequence-specific compounds that bind to the major or minor grooves remains challenging.…”

“…The recognition of RNA is much less advanced than DNA, with the unique folded structure requiring the ability to recognize structural parameters that arise from irregularities such as bulges, loops, and mismatches. [6] Dynamic combinatorial chemistry (DCC) is a new approach to understanding molecular recognition. In DCC, building blocks that incorporate functional groups that are able to undergo reversible reactions, are equilibrated to generate a dynamic combinatorial library (DCL) that comprises all possible combinations of the building blocks (Fig.…”