A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+

Kromann, Jimmy C.; Christensen, Anders; Steinmann, Casper; Korth, Martin; Jensen, Jan H.

doi:10.7287/peerj.preprints.353v1

Cited by 18 publications

(22 citation statements)

References 23 publications

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“…In this paper we benchmark the NMR chemical shift predictions on Ubiquitin and GB3. The structures are geometry optimized using PM6-D3H+ ( Kromann et al, 2014 ) using the PCM solvation model ( Tomasi, Mennucci & Cammi, 2005 ; Steinmann et al, 2013 ) and the CHARMM22/CMAP force field ( Mackerell, 2004 ) using the GB/SA solvation model ( Qiu et al, 1997 ) with the 1UBQ ( Vijay-Kumar, Bugg & Cook, 1987 ) and 2OED ( Ulmer et al, 2003 ) structures as starting points. The PM6-D3H+ optimizations are done using the GAMESS program ( Schmidt et al, 1993 ) with a convergence criterion of 5 × 10 −4 atomic units, while the CHARMM22/CMAP optimizations are done using TINKER ( Ponder & Richards, 1987 ) with the default convergence criterion of 0.01 kcal/mole/Å.…”

Section: Methodsmentioning

confidence: 99%

ProCS15: a DFT-based chemical shift predictor for backbone and Cβatoms in proteins

Larsen

Bratholm

Christensen

et al. 2015

PeerJ

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We present ProCS15: a program that computes the isotropic chemical shielding values of backbone and Cβ atoms given a protein structure in less than a second. ProCS15 is based on around 2.35 million OPBE/6-31G(d,p)//PM6 calculations on tripeptides and small structural models of hydrogen-bonding. The ProCS15-predicted chemical shielding values are compared to experimentally measured chemical shifts for Ubiquitin and the third IgG-binding domain of Protein G through linear regression and yield RMSD values of up to 2.2, 0.7, and 4.8 ppm for carbon, hydrogen, and nitrogen atoms. These RMSD values are very similar to corresponding RMSD values computed using OPBE/6-31G(d,p) for the entire structure for each proteins. These maximum RMSD values can be reduced by using NMR-derived structural ensembles of Ubiquitin. For example, for the largest ensemble the largest RMSD values are 1.7, 0.5, and 3.5 ppm for carbon, hydrogen, and nitrogen. The corresponding RMSD values predicted by several empirical chemical shift predictors range between 0.7–1.1, 0.2–0.4, and 1.8–2.8 ppm for carbon, hydrogen, and nitrogen atoms, respectively.

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Section: Methodsmentioning

confidence: 99%

ProCS15: a DFT-based chemical shift predictor for backbone and Cβatoms in proteins

Larsen

Bratholm

Christensen

et al. 2015

PeerJ

Self Cite

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show abstract

“…All energy terms are computed using gas phase geometries. ∆H f (X) is computed using either PM6 (Stewart, 2007) or PM6-D3H+ (Kromann et al, 2014) while ∆G • solv (X) is computed using either the SMD (Marenich et al, 2009) or COSMO (Klamt and Schüürmann, 1993) solvation method. The PM6-D3H+ and SMD calculations are performed with the GAMESS program (Schmidt et al, 1993), the latter using the semiempirical PCM interface developed by Steinmann et al (2013), while the COSMO calculations are performed using MOPAC2012.…”

Section: Computational Methodologymentioning

confidence: 99%

Prediction of pKa values using the PM6 semiempirical method

Kromann

Larsen

Moustafa

et al. 2016

Preprint

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The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences of 0.6 - 0.7 pH units. For carboxylic acids the accuracy (0.7 - 1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5 - 0.6), slightly worse (0.5 - 1.0), and worse (1.0 - 2.5), provided that di- and triethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 minutes per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

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Section: Computational Methodologymentioning

confidence: 99%

Peer Review #1 of "Prediction of pKa values using the PM6 semiempirical method (v0.1)"

2016

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The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences of 0.6-0.7 pH units. For carboxylic acids the accuracy (0.7-1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5-0.6), slightly worse (0.5-1.0), and worse (1.0-2.5), provided that di-and triethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 minutes per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for highthroughput screening using on the order of 100 core processors.

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A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+

Cited by 18 publications

References 23 publications

ProCS15: a DFT-based chemical shift predictor for backbone and Cβatoms in proteins

ProCS15: a DFT-based chemical shift predictor for backbone and Cβatoms in proteins

Prediction of pKa values using the PM6 semiempirical method

Peer Review #1 of "Prediction of pKa values using the PM6 semiempirical method (v0.1)"

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