Comparison of some dispersion-corrected and traditional functionals with CCSD(T) and MP2 <i>ab initio</i> methods: Dispersion, induction, and basis set superposition error

Roy, Dipankar; Marianski, Mateusz; Maitra, Neepa T.; Dannenberg, J. J.

doi:10.1063/1.4755990

Cited by 53 publications

(48 citation statements)

References 68 publications

(34 reference statements)

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“…We have used this procedure on small systems. 21 However, TABLE V. E's (kcal/mol) for aggregation of parallel sheets and sums of pairwise interactions using DFT, MP2, and CCSD(T) with the cc-PVTZ basis set for structures of Figure 1. the small changes expected would not bring the CCSD(T) values close to the interaction energies predicted by the DFs.…”

Section: Resultsmentioning

confidence: 99%

“…We have published an evaluation of the DFs compared to high level calculations for small systems that have dispersion combined with inductive interactions. 21 This report contains a comprehensive list of the database used for parameterization of the DFs. In order to further probe the local interactions that contribute to overall stabilizations of the stacked structures, we divided each structure into two parts that we considered separately: (1) the peptide backbone, and (2) the aromatic rings.…”

Section: Resultsmentioning

confidence: 99%

“…None of the functionals used, even those specifically designed to include dispersion interactions (and which have been shown to exaggerate the importance of these interactions 4,19,21 ), predict the stacked structure, C, to be the most stable. The results strongly suggest that π -stacking of phenylalanine residues does not provide a substantial energetic contribution to the formation of β-sheet type structures.…”

Section: Discussionmentioning

confidence: 99%

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The interactions of phenylalanines in β-sheet-like structures from molecular orbital calculations using density functional theory (DFT), MP2, and CCSD(T) methods

Pohl¹,

Plumley²,

Dannenberg³

2013

The Journal of Chemical Physics

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We present density functional theory calculations designed to evaluate the importance of π -stacking interactions to the stability of in-register Phe residues within parallel β-sheets, such as amyloids. We have used a model of a parallel H-bonded tetramer of acetylPheNH 2 as a model and both functionals that were specifically designed to incorporate dispersion effects (DFs), as well as, several traditional functionals which have not been so designed. None of the functionals finds a global minimum for the π -stacked conformation, although two of the DFs find this to be a local minimum. The stacked phenyls taken from the optimized geometries calculated for each functional have been evaluated using MP2 and CCSD(T) calculations for comparison. The results suggest that π -stacking does not make an important contribution to the stability of this system and (by implication) to amyloid formation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The interactions of phenylalanines in β-sheet-like structures from molecular orbital calculations using density functional theory (DFT), MP2, and CCSD(T) methods

Pohl¹,

Plumley²,

Dannenberg³

2013

The Journal of Chemical Physics

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“…can improve the accuracy of grid based electrostatic potential methods such as CHELPG and MK [50]. Roy [26] has shown that the B3LYP functional can account for dispersion and induction interactions in intermolecular complexes as well as functionals (eg MP2) designed to treat dispersion.…”

Section: Methodsmentioning

confidence: 99%

“…The van der Waals interactions in vacuo, ΔE vdW , are then calculated from equation (2), where -van der Waals interactions‖ are defined as a global term including all forces that are not hydrogen bonding or other polar interactions. Calculating ΔE vdW is difficult, since they include multiple anisotropic forces [26], and solvents have large attenuating effects compared to those in vacuo. All water molecules in the X-ray structures were removed, since it is unknown how many, if any, water molecules dynamically interact with the tyrosine kinase, inhibitor, or tyrosine kinaseinhibitor complex.…”

Section: The Approach Taken In This Study To Examine How Possible Dismentioning

confidence: 99%

Binding energies of tyrosine kinase inhibitors: Error assessment of computational methods for imatinib and nilotinib binding

Fong¹

2015

Computational Biology and Chemistry

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To cite this version:Clifford Fong. Binding Energies of Tyrosine Kinase Inhibitors: error assessment of computational methods for imatinib and nilotinib binding. Computational Biology and Chemistry, Elsevier, 2015, 58, pp.40-54. <10.1016/j.compbiolchem.2015 The binding energies of imatinib and nilotinib to tyrosine kinase have been determined by quantum mechanical (QM) computations, and compared with literature binding energy studies using molecular mechanics (MM). The potential errors in the computational methods include these critical factors: Errors in X-ray structures such as structural distortions and steric clashes give unrealistically high van der Waals energies, and erroneous binding energies.  MM optimisation gives a very different configuration to the QM optimisation for nilotinib, whereas the imatinib ion gives similar configurations  Solvation energies are a major component of the overall binding energy. The QM based solvent model (PCM/SMD) gives different values from those used in the implicit PBSA solvent MM models. A major error in inhibitor -kinase binding lies in the non-polar solvation terms.  Solvent transfer free energies and the required empirical solvent accessible surface area factors for nilotinib and imatinib ion to give the transfer free energies have been reverse calculated. These values differ from those used in the MM PBSA studies.  An intertwined desolvation -conformational binding selectivity process is a balance of thermodynamic desolvation and intramolecular conformational kinetic control.  The configurational entropies (TΔS) are minor error sources.

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Computed versus experimental energy barriers in solution: Influence of the type of the density functional approximation

Denjean,

Rio,

Ciofini

et al. 2024

J Comput Chem

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Mechanistic investigations at the density functional theory level of organic and organometallic reactions in solution are now broadly accessible and routinely implemented to complement experimental investigations. The selection of an appropriate functional among the plethora of developed ones is the first challenge on the way to reliable energy barrier calculations. To provide guidelines for the choice of an initial and reliable computational level, the performances of commonly used non‐empirical (PBE, PBE0, PBE0‐DH) and empirical density functionals (BLYP, B3LYP, B2PLYP) were evaluated relative to experimental activation enthalpies. Most reactivity databases to assess density functional performances are primarily based on high level calculations, here a set of experimental activation enthalpies of organic and organometallic reactions performed in solution were selected from the literature. As a general trend, the non‐empirical functionals outperform the empirical ones. The most accurate energy barriers are obtained with hybrid PBE0 and double‐hybrid PBE0‐DH density functionals, both providing similar performance. Regardless of the functional under consideration, the addition of the GD3‐BJ empirical dispersion correction does not enhance the accuracy of computed energy barriers.

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Comparison of some dispersion-corrected and traditional functionals with CCSD(T) and MP2 ab initio methods: Dispersion, induction, and basis set superposition error

Cited by 53 publications

References 68 publications

The interactions of phenylalanines in β-sheet-like structures from molecular orbital calculations using density functional theory (DFT), MP2, and CCSD(T) methods

The interactions of phenylalanines in β-sheet-like structures from molecular orbital calculations using density functional theory (DFT), MP2, and CCSD(T) methods

Binding energies of tyrosine kinase inhibitors: Error assessment of computational methods for imatinib and nilotinib binding

Computed versus experimental energy barriers in solution: Influence of the type of the density functional approximation

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