Computational investigation of RNA CUG repeats responsible for myotonic dystrophy 1.

Despite the importance of the knowledge of molecular hydration entropy (ΔShyd) in chemical and biological processes, the exact calculation of ΔShyd is very difficult, because of the complexity in solute–water interactions. Although free-energy perturbation (FEP) methods have been employed quite widely in the literature, the poor convergent behavior of the van der Waals interaction term in the potential function limited the accuracy and robustness. In this study, we propose a new method for estimating ΔShyd by means of combining the FEP approach and the scaled particle theory (or information theory) to separately calculate the electrostatic solute–water interaction term (ΔSelec) and the hydrophobic contribution approximated by the cavity formation entropy (ΔScav), respectively. Decomposition of ΔShyd into ΔScav and ΔSelec terms is found to be very effective with a substantial accuracy enhancement in ΔShyd estimation, when compared to the conventional full FEP calculations. ΔScav appears to dominate over ΔSelec in magnitude, even in the case of polar solutes, implying that the major contribution to the entropic cost for hydration comes from the formation of a solvent-excluded volume. Our hybrid scaled particle theory and FEP method is thus found to enhance the accuracy of ΔShyd prediction by effectively complementing the conventional full FEP method.Computations were done in Advanced Research Computing (QUEST) at the Northwestern University, and Theory Group Computing Clusters at the University of Cambridge. This work was supported by the National Science Foundation Grant CHE-1147335) (GCS), PS-OC Center of the NIH/NCI Grant 1U54CA143869-01 (GCS), NIH Grant R01GM097455 (MDD), Muscular Dystrophy Association Grant 254929 (MDD), and the EPSRC Grant EP/I001352/1 (DJW), and the ERC Grant RG59508 (DJW). DC acknowledges financial support from the Cambridge Commonwealth European and International Trust.This is the final version of the article. It first appeared from ACS Publications via http://dx.doi.org/10.1021/acs.jctc.5b0072

Revision of AMBER Torsional Parameters for RNA Improves Free Energy Predictions for Tetramer Duplexes with GC and iGiC Base Pairs

All-atom force fields are important for predicting thermodynamic, structural, and dynamic properties of RNA. In this paper, results are reported for thermodynamic integration calculations of free energy differences of duplex formation when CG pairs in the RNA duplexes r(CCGG)2, r(GGCC)2, r(GCGC)2, and r(CGCG)2 are replaced by isocytidine–isoguanosine (iCiG) pairs. Agreement with experiment was improved when ε/ζ, α/γ, β, and χ torsional parameters in the AMBER99 force field were revised on the basis of quantum mechanical calculations. The revised force field, AMBER99TOR, brings free energy difference predictions to within 1.3, 1.4, 2.3, and 2.6 kcal/mol at 300 K, respectively, compared to experimental results for the thermodynamic cycles of CCGG → iCiCiGiG, GGCC → iGiGiCiC, GCGC → iGiCiGiC, and CGCG → iCiGiCiG. In contrast, unmodified AMBER99 predictions for GGCC → iGiGiCiC and GCGC → iGiCiGiC differ from experiment by 11.7 and 12.6 kcal/mol, respectively. In order to test the dynamic stability of the above duplexes with AMBER99TOR, four individual 50 ns molecular dynamics (MD) simulations in explicit solvent were run. All except r(CCGG)2 retained A-form conformation for ≥82% of the time. This is consistent with NMR spectra of r(iGiGiCiC)2, which reveal an A-form conformation. In MD simulations, r(CCGG)2 retained A-form conformation 52% of the time, suggesting that its terminal base pairs may fray. The results indicate that revised backbone parameters improve predictions of RNA properties and that comparisons to measured sequence dependent thermodynamics provide useful benchmarks for testing force fields and computational methods

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA’s 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures

Free vibration/buckling analyses of noncylindrical initially compressed helical composite springs

WOS: 000386159000004This article presents the use of the stiffness matrix method based on the first-order shear deformation theory to predict the fundamental natural frequencies and buckling loads of noncylindrical unidirectional composite helical springs subjected to initial static axial force and moment. This theoretical study about such springs with circular/rectangular cross-sections and large pitch angles is performed for the first time in the literature. The validity of the present results is verified by the benchmark studies related with initially compressed isotropic cylindrical springs

Dynamic docking of small molecules targeting RNA CUG repeats causing myotonic dystrophy type 1.

Expansion of RNA CUG repeats causes myotonic dystrophy type 1 (DM1). Once transcribed, the expanded CUG repeats strongly attract muscleblind-like 1 (MBNL1) proteins and disturb their functions in cells. Because of its unique structural form, expanded RNA CUG repeats are prospective drug targets, where small molecules can be utilized to target RNA CUG repeats to inhibit MBNL1 binding and ameliorate DM1-associated defects. In this contribution, we developed two physics-based dynamic docking approaches (DynaD and DynaD/Auto) and applied them to nine small molecules known to specifically target RNA CUG repeats. While DynaD uses a distance-based reaction coordinate to study the binding phenomenon, DynaD/Auto combines results of umbrella sampling calculations performed on 1 × 1 UU internal loops and AutoDock calculations to efficiently sample the energy landscape of binding. Predictions are compared with experimental data, displaying a positive correlation with correlation coefficient (R) values of 0.70 and 0.81 for DynaD and DynaD/Auto, respectively. Furthermore, we found that the best correlation was achieved with MM/3D-RISM calculations, highlighting the importance of solvation in binding calculations. Moreover, we detected that DynaD/Auto performed better than DynaD because of the use of prior knowledge about the binding site arising from umbrella sampling calculations. Finally, we developed dendrograms to present how bound states are connected to each other in a binding process. Results are exciting, as DynaD and DynaD/Auto will allow researchers to utilize two novel physics-based and computer-aided drug-design methodologies to perform in silico calculations on drug-like molecules aiming to target complex RNA loops

Angiomyofibroblastoma of the right labia major

[No Abstract Available