Water–Water and Water–Solute Interactions in Microsolvated Organic Complexes

Pérez, C.; Neill, J. L.; Muckle, M. T.; Zaleski, D. P.; Peña, I.; Lopez, J. C.; Alonso, J. L.; Pate, B. H. Angew. Chem. Int. Ed. 2015, 54, 979-982
Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission

The structure of water about a solute remains of critical importance towards understanding aqueous solvation. Microwave spectroscopy and computations are the best tools we have today to gain insight on this problem. This is nicely demonstrated in the Alonso study of the microsolvated structures of β-propiolactone 1.¹ They employed chirped-pulse Fourier transform microwave (CP-FTMW) spectroscopy and MP2(fc)/6-311++G(d,p) computations to examine the structure involving 1-5 water molecules.

The computed structures of these microsolvated species are shown in Figure 1. The deviation of the computed and experimental structures (RMS in the atomic positions) is small, though increasing as the size of the cluster increases. The deviation is 0.014 Å for the 1^. H₂O cluster and 0.244 Å for the 1^.(H₂O)₅cluster. They identified two clusters with four water molecules; the lower energy structure, labeled as a, is only 0.2 kJ mol^-1 more stable than structure b.

1.H2O	1.(H2O)2
1.(H2O)3
1.(H2O)4 a	1.(H2O)4 b
1.(H2O)5

Figure 1. MP2(fc)/6-311++G(d,p) optimized geometries of the hydrates of 1.

Water rings are found in the clusters having four or five water molecules, while chains are identified in the smaller clusters. One might imagine water cages appearing with even more water molecules in the microsolvated structures.

References

(1) Pérez, C.; Neill, J. L.; Muckle, M. T.; Zaleski, D. P.; Peña, I.; Lopez, J. C.; Alonso, J. L.; Pate, B. H. Angew. Chem. Int. Ed. 2015, 54, 979-982, DOI: 10.1002/anie.201409057.

InChIs

1: InChI=1S/C3H4O2/c4-3-1-2-5-3/h1-2H2
InChIKey=VEZXCJBBBCKRPI-UHFFFAOYSA-N

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First Principles View on Chemical Compound Space: Gaining Rigorous Atomistic Control of Molecular Properties

O. Anatole vonLilienfeld, Int. J. Quantum Chem. 2013, 113, 1676–1689.

DOI: 10.1002/qua.24375
Contributed by Johannes Hachmann

In this tutorial review, von Lilienfeld gives an insightful overview of his group’s efforts to enumerate and explore chemical compound space. The author addresses a number of topics and strategies his group uses to assess the quantum chemical properties of molecules in a well-defined domain of chemical space including their mutual relationships. The last part of the discussion covering machine learning approaches to developing an understanding of structure-property relationships is particularly interesting. In this part of the tutorial, the author discusses his group’s pioneering work towards recovering the mapping between a molecular structure and its (quantum mechanical) properties - which is rigorously provided by the Schrödinger equation - via a supervised machine learning approach. He gives a practical recipe for this scheme, including a discussion of his Coulomb matrix descriptor. This tutorial is a valuable contribution to facilitating a better understanding of this exciting new research area.

An isomeric reaction benchmark set to test if the performance of state-of-the-art density functionals can be regarded as independent of the external potential

T. Schwabe, Phys. Chem. Chem. Phys., 2014,16, 14559.

Contributed by Martin Korth.

It's an open question: Can more parameters efficiently replace knowledge about the underlying physical behaviour? Some examples:

Classical potentials usually have a fixed functional form, which is chosen to capture the basics of (inter)molecular interactions. Neural networks[1], Gaussian approximation potentials[2] and other machine-learning approaches do not rely on fixed potentials, but it's unclear whether systems with complex chemistry (for instance involving more than a handful of different elements) can be efficiently treated this way in general.

One step up on the theory ladder, semi-empirical quantum mechanical (SQM) methods relying more heavily on parametrization (like PM6, PM7) seem to perform overall as good as SQM methods trying to capture more of the correct physics by introducing orthogonalization corrections (OMx), but if one looks at complicated chemistry, the latter ones turn out to be more robust.[3]

Another step up, the highlighted paper by Schwabe comes into play: Also in the field of density functional theory (DFT) exchange-correlation (XC) functional development, people have tried both pathways; more physics (like including dispersion in a DFT-D type fashion[4]) and more parameters (how some Minnesota type functionals by Truhlar and co-workers capture -- at least mid-range -- dispersion[5]).

