Balance of Attraction and Repulsion in Nucleic-Acid Base Stacking: CCSD(T)/Complete-Basis-Set-Limit Calculations on Uracil Dimer and a Comparison with the Force-Field Description

Morgado, Claudio A.; Jurečka, Petr; Svozil, Daniel; Hobza, Pavel; Šponer, Jiřı́

doi:10.1021/ct9000125

Cited by 52 publications

(68 citation statements)

References 140 publications

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“…Based on CCSD(T) studies of uracil 63 and adenine 45 dimers, we consider bases to be unstacked if d 0 > 5.0 Å. If d 0 ≤ 3.5 Å, the stacking score is incremented +1 for d 0 .…”

Section: Methodsmentioning

confidence: 99%

Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics

Condon

Kennedy

Mort

et al. 2015

J. Chem. Theory Comput.

101

379

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Molecular dynamics (MD) simulations for RNA tetramers r(AAAA), r(CAAU), r(GACC), and r(UUUU) are benchmarked against 1H–1H NOESY distances and 3J scalar couplings to test effects of RNA torsion parametrizations. Four different starting structures were used for r(AAAA), r(CAAU), and r(GACC), while five starting structures were used for r(UUUU). On the basis of X-ray structures, criteria are reported for quantifying stacking. The force fields, AMBER ff99, parmbsc0, parm99χ_Yil, ff10, and parmTor, all predict experimentally unobserved stacks and intercalations, e.g., base 1 stacked between bases 3 and 4, and incorrect χ, ϵ, and sugar pucker populations. The intercalated structures are particularly stable, often lasting several microseconds. Parmbsc0, parm99χ_Yil, and ff10 give similar agreement with NMR, but the best agreement is only 46%. Experimentally unobserved intercalations typically are associated with reduced solvent accessible surface area along with amino and hydroxyl hydrogen bonds to phosphate nonbridging oxygens. Results from an extensive set of MD simulations suggest that recent force field parametrizations improve predictions, but further improvements are necessary to provide reasonable agreement with NMR. In particular, intramolecular stacking and hydrogen bonding interactions may not be well balanced with the TIP3P water model. NMR data and the scoring method presented here provide rigorous benchmarks for future changes in force fields and MD methods.

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“…Based on CCSD(T) studies of uracil 63 and adenine 45 dimers, we consider bases to be unstacked if d 0 > 5.0 Å. If d 0 ≤ 3.5 Å, the stacking score is incremented +1 for d 0 .…”

Section: Methodsmentioning

confidence: 99%

Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics

Condon

Kennedy

Mort

et al. 2015

J. Chem. Theory Comput.

101

379

View full text Add to dashboard Cite

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“…703–948) contains stacked adenine 34 and uracil pairs. 35 The interaction energies were calculated by extrapolating the aDZ and aTZ energies plus higher-order correlation energy calculated at CCSD(T) level using a small basis set. Set 6 (Nos 949–1048) comes from Svozil, Hobza and Šponer’s work on intrinsic stacking energy calculation for dinucleotide steps in A-RNA and B-DNA duplexes.…”

Section: Methodsmentioning

confidence: 99%

Development of Polarizable Models for Molecular Mechanical Calculations. 4. van der Waals Parametrization

Wang

Cieplak

et al. 2012

J. Phys. Chem. B

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In the previous publications of this series, we presented a set of Thole induced dipole interaction models using four types of screening functions. In this work, we document our effort to refine the van der Waals parameters for the Thole polarizable models. Following the philosophy of AMBER force field development, the van der Waals (vdW) parameters were tuned for the Thole model with linear screening function to reproduce both the ab initio interaction energies and the experimental densities of pure liquids. An in-house genetic algorithm was applied to maximize the fitness of “chromosomes” which is a function of the root-mean-square errors (RMSE) of interaction energy and liquid density. To efficiently explore the vdW parameter space, a novel approach was developed to estimate the liquid densities for a given vdW parameter set using the mean residue-residue interaction energies through interpolation/extrapolation. This approach allowed the costly molecular dynamics simulations be performed at the end of each optimization cycle only and eliminated the simulations during the cycle. Test results show notable improvements over the original AMBER FF99 vdW parameter set as indicated by the reduction in errors of the calculated pure liquid density (d), heat of vaporization (Hvap) and hydration energy. The average percent error (APE) of the densities of 59 pure liquids was reduced from 5.33% to 2.97%; the RMSE of Hvap was reduced from 1.98 kcal/mol to 1.38 kcal/mol; the RMSE of solvation free energies of 15 compounds was reduced from 1.56 kcal/mol to 1.38 kcal/mol. For the interaction energies of 1639 dimers, the overall performance of the optimized vdW set is slightly better than the original FF99 vdW set (RMSE of 1.56 versus 1.63 kcal/mol). The optimized vdW parameter set was also evaluated for the exponential screening function used in the Amoeba force field to assess its applicability for different types of screening functions. Encouragingly, comparable performance was observed when the optimized vdW set was combined with Thole Amoeba-like polarizable model, particularly for the interaction energy and liquid density calculations. Thus, the optimized vdW set is applicable to both types of Thole models with either linear or Amoeba-like screening functions.

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“…In fact, Sponer and coworkers have examined more than 100 geometries of the stacked uracil 117 and stacked adenine 118 dimers in order to compare their potential energy surface computed with a variety of methods. For both systems, SCS-MP2 mimics quite nicely the reference PES computed at CCSD(T).…”

Section: Base Pairsmentioning

confidence: 99%

Solution‐Phase Organic Chemistry

Bachrach

2014

Computational Organic Chemistry

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Standard quantum chemical computations are performed on a single molecule or complex. This isolated species represents a molecule in the gas phase. While gas-phase chemistry comprises an important chemical subdiscipline, the vast majority of chemical reactions occur in solution. Perhaps most critical is that all of biochemistry takes place in an aqueous environment, and so if computational chemistry is to be relevant for biochemical applications, treatment of the solvent is imperative.Neglecting solvent effects is extremely hazardous. Equilibria and kinetics can be dramatically altered by the nature of the solvent. For example, the rate of nucleophilic substitution reactions spans 20 orders of magnitude in going from the gas phase to polar and nonpolar solvents. 1 -3 A classical example of a dramatic solvent effect on equilibrium is the tautomerism between 1 and 2. In the gas phase, the equilibrium lies far to the left, while in the solution phase, 2 dominates because of its much larger dipole moment. 4 Another classical example is that the trend in gas-phase acidity of aliphatic alcohols is reverse of the well-known trend in the solution phase; in other words, in the solution phase, the relative acidity trend is R 3 COH < R 2 CHOH < RCH 2 OH, but the opposite is true in the gas phase. 5,6 1 2Through the 1980s, computational chemists did neglect solvent effects, because they either hoped it would not matter or had no real way to effectively treat solvation. In terms of the former case, modeling reactions involving nonpolar molecules with nonpolar transition states (TSs) in nonpolar solvents as gas-phase chemistry might be appropriate. This, however, comprises a very small subset of chemistry, and even small changes in charge distribution can manifest themselves in large solvent effects.

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Balance of Attraction and Repulsion in Nucleic-Acid Base Stacking: CCSD(T)/Complete-Basis-Set-Limit Calculations on Uracil Dimer and a Comparison with the Force-Field Description

Cited by 52 publications

References 140 publications

Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics

Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics

Development of Polarizable Models for Molecular Mechanical Calculations. 4. van der Waals Parametrization

Solution‐Phase Organic Chemistry

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