Application of a new expert system for the structure elucidation of natural products from their 1D and 2D NMR data

Bacteria as a source of biopigments and their potential applications

From the prehistoric period, the utilization of pigments as colouring agents was an integral part of human life. Early people may have utilized paint for aesthetic motives, according to archaeologists. The pigments are either naturally derived or synthesized in the laboratory. Different studies reported that certain synthetic colouring compounds were toxic and had adverse health and environmental effects. Therefore, knowing the drawbacks of these synthetic colouring agents now scientists are attracted towards the harmless natural pigments. The main sources of natural pigments are plants, animals or microorganisms. Out of these natural pigments, microorganisms are the most important source for the production and application of bioactive secondary metabolites. Among all kinds of microorganisms, bacteria have specific benefits due to their short life cycle, low sensitivity to seasonal and climatic variations, ease of scaling, and ability to create pigments of various colours. Based on these physical characteristics, bacterial pigments appear to be a promising sector for novel biotechnological applications, ranging from functional food production to the development of new pharmaceuticals and biomedical therapies. This review summarizes the need for bacterial pigments, biosynthetic pathways of carotenoids and different applications of bacterial pigments.

Four undescribed triterpenes from the aerial parts of Verbena officinalis

Verbena officinalis is used as a Chinese folk medicine for the treatment of rheumatism and bronchitis. Herein, four undescribed triterpenes, officinalisoids A-D (1-4), together with thirty-three known compounds (5-37) were isolated from the aerial parts of V. officinalis. The chemical structures of the new compounds were determined by spectrometric data interpretation using NMR, HRESIMS, IR and UV spectroscopy. Biological evaluation results revealed that compound 30 exhibited potential anti-inflammatory activity with IC50 value of 6.07 μM (CC50 > 50 μM) and compound 12 showed moderate anti-dengue virus activity with the IC50 value of 24.55 μM (CC50 > 50 μM).

ACD/Structure Elucidator: 20 Years in the History of Development

The first methods associated with the Computer-Assisted Structure Elucidation (CASE) of small molecules were published over fifty years ago when spectroscopy and computer science were both in their infancy. The incredible leaps in both areas of technology could not have been envisaged at that time, but both have enabled CASE expert systems to achieve performance levels that in their present state can outperform many scientists in terms of speed to solution. The computer-assisted analysis of enormous matrices of data exemplified 1D and 2D high-resolution NMR spectroscopy datasets can easily solve what just a few years ago would have been deemed to be complex structures. While not a panacea, the application of such tools can provide support to even the most skilled spectroscopist. By this point the structures of a great number of molecular skeletons, including hundreds of complex natural products, have been elucidated using such programs. At this juncture, the expert system ACD/Structure Elucidator is likely the most advanced CASE system available and, being a commercial software product, is installed and used in many organizations. This article will provide an overview of the research and development required to pursue the lofty goals set almost two decades ago to facilitate highly automated approaches to solving complex structures from analytical spectroscopy data, using NMR as the primary data-type.

Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction

A machine learning enhanced spectrum recognition system called spectrum recognition based on computer vision (SRCV) for data extraction from previously analyzed ¹³C and ¹H NMR spectra has been developed. The intelligent system was designed with four function modules to extract data from three areas of NMR images, including ¹³C and ¹H chemical shifts, the integral, and the range of the shift values. During this study, three machine learning models were pretrained for number recognition, which is the key procedure for NMR data extraction. The k nearest neighbor (kNN) method was selected with optimized k (k = 4), which displayed a 100% recognition rate. Subsequently, the performance of SRCV was tested and validated to have high accuracy with a short processing time (11-21 s) for each NMR spectral image. Our spectrum recognizer enables high-throughput ¹³C and ¹H NMR data extraction from abundant spectra in the literature and has the potential to be used for spectral database construction. In addition, the system may be applicable to be developed for data import to computer-assisted structure elucidation systems, which would automate this procedure significantly. SRCV can be accessed in GitHub (https://github.com/WJmodels/SRCV).