Schwabe now assesses a number of functionals for isomerization reactions in which heteroatoms are systematically replaced with heavier atoms of the same group. With this setup he has found a clever way to find out whether the performance of a method depends on the elements involved and thus the external potential - which should not be the case at least for the 'true' functional. Schwabe indeed finds no such dependence except for one case, M11-L, a functional on the more-parameter side of things, thereby suggesting that like for SQM methods, the more-physics pathway might be the safer road to follow.

It would be interesting to see how SQM methods (including SCC-DFTB) perform on Schwabe's set, though in this case not a dependence on the external potential but element-specific parametrization effects would be investigated (in the past we did not find element-specific trends for PM6[3] and also no such trends for WFT/DFT methods -- including some Minnesota functionals -- when we benchmarked 'mindless'[6]).

References:

[1] J. Behler,  Representing Potential-Energy Surfaces by High-Dimensional Neural Network Potentials, J. Phys.: Condens. Matter 26 (2014) 183001.

[2] A. P. Bartok, M. C. Payne, R. Kondor, G. Csanyi, Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons, Physical Review Letters 104 136403 (2010)

[3] M. Korth, W. Thiel, Benchmarking Semiempirical Methods for Thermochemistry, Kinetics and Noncovalent Interactions: OMx Methods are Almost as Accurate and Robust as DFT-GGA Methods for Organic Molecules. J. Chem. Theory Comput., 2011, 7, 2929.

[4] S. Grimme, Density functional theory with London dispersion corrections, WIREs: Comp. Mol. Sci. 2011, 1, 211.

[5] R. Peverati, D. G. Truhlar, The Quest for a Universal Density Functional: The Accuracy of Density Functionals Across a Broad Spectrum of Databases in Chemistry and Physics, Philosophical Transactions of the Royal Society A 372, 20120476/1-51 (2014).

[6] M. Korth, S. Grimme, 'Mindless' DFT Benchmarking, J. Chem. Theory Comput., 2009, 5, 993.

Benchmarking density-functional theory calculations of NMR shielding constants and spin–rotation constants using accurate coupled-cluster calculations

Andrew M. Teale, Ola B. Lutnæs, Trygve Helgaker, David J. Tozer, and Jürgen Gauss Journal of Chemical Physics 2013, 138, 024111

Contributed by +Jan Jensen 

source CC

The H-N HSQC spectrum of protein amide groups is one of the most frequently recorded experiments in protein NMR. $^{15}$N labeling is comparatively inexpensive and the spectrum can be acquired in a relatively short period of time.  Protein structure validation using just this spectrum would therefore be ideal and, in principle, possible since the N in particular is believed to report on many aspects of protein structure.

Accurate (within ca 0.3 ppm) predictions of backbone amide H chemical shifts (CS) are possible and can be used to improve protein structures. However, accurate (within ca 1 ppm) predictions of N CS have so far remained elusive.

Accurate N chemical shift predictions are possible
The paper by Teale et al. shows that accurate predictions of N CSs are possible. The benchmark set contains five molecules (NH$_3$, N$_2$, HCN, N$_2$O, and PN) for which N-chemical shieldings have been measured in the gas phase. One of the molecules contain 2 N atoms so one can compute five CSs (using NH$_3$ as a reference).  Using CCSD(T)/CBS//CCSD(T)/cc-pVTZ these five chemical shifts can be reproduced to within 2.2 ppm (and to within 1.4 ppm if the PN molecule is excluded).  However, such accuracy is only possible if vibrational effects (computed using the method due to Ruud et al. at the B3LYP/aug-cc-pCVTZ level of theory) are included because they contribute between -4.8 and 1.5 ppm to the CSs.

More benchmark data needed
Thus, conventional predictions of amide N CSs will contain an offset, the difference between the vibrational contributions to the chemical shielding of the amide and reference N. It would be very interesting to know what this offset is for an amide group and how much it depends on the local environment such as dihedral angles, side-chains, and hydrogen bonding.

Of course, similar data is also needed for the electronic component at the CCSD(T)/CBS level for benchmarking faster method.  Flaig et al. have recently published H and C CSs for formamide at CCSD(T)/cc-pVQZ level of theory, but did not report the N CS.  However, even this level might not be sufficient: Teale et al. show that the use of the cc-pCVQZ basis set lowers the MAE compared to experimental chemical shielding from 5.3 to 2.8 ppm.  Furthermore, extrapolation to the CBS limit reduces the  maximum error for the N CS predictions from 4.7 to 2.2 ppm (2.2 to 1.4 ppm if PN is excluded).

