Mechanism of protein-RNA recognition: analysis based on the statistical mechanics of hydration

Spontaneous binding of single-stranded RNAs to RRM proteins visualized by unbiased atomistic simulations with a rescaled RNA force field

Recognition of single-stranded RNA (ssRNA) by RNA recognition motif (RRM) domains is an important class of protein-RNA interactions. Many such complexes were characterized using nuclear magnetic resonance (NMR) and/or X-ray crystallography techniques, revealing ensemble-averaged pictures of the bound states. However, it is becoming widely accepted that better understanding of protein-RNA interactions would be obtained from ensemble descriptions. Indeed, earlier molecular dynamics simulations of bound states indicated visible dynamics at the RNA-RRM interfaces. Here, we report the first atomistic simulation study of spontaneous binding of short RNA sequences to RRM domains of HuR and SRSF1 proteins. Using a millisecond-scale aggregate ensemble of unbiased simulations, we were able to observe a few dozen binding events. HuR RRM3 utilizes a pre-binding state to navigate the RNA sequence to its partially disordered bound state and then to dynamically scan its different binding registers. SRSF1 RRM2 binding is more straightforward but still multiple-pathway. The present study necessitated development of a goal-specific force field modification, scaling down the intramolecular van der Waals interactions of the RNA which also improves description of the RNA-RRM bound state. Our study opens up a new avenue for large-scale atomistic investigations of binding landscapes of protein-RNA complexes, and future perspectives of such research are discussed.

De novo ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation

Herein, we have generated ssRNA aptamers to inhibit SARS-CoV-2 M^pro, a protease necessary for the SARS-CoV-2 coronavirus replication. Because there is no aptamer 3D structure currently available in the databanks for this protein, first, we modeled an ssRNA aptamer using an entropic fragment-based strategy. We refined the initial sequence and 3D structure by using two sequential approaches, consisting of an elitist genetic algorithm and an RNA inverse process. We identified three specific aptamers against SARS-CoV-2 M^pro, called MApta^pro, MApta^pro-IR1, and MApta^pro-IR2, with similar 3D conformations and that fall in the dimerization region of the SARS-CoV-2 M^pro necessary for the enzymatic activity. Through the molecular dynamic simulation and binding free energy calculation, the interaction between the MApta^pro-IR1 aptamer and the SARS-CoV-2 M^pro enzyme resulted in the strongest and the highest stable complex; therefore, the ssRNA MApta^pro-IR1 aptamer was selected as the best potential candidate for the inhibition of SARS-CoV-2 M^pro and a perspective therapeutic drug for the COVID-19 disease.

How Does the Recently Discovered Peptide MIP Exhibit Much Higher Binding Affinity than an Anticancer Protein p53 for an Oncoprotein MDM2?

An oncoprotein MDM2 binds to the extreme N-terminal peptide region of a tumor suppressor protein p53 (p53NTD) and inhibits its anticancer activity. We recently discovered a peptide named MIP which exhibits much higher binding affinity for MDM2 than p53NTD. Experiments showed that the binding free energy (BFE) of MDM2-MIP is lower than that of MDM2-p53NTD by approximately -4 kcal/mol. Here, we develop a theoretical method which is successful in reproducing this quantitative difference and elucidating its physical origins. It enables us to decompose the BFE into a variety of energetic and entropic components, evaluate their relative magnitudes, and identify the physical factors driving or opposing the binding. It should be applicable also to the assessment of differences among ligands in the binding affinity for a particular receptor, which is a central issue in modern chemistry. In the MDM2 case, the higher affinity of MIP is ascribed to a larger gain of translational, configurational entropy of water upon binding. This result is useful to the design of a peptide possessing even higher affinity for MDM2 as a reliable drug against a cancer.

Statistical thermodynamics for the unexpectedly large difference between disaccharide stereoisomers in terms of solubility in water

We unravel the physical origins of the large difference between cellobiose and maltose, which consist of two β-1,4 and α-1,4 linked d-glucose units, respectively, in terms of the solubility in water. We construct a thermodynamic theory where the chemical-potential difference between disaccharides in water and in vacuum is identified as the key free-energy function. Its energetic and entropic components are calculated for cellobiose and maltose by statistical-mechanical theories for solute hydration. The disaccharide structures are taken into account at the atomic level and a molecular model is adopted for water. Molecular dynamics simulations are used to account for the conformational fluctuation of a disaccharide molecule, which also enables us to estimate the conformational entropy. We show that the cellobiose/maltose solubility ratio calculated is in good agreement with the experimental value. The solubility becomes much lower for cellobiose due to conformational-entropy and water-entropy effects. The former effect is relevant to higher stability of the intramolecular hydrogen bond between oxygen atoms in the six-membered ring and in the neighboring hydroxyl group: the hydration alters the fluctuation of a molecular conformation to a larger or less regular one, but the degree of this alteration is smaller. The latter effect is attributed to more separation of two hydroxymethyl groups in a molecule, causing lower probability of the overlap of excluded volumes generated by the groups for water molecules. We suggest that physicochemical properties of disaccharides in water become variable depending on the stereoisomerism through hydration effects and the origins of the variety are entropic.