Fungi as a Potential Source of Pigments: Harnessing Filamentous Fungi

The growing concern over the harmful effects of synthetic colorants on both the consumer and the environment has raised a strong interest in natural coloring alternatives. As a result the worldwide demand for colorants of natural origin is rapidly increasing in the food, cosmetic and textile sectors. Natural colorants have the capacity to be used for a variety of industrial applications, for instance, as dyes for textile and non-textile substrates such as leather, paper, within paints and coatings, in cosmetics, and in food additives. Currently, pigments and colorants produced through plants and microbes are the primary source exploited by modern industries. Among the other non-conventional sources, filamentous fungi particularly ascomycetous and basidiomycetous fungi (mushrooms), and lichens (symbiotic association of a fungus with a green alga or cyanobacterium) are known to produce an extraordinary range of colors including several chemical classes of pigments such as melanins, azaphilones, flavins, phenazines, and quinines. This review seeks to emphasize the opportunity afforded by pigments naturally found in fungi as a viable green alternative to current sources. This review presents a comprehensive discussion on the capacity of fungal resources such as endophytes, halophytes, and fungi obtained from a range or sources such as soil, sediments, mangroves, and marine environments. A key driver of the interest in fungi as a source of pigments stems from environmental factors and discussion here will extend on the advancement of greener extraction techniques used for the extraction of intracellular and extracellular pigments. The search for compounds of interest requires a multidisciplinary approach and techniques such as metabolomics, metabolic engineering and biotechnological approaches that have potential to deal with various challenges faced by pigment industry.

Quantum isomer search

Isomer search or molecule enumeration refers to the problem of finding all the isomers for a given molecule. Many classical search methods have been developed in order to tackle this problem. However, the availability of quantum computing architectures has given us the opportunity to address this problem with new (quantum) techniques. This paper describes a quantum isomer search procedure for determining all the structural isomers of alkanes. We first formulate the structural isomer search problem as a quadratic unconstrained binary optimization (QUBO) problem. The QUBO formulation is for general use on either annealing or gate-based quantum computers. We use the D-Wave quantum annealer to enumerate all structural isomers of all alkanes with fewer carbon atoms (n < 10) than Decane (C10H22). The number of isomer solutions increases with the number of carbon atoms. We find that the sampling time needed to identify all solutions scales linearly with the number of carbon atoms in the alkane. We probe the problem further by employing reverse annealing as well as a perturbed QUBO Hamiltonian and find that the combination of these two methods significantly reduces the number of samples required to find all isomers.

Computational methods for NMR and MS for structure elucidation I: software for basic NMR

Structure elucidation is an important and sometimes time-consuming step for natural products research. This step has evolved in the past few years to a faster and more automated process due to the development of several computational programs and analytical techniques. In this paper, the topics of NMR prediction and CASE programs are addressed. Furthermore, the elucidation of natural peptides is discussed.

Automatic Structure Elucidation through Data Base Search and 2D NMR Spectral Analysis

This work shows how two expert systems, LSD and SISTEMAT, can be used together to solve structure elucidation problems that were selected from recent literature articles. The LSD system is a structure generator that mainly relies on homo- and heteronuclear 2D NMR data. It lacks the knowledge of chemical shift values and of natural product chemistry. Conversely, the SISTEMAT data base contains about 20000 natural compounds and refers to both their 13C NMR chemical shifts and their botanical origin. When exploited by dedicated computer programs it yields structural constraints such as skeletal types and ring systems. The botanical and spectroscopic data in SISTEMAT proved to be very complementary in the constraints extraction process. Several application examples of the proposed methodology are described in detail.

Microbial Pigments in the Food Industry-Challenges and the Way Forward

Developing new colors for the food industry is challenging, as colorants need to be compatible with a food flavors, safety, and nutritional value, and which ultimately have a minimal impact on the price of the product. In addition, food colorants should preferably be natural rather than synthetic compounds. Micro-organisms already produce industrially useful natural colorants such as carotenoids and anthocyanins. Microbial food colorants can be produced at scale at relatively low costs. This review highlights the significance of color in the food industry, why there is a need to shift to natural food colors compared to synthetic ones and how using microbial pigments as food colorants, instead of colors from other natural sources, is a preferable option. We also summarize the microbial derived food colorants currently used and discuss their classification based on their chemical structure. Finally, we discuss the challenges faced by the use and development of food grade microbial pigments and how to deal with these challenges, using advanced techniques including metabolic engineering and nanotechnology.

The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products

Computer-assisted structure elucidation can help to determine the structures of complex natural products while minimizing the risk of structure errors.