Finally, I have not been able to find much experimental N CS data for small prototypical amides such as formamide or acetamide in various solvents (Moon and Case quote a value for neat N-methyl acetamide). (There is of course much data for small peptides but they are conformationally flexible which introduces numerous complications for the predictions.) I doubt the measurements are very difficult but it is of course difficult to publish such data by themselves, so computational chemists need to bring their experimental colleagues on board in such benchmark studies.



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Structural Revision of Cephalosporolide J and Bassianolone

Song, L.; Lee, K.-H.; Lin, Z.; Tong, R. J. Org. Chem. 2014, 79, 1493-1497
Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission

Here is a story that must drive chemical database quality control personnel nuts. Song, et al. noticed that the reported ¹³C NMR of the natural products cephalosporolide C 1, cephalosporolide J 2 and bassianolone 3 are identical.¹ Given that it is highly unlikely that two diastereomers would have identical NMR spectra, the likelihood that these three have identical spectra seemed remote at best.

Compounds 1 and 2 were synthesized and their structures confirmed by x-ray crystallography. Their ¹³C NMR spectra show clear distinctions, indicating that the isolated “2” is actually 1. Experimental support for the notion that 1 and 3 are actually the same was provided by preparing the diacetylide of 1 and comparing its NMR spectra to that of natural “3”.

Quantum computations confirmed that in fact the natural product thought to be 3 is actually 1. The structures of 1 and 3 were optimized at B3LYP/6-311+G(2d,p) and ¹³C chemical shifts were computed with these geometries at mPW1PW91/6-311+G(2d,p)/CPCM(chloroform). (The computed structures are shown in Figure 1.) The mean absolute deviation (MAD) between the computed and experimental spectra for 1 is 0.97 ppm, while the MAD for the computed spectrum of 3 compared with the experimental values is 2.44 ppm, with a maximum error of 5.13ppm, more than twice the maximum error with structure 1. The authors attribute the misassignments to a faulty initial spectra of authentic cephalosporolide C 1.

1

3

Figure 1. B3LYP/6-311+G(2d,p) optimized geometries of 1 and 3.

References

(1) Song, L.; Lee, K.-H.; Lin, Z.; Tong, R. "Structural Revision of Cephalosporolide J and Bassianolone," J. Org. Chem. 2014, 79, 1493-1497, DOI: 10.1021/jo402602h.

InChIs

1: InChI=1S/C10H16O5/c1-6-2-3-7(11)4-8(12)9(13)5-10(14)15-6/h6,8-9,12-13H,2-5H2,1H3/t6-,8+,9+/m1/s1
InChIKey=JTOYXZZLKBAIEJ-YEPSODPASA-N

2: InChI=1S/C10H16O5/c1-6-2-3-7(11)4-8(12)9(13)5-10(14)15-6/h6,8-9,12-13H,2-5H2,1H3/t6-,8+,9-/m1/s1
InChIKey=JTOYXZZLKBAIEJ-BWVDBABLSA-N

3: InChI=1S/C10H16O5/c1-6(11)2-3-7(12)4-9-8(13)5-10(14)15-9/h6,8-9,11,13H,2-5H2,1H3/t6-,8+,9+/m1/s1
InChIKey=ZTRQRDNTNXPRFW-YEPSODPASA-N

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Metal oxidation states in the oxygen-evolving complex: A computational challenge

V. Krewald, M. Retegan, N. Cox, J. Messinger, W. Lubitz, S. DeBeer, F. Neese and D.A. Pantazis,
Chemical Science 2015, early-view (Open-Access)
Contributed by Marcel Swart

The determination of oxidation states of metals in biological systems is not an easy task and can lead to surprises or controversies. This was for instance shown last year when an unusual molybdenum(III) oxidation state in the nitrogenase enzyme was reported [1], or the year before [2] for a Sc-capped iron-oxygen complex where the crystal structure shows a iron(III) oxidation state unlike the expected iron(IV) state based on solution data [3].
The situation is infinitely more complex when more than one metal ion is present, as is the case in the oxygen evolving complex (OEC) of photosystem II that is studied here through a combination of computational chemistry and spectroscopy. The OEC contains 4 manganese and 1 calcium as the active species to convert water into oxygen. Many studies have been performed on the five stages along the catalytic cycle, often with contradictory conclusions about the geometry, electronic structure, oxidation and protonation states involved.

