Study of heavy atom influence on poly-halogenated compounds using DP4/MM-DP4+/DP4+: insights and trends.

Nuclear magnetic resonance (NMR) spectroscopy complemented by density functional theory (DFT) calculations is a crucial tool for structural elucidation. Nevertheless, the precision of NMR predictions is influenced by the 'heavy atom effect', wherein heavy atoms affect the shielding values of neighboring light atoms (HALA effect). Standard practice in the field involves removing the conflicting signals. However, in the case of polyhalogenated molecules, this is challenging due to the significant amount of information that ends up being lost. In this study the HALA is thoroughly investigated in the context of three leading probability methods: DP4, MM-DP4+, and DP4+. The results show that DP4+ is more sensitive to C-Cl or C-Br signals, which is a consequence of the longer bond lengths computed with DFT. Removing conflicting signals is highly effective in DP4+, but has an uncertain outcome in methods based on molecular mechanics geometries, such as DP4 and MM-DP4+. A detailed investigation of the effect of bond distance on the corresponding chemical shifts has also been conducted.

DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation.

DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in certain cases. In this work, we made a substantial improvement to these limitations. First, we deeply explored the effect of molecular mechanics architecture on the DP4+ formalism (MM-DP4+). In addition, a Python applet (DP4+App) was developed to automate the entire process, requiring only the Gaussian NMR output files and a spreadsheet containing the experimental NMR data and labels. The script is designed to use the statistical parameters from the original 24 levels of theory (employing B3LYP/6-31G* geometries) and the new 36 levels explored in this work (over MMFF geometries). Furthermore, it enables the development of customizable methods using any desired level of theory, allowing for a free choice of test molecules.

Breaking the DFT Energy Bias Caused by Intramolecular Hydrogen-Bonding Interactions with MESSI, A Structural Elucidation Method Inspired by Wisdom of Crowd Theory.

The use of quantum-based NMR methods to complement and guide the connectivity and stereochemical assignment of natural and unnatural products has grown enormously. One of the unsolved problems is related to the improper calculation of the conformational landscape of flexible molecules that have functional groups capable of generating a complex network of intramolecular H-bonding (IHB) interactions. Here the authors present MESSI (Multi-Ensemble Strategy for Structural Identification), a method inspired by the wisdom of the crowd theory that breaks with the traditional mono-ensemble approach. By including independent mappings of selected artificially manipulated ensembles, MESSI greatly improves the sense of the assignment by neutralizing potential energy biases.

A critical review on the use of DP4+ in the structural elucidation of natural products: the good, the bad and the ugly. A practical guide.

Covering: 2015 up to the end of 2020Even in the golden age of NMR, the number of natural products being incorrectly assigned is becoming larger every day. The use of quantum NMR calculations coupled with sophisticated data analysis provides ideal complementary tools to facilitate the elucidation process in challenging cases. Among the current computational methodologies to perform this task, the DP4+ probability is a popular and widely used method. This updated version of Goodman's DP4 synergistically combines NMR calculations at higher levels of theory with the Bayesian analysis of both scaled and unscaled data. Since its publication in late 2015, the use of DP4+ to solve controversial natural products has substantially grown, with several predictions being confirmed by total synthesis. To date, the structures of more than 200 natural products were determined with the aid of DP4+. However, all that glitters is not gold. Besides its intrinsic limitations, on many occasions it has been improperly used with potentially important consequences on the quality of the assignment. Herein we present a critical revision on how the scientific community has been using DP4+, exploring the strengths of the method and how to obtain optimal results from it. We also analyze the weaknesses of DP4+, and the paths to by-pass them to maximize the confidence in the structural elucidation.

The Risks of Automation: A Study on DFT Energy Miscalculations and Its Consequences in NMR-based Structural Elucidation.

Managing and processing hundreds or thousands of files to perform an in silico structural assignment can be tedious work. Recent years have witnessed a booming need for automation to facilitate the task. In this work, we explore the repercussions of the energy mismatch made by a popular script to streamline the process. The results led us to question the risks of automation and the sensitivity toward energy miscalculations.

Redefining the Impact of Boltzmann Analysis in the Stereochemical Assignment of Polar and Flexible Molecules by NMR Calculations.

