A Method for the Rapid Discovery of Naturally Occurring Products by Proteins Immobilized on Magnetic Beads and Reverse Affinity Chromatography

A highly efficient screening method for naturally occurring products that bind to a specific target protein was demonstrated by using hVDR magnetic beads. The native ligand 1α,25(OH)₂ VD₃ ( 1 ) was selectively bound by hVDR magnetic beads when present in a mixture of natural compounds. Furthermore, this method was shown to be applicable to the identification of natural products that interact with a specific protein immobilized on the beads from an extract of a natural resource. Two new natural compounds were isolated by this method. This approach will be helpful for the discovery of novel, naturally occurring products that bind to specific target proteins. This method has the further advantages that it can identify the HPLC peak corresponding to the target compound for isolation, as well as provide important UV, CD, or MS profile information.     

Grid‐based molecular footprint comparison method for docking and de novo design: Application to HIVgp41

Scoring functions are a critically important component of computer‐aided screening methods for the identification of lead compounds during early stages of drug discovery. Here, we present a new multigrid implementation of the footprint similarity (FPS) scoring function that was recently developed in our laboratory which has proven useful for identification of compounds which bind to a protein on a per‐residue basis in a way that resembles a known reference. The grid‐based FPS method is much faster than its Cartesian‐space counterpart, which makes it computationally tractable for on‐the‐fly docking, virtual screening, or de novo design. In this work, we establish that: (i) relatively few grids can be used to accurately approximate Cartesian space footprint similarity, (ii) the method yields improved success over the standard DOCK energy function for pose identification across a large test set of experimental co‐crystal structures, for crossdocking, and for database enrichment, and (iii) grid‐based FPS scoring can be used to tailor construction of new molecules to have specific properties, as demonstrated in a series of test cases targeting the viral protein HIVgp41. The method is available in the program DOCK6. © 2013 Wiley Periodicals, Inc.     

Analysis of large screening data sets via adaptively grown phylogenetic-like trees

As the use of high-throughput screening systems becomes more routine in the drug discovery process, there is an increasing need for fast and reliable analysis of the massive amounts of the resulting data. At the forefront of the methods used is data reduction, often assisted by cluster analysis. Activity thresholds reduce the data set under investigation to manageable sizes while clustering enables the detection of natural groups in that reduced subset, thereby revealing families of compounds that exhibit increased activity toward a specific biological target. The above process, designed to handle primarily data sets of sizes much smaller than the ones currently produced by high-throughput screening systems, has become one of the main bottlenecks of the modern drug discovery process. In addition to being fragmented and heavily dependent on human experts, it also ignores all screening information related to compounds with activity less than the threshold chosen and thus, in the best case, can only hope to discover a subset of the knowledge available in the screening data sets. To address the deficiencies of the current screening data analysis process the authors have developed a new method that analyzes thoroughly large screening data sets. In this report we describe in detail this new approach and present its main differences with the methods currently in use. Further, we analyze a well-known, publicly available data set using the proposed method. Our experimental results show that the proposed method can improve significantly both the ease of extraction and amount of knowledge discovered from screening data sets.     

Semi‐supervised kernel partial least squares fault detection and identification approach with application to HGPWLTP

In this paper, fault detection and identification methods based on semi‐supervised Laplacian regularization kernel partial least squares (LRKPLS) are proposed. In Laplacian regularization learning framework, unlabeled and labeled samples are used to improve estimate of data manifold so that one can establish a more robust data model. We show that LRKPLS can avoid the over‐fitting problem which may be caused by sample insufficient and outliers present. Moreover, the proposed LRKPLS approach has no special restriction on data distribution, in other words, it can be used in the case of nonlinear or non‐Gaussian data. On the basis of LRKPLS, corresponding fault detection and identification methods are proposed. Those methods are used to monitor a numerical example and Hot Galvanizing Pickling Waste Liquor Treatment Process (HGPWLTP), and the cases study show effeteness of the proposed approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Multiple ligand detection and affinity measurement by ultrafiltration and mass spectrometry analysis applied to fragment mixture screening

Binding affinity of a small molecule drug candidate to a therapeutically relevant biomolecular target is regarded the first determinant of the candidate's efficacy. Although the ultrafiltration-LC/MS (UF-LC/MS) assay enables efficient ligand discovery for a specific target from a mixed pool of compounds, most previous analysis allowed for relative affinity ranking of different ligands. Moreover, the reliability of affinity measurement for multiple ligands with UF-LC/MS has hardly been strictly evaluated. In this study, we examined the accuracy of K_d determination through UF-LC/MS by comparison with classical ITC measurement. A single-point K_d calculation method was found to be suitable for affinity measurement of multiple ligands bound to the same target when binding competition is minimized. A second workflow based on analysis of the unbound fraction of compounds was then developed, which simplified sample preparation as well as warranted reliable ligand discovery. The new workflow implemented in a fragment mixture screen afforded rapid and sensitive detection of low-affinity ligands selectively bound to the RNA polymerase NS5B of hepatitis C virus. More importantly, ligand identification and affinity measurement for mixture-based fragment screens by UF-LC/MS were in good accordance with single ligand evaluation by conventional SPR analysis. This new approach is expected to become a valuable addition to the arsenal of high-throughput screening techniques for fragment-based drug discovery.     

