Prediction of octanol-water partition coefficients for the SAMPL6-$$\log P$$ molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields

Fan, Shujie; Iorga, Bogdan I.; Beckstein, Oliver

doi:10.1007/s10822-019-00267-z

Cited by 44 publications

(60 citation statements)

References 53 publications

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“…Previous SAMPL challenges have looked at the prediction of solvation free energies [8][9][10][11][12], guest-host [13][14][15][16][17][18][19] and protein-ligand binding affinities [20][21][22][23][24][25][26], pK a [27][28][29][30][31][32][33], distribution coefficients [34][35][36][37], and partition coefficients [38][39][40][41]. These challenges have helped uncover sources of error, pinpoint the reasons various methods performed poorly or well and their strengths and weaknesses, and facilitate (1) log P = log 10 K ow = log 10 [unionized solute] octanol [unionized solute] water dissemination of lessons learned after each challenge ends, ultimately leading to improved methods and algorithms.…”

Section: Motivation For the Log P And Pk A Challengementioning

confidence: 99%

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Bergazin

Tielker

Zhang

et al. 2021

J Comput Aided Mol Des

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The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

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Section: Motivation For the Log P And Pk A Challengementioning

confidence: 99%

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Bergazin

Tielker

Zhang

et al. 2021

J Comput Aided Mol Des

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“…These sets were ranked based on the computed HFEs and the IODs. The HFEs were calculated via stratified all-atom alchemical free energy perturbation (FEP) MD simulations (Fan et al, 2020;Klimovich et al, 2015) and a finite size correction (Reif and Hünenberger, 2011) was applied to the simulated free energy values. In particular, a total correction of -150.1 kJ/mol, consisting of a type C 1 correction of -149.8 kJ/mol for the use of a Ewald method to evaluate electrostatics, a type C 2 correction of -1.3 kJ/mol to correct for the artifactual constraint of vanishing average potential in the Ewald method, and a type D (PBC/LS) correction of +1.0 kJ/mol to adjust for the difference in the dielectric permittivity between the water model and real water, was added to obtain final HFE that could be compared to the experimental value.…”

Section: Classical Force Field Model For Zn(ii) Ionsmentioning

confidence: 99%

Zinc Dependent Conformational Changes in the Cation Diffusion Facilitator YiiP from S. oneidensis

Maria

Fan

Koide

et al. 2020

Preprint

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YiiP is a secondary transporter that couples Zn2+ transport to the proton motive force. Structural studies of YiiP from prokaryotes as well as Znt8 from humans revealed three different Zn2+ sites and a conserved homodimeric architecture. These structures define the inward-facing and outward-facing states that characterize the archetypal alternating access mechanism of transport. To study effects of Zn2+ binding on the conformational transition, we have used a YiiP/Fab complex for single-particle cryo-EM together with Molecular Dynamics simulation to compare structures of YiiP from S. oneidensis in presence and absence of Zn2+. Without Zn2+, YiiP exhibits enhanced flexibility and adopts a novel conformation that appears to be an intermediate state. The transition closes a hydrophobic gate and is controlled by the Zn2+ site at the cytoplasmic membrane interface. This work enhances our understanding of individual Zn2+ binding sites and their role in the conformational dynamics that governs the transport cycle.

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“…Relatively few literature studies evaluate force field accuracy on general small drug-like molecules, in contrast to force fields for proteins 17 – 25 , nucleic acids 26 – 28 , carbohydrates 29 – 32 , and other specific chemical systems 33 – 45 . On small molecules, these studies comprise predictions of solvation free energies 46 , 47 , strain energies 48 experimental osmotic coeﬃcients 49 , partition coeﬃcients 50 , 51 , conformer energies 52 – 56 , conformer geometries 55 , 57 , and robustness of parameterization 57 . Most of these studies assess four or fewer force fields on molecule sets up to several hundreds of molecules.…”

Section: Introductionmentioning

confidence: 99%

Benchmark assessment of molecular geometries and energies from small molecule force fields

et al. 2020

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Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

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Prediction of octanol-water partition coefficients for the SAMPL6-$$\log P$$ molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields

Cited by 44 publications

References 53 publications

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Zinc Dependent Conformational Changes in the Cation Diffusion Facilitator YiiP from S. oneidensis

Benchmark assessment of molecular geometries and energies from small molecule force fields

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