Summary of the catalytic cycle with the five stages (reproduced with permission from Chem. Sci., 2015, Advance Article, DOI: 10.1039/C4SC03720K; Published by The Royal Society of Chemistry) 

A breakthrough was a recent atomic resolution crystal structure [4] that largely confirmed the theoretical predictions by Siegbahn [5] about the geometrical positioning of the manganese ions and the oxygens. Still, a detailed understanding of the oxidation states was lacking, which is being explored in the paper by Pantazis and co-workers through a combination of a variety of experimental and theoretical techniques.

Typical model system used in the calculations (reproduced with permission from Chem. Sci., 2015, Advance Article, DOI: 10.1039/C4SC03720K; Published by The Royal Society of Chemistry) 

It is this combination of both theory and experiment, and the systematic exploration of protonation states, open- vs. closed cubane structure, distribution of +3/+4 oxidation states over the different manganese ions that makes this a complete and convincing story for the assignment of high-valent (HV) oxidation states along the catalytic cycle.

One example of the different possible distributions of oxidation states (III or IV) and spin states (1/2, 5/2 or 13/2), for state 2 models (reproduced with permission from Chem. Sci., 2015, Advance Article, DOI: 10.1039/C4SC03720K; Published by The Royal Society of Chemistry) 

The HV assignments made are consistent with the experimental data obtained so far, including a very recent “radiation-damage-free” crystal structure [6], for the first three stages of the catalytic cycle (S1-S3). What remains to be explored is how the enzyme produces the dioxygen molecule in the S4 stage and returns back to the resting state S0. Without any doubt, further unexpected findings will be coming along for this intriguing catalytic cycle.

[1] Chem. Sci. 2014, 5, 3096-3103, DOI: 10.1039/C4SC00337C 
[2] Chem. Commun. 2013, 49, 6650-6652, DOI: 10.1039/c3cc42200c
[3] Nature Chem. 2010, 2, 756-759, DOI: 10.1038/nchem.731
[4] Nature 2011, 473, 55-60, DOI: 10.1038/nature09913
[5] Acc. Chem. Res. 2009, 42, 1871-1880, DOI: 10.1021/ar900117k
[6] Nature 2015, 517, 99-103, DOI: 10.1038/nature13991


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Effect of Anharmonicity on Adsorption Thermodynamics

GiovanniMaria Piccini and Joachim Sauer Journal of Chemical Theory and Computation 2014, 10, 2476.

Contributed by +Jan Jensen 

Low frequency modes can make a sizable contribution to the binding free energy computed using the harmonic oscillator approximation (HO), but is even appropriate to use the HO-expression for the free energy for these modes.  In a paper I highlighted previously, Kjærgaard and co-workers showed that the HO gave good agreement with the experimental binding free energy for acetonitrile-HCl dimer even though the lowest frequency mode was a mere 38 cm$^{-1}$.

However, in this study Piccini and Sauer show that anharmonic effects need to be included to obtain agreement with the experimental binding free energy of methane to H-CHA zeolite.  Specifically, Piccini and Sauer compute the vibrational binding free energy by computing the 1-dimensional (1D) PES for each low frequency mode and compute the vibrational energy levels and corresponding partition function numerically using the method proposed by Beste (as opposed to using the anharmonic fundamental frequency together with the HO partition function).  This decreases the binding free energy by 2.5 kcal/mol compared to the standard HO treatment.  

Actually, Piccini and Sauer show that the usual double difference displacement in Cartesian coordinates can result in errors for low frequency modes and if harmonic frequencies are extracted from the 1D PESs the difference between harmonic and anharmonic binding free energies increase to 3.0 kcal/mol.  (Of course if one has already computed the 1D PESs one might as well compute the anharmonic free energy contribution).

As Piccini and Sauer point out it is crucial to use internal coordinates when displacing the molecule along the normal mode to minimize unphysical distortions in bond lengths and bond angles. The choice of internal coordinates can affect the results and must be chosen carefully.

One note on the general methodology (described in more detail here): I believe the normal mode optimization reduces to a standard RFO quasi Newton-Raphson optimization if all modes are optimized, but using a transformed gradient rather than the Cartesian gradient to check convergence. So if one is using such an optimizer to begin with (rather than e.g. conjugate gradient) implementing the normal mode optimization is not necessary or requires merely a change in convergence criteria.    