Cannabinomimetric Lipids: From Natural Extract to Artificial Synthesis

Endocannabinoid system is related with various physiological and cognitive processes including fertility, pregnancy, during pre- and postnatal development, pain-sensation, mood, appetite, and memory. In the latest decades, an important milestone concerning the endocannabinoid system was the discovery of the existence of the cannabinoid receptors CB1 and CB2. Anandamide was the first reported endogenous metabolite, which adjusted the release of some neurotransmitters through binding to the CB1 or CB2 receptors. Then a series of cannabinomimetric lipids were extracted from marine organisms, which possessed similar structure with anandamide. This review will provide a short account about cannabinomimetric lipids for their extraction and synthesis.

Autonomous driving in NMR

The automatic analysis of NMR data has been a much-desired endeavour for the last six decades, as it is the case with any other analytical technique. This need for automation has only grown as advances in hardware; pulse sequences and automation have opened new research areas to NMR and increased the throughput of data. Full automatic analysis is a worthy, albeit hard, challenge, but in a world of artificial intelligence, instant communication and big data, it seems that this particular fight is happening with only one technique at a time (let this be NMR, MS, IR, UV or any other), when the reality of most laboratories is that there are several types of analytical instrumentation present. Data aggregation, verification and elucidation by using complementary techniques (e.g. MS and NMR) is a desirable outcome to pursue, although a time-consuming one if performed manually; hence, the use of automation to perform the heavy lifting for users is required to make the approach attractive for scientists. Many of the decisions and workflows that could be implemented under automation will depend on the two-way communication with databases that understand analytical data, because it is desirable not only to query these databases but also to grow them in as much of an automatic manner as possible. How these databases are designed, set up and the data inside classified will determine what workflows can be implemented. Copyright © 2016 John Wiley & Sons, Ltd.

Dereplication: racing to speed up the natural products discovery process

Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.

Blind trials of computer-assisted structure elucidation software

Background

One of the largest challenges in chemistry today remains that of efficiently mining through vast amounts of data in order to elucidate the chemical structure for an unknown compound. The elucidated candidate compound must be fully consistent with the data and any other competing candidates efficiently eliminated without doubt by using additional data if necessary. It has become increasingly necessary to incorporate an in silico structure generation and verification tool to facilitate this elucidation process. An effective structure elucidation software technology aims to mimic the skills of a human in interpreting the complex nature of spectral data while producing a solution within a reasonable amount of time. This type of software is known as computer-assisted structure elucidation or CASE software. A systematic trial of the ACD/Structure Elucidator CASE software was conducted over an extended period of time by analysing a set of single and double-blind trials submitted by a global audience of scientists. The purpose of the blind trials was to reduce subjective bias. Double-blind trials comprised of data where the candidate compound was unknown to both the submitting scientist and the analyst. The level of expertise of the submitting scientist ranged from novice to expert structure elucidation specialists with experience in pharmaceutical, industrial, government and academic environments.

Results

Beginning in 2003, and for the following nine years, the algorithms and software technology contained within ACD/Structure Elucidator have been tested against 112 data sets; many of these were unique challenges. Of these challenges 9% were double-blind trials. The results of eighteen of the single-blind trials were investigated in detail and included problems of a diverse nature with many of the specific challenges associated with algorithmic structure elucidation such as deficiency in protons, structure symmetry, a large number of heteroatoms and poor quality spectral data.

Conclusion

When applied to a complex set of blind trials, ACD/Structure Elucidator was shown to be a very useful tool in advancing the computer's contribution to elucidating a candidate structure from a set of spectral data (NMR and MS) for an unknown. The synergistic interaction between humans and computers can be highly beneficial in terms of less biased approaches to elucidation as well as dramatic improvements in speed and throughput. In those cases where multiple candidate structures exist, ACD/Structure Elucidator is equipped to validate the correct structure and eliminate inconsistent candidates. Full elucidation can generally be performed in less than two hours; this includes the average spectral data processing time and data input.

Computer-assisted methods for molecular structure elucidation: realizing a spectroscopist's dream

BACKGROUND

This article coincides with the 40 year anniversary of the first published works devoted to the creation of algorithms for computer-aided structure elucidation (CASE). The general principles on which CASE methods are based will be reviewed and the present state of the art in this field will be described using, as an example, the expert system Structure Elucidator.

