Comparison of Molecular Mechanics, Semi-Empirical Quantum Mechanical, and Density Functional Theory Methods for Scoring Protein–Ligand Interactions

Yilmazer, Nusret Duygu; Korth, Martin

doi:10.1021/jp402719k

Cited by 55 publications

(53 citation statements)

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“…The Yilmazer and Korth (2013) study raises a similar question about whether benchmark results for semiempirical barrier height-predictions on small systems, such as the BH76 and BHPERI subsets of GMTK24/30, are transferable to barrier height predictions for enzymes. The first step towards answering this question is to create a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions.…”

Section: Introductionmentioning

confidence: 99%

“…While this is encouraging one concern is whether the results obtained for the small systems that make up these data sets are representative of those one would obtain for large systems. For example, Yilmazer and Korth (2013) performed a benchmark study of hundreds of protein-ligand complexes that included protein atoms within up to 10Å from the ligand and showed, for example, that the mean absolute difference (MAD) between interaction energies computed using PM6-DH+ and BP86-D2/TZVP was 14 kcal/mol. In comparison the MADs for the S22 interaction energy subset of GMTKN24 are <2 kcal/mol for both dispersion corrected PM6 and DFT/TZVP calculations (Korth and Thiel, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Towards a barrier height benchmark set for biologically relevant systems

Kromann

Christensen

Jensen

2016

Preprint

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We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6-311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10-15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4-5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a barrier height benchmark set for biologically relevant systems

Kromann

Christensen

Jensen

2016

Preprint

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“…[26] The corrections to noncovalent interactions in semiempirical methods has been thoroughly tested in biological systems. [27][28][29] In particular, the PM6 method [30] and its family of derived methods have been widely used to model biological and other complex systems on several occasions. [28,[31][32][33][34] The PM6 method has also been used to model the geometries of proteins with a good degree of success.…”

Section: Introductionmentioning

confidence: 99%

“…[27][28][29] In particular, the PM6 method [30] and its family of derived methods have been widely used to model biological and other complex systems on several occasions. [28,[31][32][33][34] The PM6 method has also been used to model the geometries of proteins with a good degree of success. [35] Recently, PM6-DH1 has also been highlighted by Yilmazer et al as a fast and accurate alternative to a full ab initio computation of the interactions between proteins and their ligands.…”

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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“…Dispersion and hydrogen bonded corrections to the PM6 method such as PM6-DH2 [3], PM6-D3H4 [4] and PM6-DH+ [5] yield interaction energies that in many cases rival in accuracy those computed with Density Functional Theory (DFT) [6,7]. The computational efficiency of the underlying PM6 method allows for calculations that are not practically possible with DFT or HF, such as geometry optimizations of proteins or vibrational analyses of large systems.…”

Section: Contents Introductionmentioning

confidence: 99%

New methods for computational drug design

Kromann

2015

Preprint

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PrePrints AbstractThis thesis describes the work that has been carried out in connection with my Masters at the University of Copenhagen. This work has led to new dispersion and hydrogen bond corrections to the PM6 method, PM6-D3H+, and its implementation in the GAMESS program. The method combines the DFT-D3 dispersion correction by Grimme et al. with a modified version of the H+ hydrogen bond correction by Korth. This work also included the implementation of the new HF-3c method in GAMESS and its interface with the fragmentation method FMO.Overall, the interaction energy of PM6-D3H+ is very similar to PM6-DH2 and PM6-DH+, with RMSD and MAD values within 0.02 kcal/mol of one another. HF-3c also shows interaction energies within the same order of accuracy as the PM6 based methods. The main difference is that the geometry optimizations of 88 complexes result in 82, 6, 0, and 0 geometries with 0, 1, 2, and 3 or more imaginary frequencies using PM6-D3H+ implemented in GAMESS, while the corresponding numbers for PM6-DH+ implemented in MOPAC are 54, 17, 15, and 2. PM6-D3H+ and FMO2-HF3c in GAMESS was used to optimize two small proteins which resulted in a much more reliable structure compared to the reference structures, than PM6-DH+ in MOPAC, most likely due to the different optimization algorithms associated with the programs.The PM6-D3H+ method as implemented in GAMESS offers an attractive alternative to PM6-DH+ in MOPAC in cases where the LBFGS optimizer must be used and a vibrational analysis is needed, e.g., when computing vibrational free energies. While the GAMESS implementation is up to 10 times slower for geometry optimizations of proteins in bulk solvent compared to MOPAC, it is sufficiently fast to make geometry optimizations of small proteins practically feasible.

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Comparison of Molecular Mechanics, Semi-Empirical Quantum Mechanical, and Density Functional Theory Methods for Scoring Protein–Ligand Interactions

Cited by 55 publications

References 50 publications

Towards a barrier height benchmark set for biologically relevant systems

Towards a barrier height benchmark set for biologically relevant systems

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

New methods for computational drug design

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