MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.     

Ligand-based virtual screening by novelty detection with self-organizing maps

We describe a novel method for ligand-based virtual screening, based on utilizing Self-Organizing Maps (SOM) as a novelty detection device. Novelty detection (or one-class classification) refers to the attempt of identifying patterns that do not belong to the space covered by a given data set. In ligand-based virtual screening, chemical structures perceived as novel lie outside the known activity space and can therefore be discarded from further investigation. In this context, the concept of "novel structure" refers to a compound, which is unlikely to share the activity of the query structures. Compounds not perceived as "novel" are suspected to share the activity of the query structures. Nowadays, various databases contain active structures but access to compounds which have been found to be inactive in a biological assay is limited. This work addresses this problem via novelty detection, which does not require proven inactive compounds. The structures are described by spatial autocorrelation functions weighted by atomic physicochemical properties. Different methods for selecting a subset of targets from a larger set are discussed. A comparison with similarity search based on Daylight fingerprints followed by data fusion is presented. The two methods complement each other to a large extent. In a retrospective screening of the WOMBAT database novelty detection with SOM gave enrichment factors between 105 and 462-an improvement over the similarity search based on Daylight fingerprints between 25% and 100%, when the 100 top ranked structures were considered. Novelty detection with SOM is applicable (1) to improve the retrieval of potentially active compounds also in concert with other virtual screening methods; (2) as a library design tool for discarding a large number of compounds, which are unlikely to possess a given biological activity; and (3) for selecting a small number of potentially active compounds from a large data set.     

毛细管电泳结合高效液相色谱-质谱用于天然产物活性成分的筛选

发展了毛细管电泳(CE)和高效液相色谱-质谱(HPLC-MS)相结合的用于天然产物中活性成分筛选和鉴定的方法。该方法中,用HPLC半制备柱对天然产物粗提物进行分离纯化,再用CE对HPLC纯化后的组分进行活性测试。根据HPLC-MS/MS提供的二级质谱数据,即可确定活性成分的化学结构。以乙酰胆碱酯酶为实验模型,对我们发展的筛选方法进行了验证。从黄连粗提物中确定了药根碱、巴马汀等7种活性成分,并通过CE测定了它们的半抑制率(IC₅₀)值。与传统的天然产物分离纯化和活性筛选方法相比,该方法具有简单、微量、快速、准确的优点。本文建立的方法为天然产物粗提物中活性成分的筛选提供了新技术。     

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine_B site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.     

Surface Faceting and Reconstruction of Ceria Nanoparticles

The surface atomic arrangement of metal oxides determines their physical and chemical properties, and the ability to control and optimize structural parameters is of crucial importance for many applications, in particular in heterogeneous catalysis and photocatalysis. Whereas the structures of macroscopic single crystals can be determined with established methods, for nanoparticles (NPs), this is a challenging task. Herein, we describe the use of CO as a probe molecule to determine the structure of the surfaces exposed by rod‐shaped ceria NPs. After calibrating the CO stretching frequencies using results obtained for different ceria single‐crystal surfaces, we found that the rod‐shaped NPs actually restructure and expose {111} nanofacets. This finding has important consequences for understanding the controversial surface chemistry of these catalytically highly active ceria NPs and paves the way for the predictive, rational design of catalytic materials at the nanoscale.     

Compound set enrichment: a novel approach to analysis of primary HTS data

The main goal of high-throughput screening (HTS) is to identify active chemical series rather than just individual active compounds. In light of this goal, a new method (called compound set enrichment) to identify active chemical series from primary screening data is proposed. The method employs the scaffold tree compound classification in conjunction with the Kolmogorov-Smirnov statistic to assess the overall activity of a compound scaffold. The application of this method to seven PubChem data sets (containing between 9389 and 263679 molecules) is presented, and the ability of this method to identify compound classes with only weakly active compounds (potentially latent hits) is demonstrated. The analysis presented here shows how methods based on an activity cutoff can distort activity information, leading to the incorrect activity assignment of compound series. These results suggest that this method might have utility in the rational selection of active classes of compounds (and not just individual active compounds) for followup and validation.     

Antioxidant activity assays on-line with liquid chromatography

Screening for antioxidants requires simple in vitro model systems to investigate antioxidant activity. High resolution screening (HRS), combining a separation technique like HPLC with fast post-column (bio)chemical detection can rapidly pinpoint active compounds in complex mixtures. In this paper both electrochemical and chemistry-based assays are reviewed and discussed. The focus is on the mechanisms involved and differences between the assays, rather than on the matrix or analytes. With 45 applications high resolution antioxidant screening has now become an almost routine tool for the rapid identification of antioxidants in plant extracts, foods and beverages. The methods based on true reactive oxygen species (ROS) provide the most realistic measure of antioxidant activity. Unfortunately these methods are difficult to set up and control and have not been applied since they were reported. The methods based on electrochemical detection are more practical, but have still received only limited attention for practical screening purposes. The methods based on a single relatively stable reagent such as DPPH and ABTS(+) have become most popular, because of their simple set-up and ease of control. The methods have been combined with on-line DAD, MS and NMR detection for rapid identification of active constituents.     

杯菊中的倍半萜内酯

摘要杯菊(Cyathocline purpurea)是云南民间民族药, 全株用于治疗各种炎症和肺结核. 采用L1210细胞系对杯菊的粗提物、 分步萃取物的成分进行了细胞毒试验; 用柱层色谱法同步对活性组分进行化学成分分离纯化; 通过理化数据测定及波谱分析鉴定单体结构. 杯菊的氯仿及乙酸乙酯萃取物具有细胞毒活性, 其IC50分别为3.5和2.8 μg/mL. 从活性组分中分离出3种成分, 分别鉴定为Santamarin(1)、 9β-乙酰基广木香内酯(9β-acetoxycostunolide, 2)和9β-乙酰基小白菊内酯(9β-acetoxypathenolide, 3), 其IC50分别为0.41, 0.89, 0.59 μg/mL. 这三个化合物都有较强的抗癌活性, 其中化合物3为新化合物.     

