Method for the improved semiempirical description of intermolecular interactions of biomolecules and their fragments

Anikin, N. A.; Bugaenko, V. L.; Kuz’minskii, M. B.; Mendkovich, A. S.

doi:10.1007/s11172-012-0002-0

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Apart from the above listed substantial contributions, a number of additional PM3-D and AM1-D models were published, for instance by Anikin et al [55] in 2012, though methods like PM6-DH2 and PM6-DH + are readily available in MOPAC [56] from MOPAC2009 on. An important development related to SQM-DH type methods was the publication of corresponding GPU-enabled algorithms by Carvalho Maia et al [57] in 2012, which allowed for a very impressive illustration of the capability of PM6-DH + to identify native protein structures out of large sets of decoy conformations.…”

Section: Sqm Methods Sqm-dh Methods Development and Benchmarkingmentioning

confidence: 99%

Enhanced semiempirical QM methods for biomolecular interactions

Yilmazer

Korth

2015

Computational and Structural Biotechnology Journal

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Recent successes and failures of the application of ‘enhanced’ semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.

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Section: Sqm Methods Sqm-dh Methods Development and Benchmarkingmentioning

confidence: 99%

Enhanced semiempirical QM methods for biomolecular interactions

Yilmazer

Korth

2015

Computational and Structural Biotechnology Journal

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“…Despite the fact that a number of force fields have been parametrized to describe these interactions with a good margin of accuracy, the correct description of these types of interactions can only be done through the recovery of a large portion of electron correlation using large basis sets and high-level quantum chemistry methods. To avoid such costly computational solutions, ad hoc corrections in the energetics of dispersion interactions were proposed by Grimme [19,20] for DFT methods, followed closely by McNamara and Hillier [21] and more recently by Anikin [22] for semiempirical quantum chemical methods. Similar corrections for hydrogen bonds also followed, using specific methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid such costly computational solutions, ad hoc corrections in the energetics of dispersion interactions were proposed by Grimme for DFT methods, followed closely by McNamara and Hillier and more recently by Anikin for semiempirical quantum chemical methods. Similar corrections for hydrogen bonds also followed, using specific methodologies .…”

Section: Introductionmentioning

confidence: 99%

Assessment of semiempirical enthalpy of formation in solution as an effective energy function to discriminate native‐like structures in protein decoy sets

2016

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In this work, we tested the PM6, PM6-DH1, PM6-D3, and PM7 enthalpies of formation in aqueous solution as scoring functions across 33 decoy sets to discriminate native structures or good models in a decoy set. In each set these semiempirical quantum chemistry methods were compared according to enthalpic and geometric criteria. Enthalpically, we compared the methods according to how much lower was the enthalpy of each native, when compared with the mean enthalpy of its set. Geometrically, we compared the methods according to the fraction of native contacts (Q), which is a measure of geometric closeness between an arbitrary structure and the native. For each set and method, the Q of the best decoy was compared with the Q 0 , which is the Q of the decoy closest to the native in the set. It was shown that the PM7 method is able to assign larger energy differences between the native structure and the decoys in a set, arguably because of a better description of dispersion interactions, however PM6-DH1 was slightly better than the rest at selecting geometrically good models in the absence of a native structure in the set.

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