RESULTS

The developers of CASE systems have been forced to overcome many obstacles hindering the development of a software application capable of drastically reducing the time and effort required to determine the structures of newly isolated organic compounds. Large complex molecules of up to 100 or more skeletal atoms with topological peculiarity can be quickly identified using the expert system Structure Elucidator based on spectral data. Logical analysis of 2D NMR data frequently allows for the detection of the presence of COSY and HMBC correlations of "nonstandard" length. Fuzzy structure generation provides a possibility to obtain the correct solution even in those cases when an unknown number of nonstandard correlations of unknown length are present in the spectra. The relative stereochemistry of big rigid molecules containing many stereocenters can be determined using the StrucEluc system and NOESY/ROESY 2D NMR data for this purpose.

CONCLUSION

The StrucEluc system continues to be developed in order to expand the general applicability, provide improved workflows, usability of the system and increased reliability of the results. It is expected that expert systems similar to that described in this paper will receive increasing acceptance in the next decade and will ultimately be integrated directly to analytical instruments for the purpose of organic analysis. Work in this direction is in progress. In spite of the fact that many difficulties have already been overcome to deliver on the spectroscopist's dream of "fully automated structure elucidation" there is still work to do. Nevertheless, as the efficiency of expert systems is enhanced the solution of increasingly complex structural problems will be achievable.

Are Deterministic Expert Systems for Computer-Assisted Structure Elucidation Obsolete?

Expert systems for spectroscopic molecular structure elucidation have been developed since the mid-1960s. Algorithms associated with the structure generation process within these systems are deterministic; that is, they are based on graph theory and combinatorial analysis. A series of expert systems utilizing 2D NMR spectra have been described in the literature and are capable of determining the molecular structures of large organic molecules including complex natural products. Recently, an opinion was expressed in the literature that these systems would fail when elucidating structures containing more than 30 heavy atoms. A suggestion was put forward that stochastic algorithms for structure generation would be necessary to overcome this shortcoming. In this article, we describe a comprehensive investigation of the capabilities of the deterministic expert system Structure Elucidator. The results of performing the structure elucidation of 250 complex natural products with this program were studied and generalized. The conclusion is that 2D NMR deterministic expert systems are certainly capable of elucidating large structures (up to about 100 heavy atoms) and can deal with the complexities associated with both poor and contradictory spectral data.

Automated structure verification based on 1H NMR prediction

A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification--the confirmation of a postulated structure--is now possible, as opposed to Elucidation-the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum-structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human.

Phenotypic taxonomy and metabolite profiling in microbial drug discovery

Microorganisms and in particular actinomycetes and microfungi are known to produce a vast number of bioactive secondary metabolites. For industrially important fungal genera such as Penicillium and Aspergillus the production of these compounds has been demonstrated to be very consistent at the species level. This means that direct metabolite profiling techniques such as direct injection mass spectrometry or NMR can easily be used for chemotyping/metabolomics of strains from both culture collections and natural samples using modern informatics tools. In this review we discuss chemotyping/metabolomics as part of intelligent screening and highlight how it can be used for identification and classification of filamentous fungi and for the discovery of novel compounds when used in combination with modern methods for dereplication. In our opinion such approaches will be important for future effective drug discovery strategies, especially for dereplication of culture collections in order to avoid redundancy in the selection of species. This will maximize the chemical diversity of the microbial natural product libraries that can be generated from fungal collections.

Exploring fungal biodiversity for the production of water-soluble pigments as potential natural food colorants

The production of many currently authorized natural food colorants has a number of disadvantages, including a dependence on the supply of raw materials and variations in pigment extraction. Fungi provide a readily available alternative source of naturally derived food colorants that could easily be produced in high yields. The recent authorization of a fungal food colorant has fuelled research to explore the extraordinary chemical diversity and biodiversity of fungi for the biotechnological production of pigments as natural food colorants. These studies require an appropriate use of chemotaxonomic tools and a priori knowledge of fungal metabolites to carry out intelligent screening for known or novel colorants as lead compounds. Such screening would result in the preselection of some potential pigment producers and the deselection of pathogenic strains and toxin producers. With advances in gene technology, in the future it should be possible to employ metabolic engineering to create microbial cell factories for the production of food colorants.