Coupling HPLC to on-line, post-column (bio)chemical assays for high-resolution screening of bioactive compounds from complex mixtures

It is challenging to screen and identify bioactive compounds from complex mixtures. We review a recently developed technique that couples high-performance liquid chromatography (HPLC) to on-line, post-column (bio)chemical assays and parallel chemical analysis to screen and identify bioactive compounds from complex mixtures without the need for cumbersome purification and subsequent screening. In this system, HPLC separates complex mixtures and a post-column (bio)chemical assay determines the activity of the individual compounds present in the mixtures. Parallel chemical-detection methods (e.g., diode-array detection, mass spectrometry and nuclear magnetic resonance) identify and quantify the active compounds simultaneously. We focus on relatively widely used on-line, post-column assays for antioxidant screening and less widely used hyphenated systems involving assays based on enzymes and receptors. These strategies have proved to be very useful for rapid profiling and identification of individual active components in mixtures to provide a powerful method for natural product-based drug discovery.     

Matching unknown empirical formulas to chemical structure using LC/MS TOF accurate mass and database searching: example of unknown pesticides on tomato skins

Traditionally, the screening of unknown pesticides in food has been accomplished by GC/MS methods using conventional library searching routines. However, many of the new polar and thermally labile pesticides and their degradates are more readily and easily analyzed by LC/MS methods and no searchable libraries currently exist (with the exception of some user libraries, which are limited). Therefore, there is a need for LC/MS approaches to detect unknown non-target pesticides in food. This report develops an identification scheme using a combination of LC/MS time-of-flight (accurate mass) and LC/MS ion trap MS (MS/MS) with searching of empirical formulas generated through accurate mass and a ChemIndex database or Merck Index database. The approach is different than conventional library searching of fragment ions. The concept here consists of four parts. First is the initial detection of a possible unknown pesticide in actual market-place vegetable extracts (tomato skins) using accurate mass and generating empirical formulas. Second is searching either the Merck Index database on CD (10,000 compounds) or the ChemIndex (77,000 compounds) for possible structures. Third is MS/MS of the unknown pesticide in the tomato-skin extract followed by fragment ion identification using chemical drawing software and comparison with accurate-mass ion fragments. Fourth is the verification with authentic standards, if available. Three examples of unknown, non-target pesticides are shown using a tomato-skin extract from an actual market place sample. Limitations of the approach are discussed including the use of A + 2 isotope signatures, extended databases, lack of authentic standards, and natural product unknowns in food extracts.     

Receptor-based computational screening of compound databases: the main docking-scoring engines

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance with many new and exciting techniques. The number of protein structures and/or chemical ligands is constantly growing, through the use of parallel chemistry, X-ray crystallography, NMR or homology modeling methods and so is the theoretical understanding of protein-ligand interactions. As such, structure-based approaches to drug-design and in silico screening are becoming routine part of most modern lead discovery programs. Prioritization of compound libraries is an extremely important task that aims at the rapid identification of tight-binding ligands and ultimately new therapeutic compounds. These in silico approaches combined with other experimental methods facilitate the design of new medicines to treat cardiovascular, degenerative, infectious, and neoplastic diseases, among others. Here, we review key concepts and specific features of several selected ligand-receptor docking/scoring methods while several other topics pertaining to the field of in silico screening are reviewed in the following articles of this special issue of Current Protein and Peptide Science.     

Construction of a Meroterpenoid-Like Compounds Library Based on Diversity-Enhanced Extracts

The structural diversity of natural products and their derivatives have long contributed to the development of new drugs. However, the difficulty in obtaining compounds bearing skeletally novel structures has recently led to a decline of pharmaceutical research into natural products. This paper reports the construction of a meroterpenoid-like library containing 25 compounds with diverse molecular scaffolds obtained from diversity-enhanced extracts. This method constitutes an approach for increasing the chemical diversity of natural-product-like compounds by combining natural product chemistry and diversity-oriented synthesis. Extensive pharmacological screening of the library revealed promising compounds for anti-osteoporotic and anti-lymphoma/leukemia drugs. This result indicates that the use of diversity-enhanced extracts is an effective methodology for producing chemical libraries for the purpose of drug discovery.     

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

Cell extraction combined with off‐line HPLC for screening active compounds from Coptis chinensis

Cell membrane chromatography is a useful tool for screening active compounds from natural products. As the reason of separation mechanism, traditional cell membrane chromatography could not be used for screening the active compounds absorbed through the cell membrane and influencing the cell signal transduction pathway. In this work, we establish a new method named cell extraction combined with off‐line HPLC for screening the compounds penetrating the cell membrane. This is the first time 3 T3‐L1 adipocyte culture has been combined with HPLC technology. Compared with other cell membrane chromatography methods, there is good resolution and no further analysis by other chromatographic steps is required. On co‐incubating crude extracts of Coptis chinensis with cells and analyzing the compounds extracted by the cells, active compounds such as berberine were detected. Glucose consumption tests showed that berberine could increase glucose consumption by insulin‐resistant 3 T3‐L1 adipocytes. The levels of intracellular berberine correlated with its activity. The results indicate that the developed method could be an alternative method for screening active compounds from natural products. Copyright © 2015 John Wiley & Sons, Ltd.