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A guide to small-molecule structure assignment through computation of (1H and 13C) NMR chemical shifts

Willoughby, P. H.; Jansma, M. J.; Hoye, T. R. Nat. Protocols 2014, 9, 643-660
Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission

I have posted on the use of computed NMR chemical shifts and coupling constants to help aid in structure identification. The second edition of my book Computational Organic Chemistry has a largely all-new chapter on structure identification aided by computed spectra, especially NMR spectra. In my recent opinion piece speculating on challenges in computational organic chemistry,¹ the first area I highlight is encouraging the larger use of computed spectra as an essential component of structure determination.

While more and more non-traditional computational users are employing quantum computations towards these problems, I suspect that many non-users are a bit wary about stepping into an arena they are not expert in, an arena chock-filled with acronyms and methods and potentially little guidance. While some very nice papers^2-6 and web sites (Chemical Shift Repository (Cheshire) and DP4) do outline procedures for using computations in this fashion, they are not truly designed for the non-specialist.

Well, fear not any longer. Hoye and coworkers, synthetic chemists who have utilized computational approaches in structure determinations for a number of years, have written a detailed step-by-step protocol for using a standard computational approach towards structure determination.⁷ The article is written with the synthetic chemist in mind, and includes a number of scripts to automate many of the steps.

For the specialist, the overall outline of the protocol is fairly routine:

1. Utilize MacroModel to perform a conformational search for each proposed structure, retaining the geometries within 5 kcal mol^-1 of the global minimum.
2. Optimize these conformations for each structure at M06-2x/6-31+G(d).
3. For each conformation of each structure, compute the ¹H and ¹³C chemical shifts, scale them, and determine the Boltzmann weighted chemical shifts
4. Compare these chemical shifts with the experimental values using Mean Absolute Error

The article is straightforward and easily guides the novice user through these steps. Anyone unsure of how to utilize quantum chemical computations in structure determination is well advised to start with this article.

References

(1) Bachrach, S. M. "Challenges in computational organic chemistry," WIRES: Comput. Mol. Sci. 2014, 4, 482-487, DOI: 10.1002/wcms.1185.

(2) Lodewyk, M. W.; Siebert, M. R.; Tantillo, D. J. "Computational Prediction of ¹H and ¹³C Chemical Shifts: A Useful Tool for Natural Product, Mechanistic, and Synthetic Organic Chemistry," Chem. Rev. 2012, 112, 1839–1862, DOI: href="http://dx.doi.org/10.1021/cr200106v">10.1021/cr200106v.

(3) Bally, T.; Rablen, P. R. "Quantum-Chemical Simulation of ¹H NMR Spectra. 2. Comparison of DFT-Based Procedures for Computing Proton-Proton Coupling Constants in Organic Molecules," J. Org. Chem. 2011,76, 4818-4830, DOI: 10.1021/jo200513q.>

(4) Jain, R.; Bally, T.; Rablen, P. R. "Calculating Accurate Proton Chemical Shifts of Organic Molecules with Density Functional Methods and Modest Basis Sets," J. Org. Chem. 2009, 74, 4017-4023, DOI:10.1021/jo900482q.

(5) Smith, S. G.; Goodman, J. M. "Assigning the Stereochemistry of Pairs of Diastereoisomers Using GIAO NMR Shift Calculation," J. Org. Chem. 2009, 74, 4597-4607, DOI: 10.1021/jo900408d.

(6) Smith, S. G.; Goodman, J. M. "Assigning Stereochemistry to Single Diastereoisomers by GIAO NMR Calculation: The DP4 Probability," J. Am. Chem. Soc. 2010, 132, 12946-12959, DOI: 10.1021/ja105035r.

(7) Willoughby, P. H.; Jansma, M. J.; Hoye, T. R. "A guide to small-molecule structure assignment through computation of (1H and 13C) NMR chemical shifts," Nat. Protocols 2014, 9, 643-660, DOI:10.1038/nprot.2014.042.

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Quantum-Mechanical Evaluation of pi-pi versus Substituent-pi Interactions in pi Stacking: Direct evidence for the Wheeler-Houk Picture

Robert M. Parrish and C. David Sherrill J. Am. Chem. Soc., 2014, 136 (50), pp 17386–17389

DOI: 10.1021/ja5101245

In this recent paper, the Sherrill group provides evidence from SAPT (symmetry-adapted perturbation theory) that supports the Wheeler-Houk model of substituent effects on pi-pi stacking.  The 

Wheeler-Houk model posits that substituent effects on binding energies between substituted aromatics arise almost entirely from direct interactions with substituents (and substituent-C bonds).  Although this paper provides evidence that pi-polarization (the Hunter-Sanders model) does contribute, it is found that direct interactions usually dominate.