Evolutionary-algorithm-based strategy for computer-assisted structure elucidation

An evolutionary algorithm (EA) using a graph-based data structure to explore the molecular constitution space is presented. The EA implementation proves to be a promising alternative to deterministic approaches to the problem of computer-assisted structure elucidation (CASE). While not relying on any external database, the EA-guided CASE program SENECA is able to find correct solutions within calculation times comparable to that of other CASE expert systems. The implementation presented here significantly expands the size limit of constitutional optimization problems treatable with evolutionary algorithms by introducing novel efficient graph-based genetic operators. The new EA-based search strategy is discussed including the underlying data structures, component design, parameter optimization, and evolution process control. Typical structure elucidation examples are given to demonstrate the algorithm's performance.

Structure Elucidation from 2D NMR Spectra Using theStrucElucExpert System: Detection and Removal of Contradictions in the Data

The elucidation of chemical structures from 2D NMR data commonly utilizes a combination of COSY, HMQC/HSQC, and HMBC data. Generally COSY connectivities are assumed to mostly describe the separation of protons that are separated by 1 skeletal bond (3JHH), while HMBC connectivities represent protons separated from carbon atoms by 1 to 2 skeletal bonds (2JCH and 3JCH). Obviously COSY and HMBC connectivities of lengths greater than those described have been detected. Though experimental techniques have recently been described to aid in the identification of the nature of the couplings the detection of whether a coupling is 2-bond or greater still remains a challenge in most laboratories. In the StrucEluc software system the common lengths of the connectivities, 1-bond for COSY and 1- or 2-bond for HMBC, derived from 2D NMR data are set as the default. Therefore, in the presence of any extended connectivities contradictions can appear in the 2D NMR data. In this article, algorithmic methods for the detection and removal of contradictions in 2D NMR data that have been developed in support of StrucEluc are described. The methods are based on the analysis of molecular connectivity diagrams, MCDs. These methods have been implemented in the StrucEluc system and tested by solving 50 structural problems with 2D NMR spectral data containing contradictions. The presence of contradictions was detected by the algorithm in 90% of the cases, and the contradictions were automatically removed in approximately 50% of the problems. A method of "fuzzy" structure generation in the presence of contradictions has been suggested and successfully tested in this work. This work will demonstrate examples of the application of developed methods to a number of structural problems.

HOUDINI: a new approach to computer-based structure generation

A new method of structure generation called convergent structure generation has been developed to address limitations of earlier methods. The features of the program (HOUDINI) based on this method include the following: a single integrated representation of the collective substructural information; the use of parallel atom groups for efficient processing of families of alternative substructural inferences; and a managed structure generation procedure designed to build required structural features early in the process.

Structure Elucidator: A Versatile Expert System for Molecular Structure Elucidation from 1D and 2D NMR Data and Molecular Fragments

StrucEluc is an expert system that allows the computer-assisted elucidation of chemical structures based on the inputs of a series of spectral data including 1D and 2D NMR and mass spectra. The system has been enabled to allow a chemist to utilize fragments stored in a fragment database as well as user-defined fragments submitted by the chemist in the structure elucidation process. The association of fragments in this way has been shown to dramatically speed up the process of structure generation from 2D NMR data and has helped to minimize or eliminate the need for user intervention thereby further enabling the vision of automated elucidation. The use of fragments has frequently transformed very difficult 2D NMR elucidation challenges into easily solvable tasks. A strategy to utilize molecular fragments has been developed and optimized based on specific challenging examples. This strategy will be described here using real world examples. Experience gained by solving more than 150 structure elucidation problems from a variety of literature sources is also reviewed in this work.

Flow NMR applications in combinatorial chemistry

Flow NMR techniques are now well accepted and widely used in many areas of drug discovery. Although natural-product-, rational-drug-design-, and NMR-screening-programs have begun to use flow NMR more routinely, flow NMR has not yet gained widespread acceptance in combinatorial chemistry, even though it has been shown to be a potentially useful tool. Recent developments in DI-NMR, FIA-NMR, and LC-NMR will help flow NMR eventually gain a wider acceptance within combinatorial chemistry. These developments include LC-NMR-MS instrumentation, flow probe improvements, new pulse sequences, improved automation of NMR data analysis, and the application of flow NMR to related fields in drug discovery.