The in silico assignment of polyhydroxylated compounds represents a major challenge given the thus far unsolved problem of inappropriate description of the conformational amplitudes. Herein, we report a conceptually novel stochastic approach based on the creation and evaluation of random artificial ensembles, which could provide a new paradigm for computing NMR properties of flexible molecules. The strategy was successfully tested under the DP4/DP4+ platforms using a large set of compounds belonging to the hyacinthacine family.

General Quantum-Based NMR Method for the Assignment of Absolute Configuration by Single or Double Derivatization: Scope and Limitations.

The determination of the absolute configuration of chiral alcohols and amines is typically carried out with modified Mosher methods involving a double-derivatization strategy. On the other hand, the number of robust and reliable methods to accomplish that goal using a single derivatization approach is much less abundant and mainly limited to secondary alcohols or primary amines. Herein, we report a conceptually novel strategy to settle the most likely absolute configuration of a wide variety of substrates and chiral derivatizing agents following a single-derivatization experiment coupled with quantum calculations of NMR shifts and DP4+ analysis. Using an ambitious set of 114 examples, our methodology succeeded in setting the correct absolute configuration of the substrates in 96% of the cases. The classification achieved with secondary alcohols, secondary amines, and primary amines herein studied was excellent (100%), whereas more modest results (89%) were observed for primary and tertiary alcohols. Moreover, a new DP4+ integrated probability was built to strengthen the analysis when the NMR data of the two possible diastereoisomers are available. The suitability of these methods in solving the absolute configuration of two relevant cases of stereochemical misassignment ((+)- erythro-mefloquine and angiopterlactone B) is also provided.

Determination of the Relative Configuration of Terminal and Spiroepoxides by Computational Methods. Advantages of the Inclusion of Unscaled Data.

The assignment of the relative configuration of spiroepoxides or related quaternary carbon-containing oxiranes can be troublesome and difficult to achieve. The use of GIAO NMR shift calculations can provide helpful assistance in challenging cases of structural elucidation. In this regard, the DP4 probability is one of the most popular methods to be employed when only one set of experimental data is available, though modest results were obtained when dealing with spiroepoxides. Recently, we introduced an improved probability (DP4+) that includes the use of both scaled and unscaled NMR data computed at higher levels of theory. Here, we report a comprehensive study to explore the scope and limitations of the DP4+ methodology in the stereoassignment of terminal or spiroepoxides bearing a wide variety of molecular complexity and conformational freedom. The excellent levels of correct classification achieved were interpreted on the basis of a constructive compensation of errors upon using both scaled and unscaled proton and carbon data. The advantages of the DP4+ methodology in solving two case studies that could not be unequivocally assigned by NOE experiments are also provided.

Beyond DP4: an Improved Probability for the Stereochemical Assignment of Isomeric Compounds using Quantum Chemical Calculations of NMR Shifts.

The DP4 probability is one of the most sophisticated and popular approaches for the stereochemical assignment of organic molecules using GIAO NMR chemical shift calculations when only one set of experimental data is available. In order to improve the performance of the method, we have developed a modified probability (DP4+), whose main differences from the original DP4 are the inclusion of unscaled data and the use of higher levels of theory for the NMR calculation procedure. With these modifications, a significant improvement in the overall performance was achieved, providing accurate and confident results in establishing the stereochemistry of 48 challenging isomeric compounds.

GIAO C-H COSY Simulations Merged with Artificial Neural Networks Pattern Recognition Analysis. Pushing the Structural Validation a Step Forward.

The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.

Efficient production of the flavoring agent zingerone and of both (R)- and (S)-zingerols via green fungal biocatalysis. Comparative antifungal activities between enantiomers.

Zingerone (1) and both chiral forms of zingerol (2) were obtained from dehydrozingerone (3) by biotransformation with filamentous fungi. The bioconversion of 3 with A. fumigatus, G. candidum or R. oryzae allowed the production of 1 as the sole product at 8 h and in 81%-90% at 72 h. In turn, A. flavus, A. niger, C. echinulata, M. circinelloides and P. citrinum produced 1 at 8 h, but at 72 h alcohol 2 was obtained as the major product (74%-99%). Among them, A. niger and M. circinelloides led to the anti-Prelog zingerol (R)-2 in only one step with high conversion rates and ee. Instead, C. echinulata and P. citrinum allowed to obtain (S)-2 in only one step, with high conversion rates and ee. Both chiral forms of 2 were tested for antifungal properties against a panel of clinically important fungi, showing that (R)-, but not (S)-2 possessed antifungal activity.