Solid‐Phase Enrichment and Analysis of Azide‐Labeled Natural Products: Fishing Downstream of Biochemical Pathways

Many methods have been devised over the decades to trace precursors of specific molecules in cellular environments as, for example, in biosynthesis studies. The advent of click chemistry has facilitated the powerful combination of tracing and at the same time sieving the highly complex metabolome for compounds derived from simple or complex starting materials, especially when the click reaction takes place on a solid support. While the principle of solid‐phase click reactions has already been successfully applied for selective protein and peptide enrichment, the successful enrichment of much smaller primary and secondary metabolites, showing great structural diversity and undergoing many different biosynthetic steps, has seen only little development. For bacterial secondary metabolism, a far broader tolerance for “clickable” precursors was observed than in ribosomal proteinogenesis, thus making this method a surprisingly valuable tool for the tracking and discovery of compounds within the cellular biochemical network. The implementation of this method has led to the identification of several new compounds from the bacterial genera Photorhabdus and Xenorhabdus, clearly proving its power.     

Characterization of N‐methylated amino acids by GC‐MS after ethyl chloroformate derivatization

Methylation is an essential metabolic process in the biological systems, and it is significant for several biological reactions in living organisms. Methylated compounds are known to be involved in most of the bodily functions, and some of them serve as biomarkers. Theoretically, all α‐amino acids can be methylated, and it is possible to encounter them in most animal/plant samples. But the analytical data, especially the mass spectral data, are available only for a few of the methylated amino acids. Thus, it is essential to generate mass spectral data and to develop mass spectrometry methods for the identification of all possible methylated amino acids for future metabolomic studies. In this study, all N‐methyl and N,N‐dimethyl amino acids were synthesized by the methylation of α‐amino acids and characterized by a GC‐MS method. The methylated amino acids were derivatized with ethyl chloroformate and analyzed by GC‐MS under EI and methane/CI conditions. The EI mass spectra of ethyl chloroformate derivatives of N‐methyl ( 1–18 ) and N,N‐dimethyl amino acids ( 19–35 ) showed abundant [M‐COOC₂H₅]⁺ ions. The fragment ions due to loss of C₂H₄, CO₂, (CO₂ + C₂H₄) from [M‐COOC₂H₅]⁺ were of structure indicative for 1–18 . The EI spectra of 19–35 showed less number of fragment ions when compared with those of 1–18 . The side chain group (R) caused specific fragment ions characteristic to its structure. The methane/CI spectra of the studied compounds showed [M + H]⁺ ions to substantiate their molecular weights. The detected EI fragment ions were characteristic of the structure that made easy identification of the studied compounds, including isomeric/isobaric compounds. Fragmentation patterns of the studied compounds ( 1–35 ) were confirmed by high‐resolution mass spectra data and further substantiated by the data obtained from ¹³C₂‐labeled glycines and N‐ethoxycarbonyl methoxy esters. The method was applied to human plasma samples for the identification of amino acids and methylated amino acids. Copyright © 2016 John Wiley & Sons, Ltd.     

Microbalance Techniques in Design and Control of Synthetic Carbons

Peculiarities of carbonization of two styrene/divinylbenzene precursors (one sulfonated, another aminated and phosphorylated) have been investigated by thermogravimetry and differential thermal analysis. It was shown that phosphorus compounds incorporate into carbon structure and cause delayed carbonization. Porous structure and surface properties of synthetic carbons have been investigated by standard (BET, α_s method, DA) and advanced (AED, PSD, regularization) methods from benzene and water adsorption isotherms. It was shown that phosphorus-containing carbon is less microporous and shows highly hydrophilic surface. This revised version was published online in August 2006 with corrections to the Cover Date.     

Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.     

组合化学、分子库与新药研究

组合化学是进入90 年代以来寻找及优化新药先导化合物的主要研究方法, 其特点是改变了传统的逐一合成、逐一纯化、逐一筛选的模式, 而是以合成和筛选化学库的形式完成寻找及优化药物先导化合物, 极大地加快了药物先导化合物出现的速度。本文就目前有关组合化学研究的基本理论、基本方法、发展趋势、研究成果以及我国应当采取的措施进行了综述。     

Chiral Compounds Synthesized by Biocatalytic Reductions [New Synthetic Methods (51)]

It has been known for many decades that chiral compounds can be obtained by stereospecific biocatalytic reduction. Further significant methodological developments in this field have, however, only been made during the past ten years; they include the application of previously unused microorganisms and electron donors, the discovery of additional substrates for the known reductases, the development of methods for regenerating reduced pyridine nucleotides, and the discovery of new reductases which were sought for specific preparative purposes. Many chiral compounds can now be synthesized by microbial hydrogenation using H₂ and hydrogenase-containing microorganisms as well as by electromicrobial or electroenzymatic reduction. In the two latter methods, anaerobic or aerobic organisms are supplied with electrons from electrochemically reduced, artificial mediators, e.g., methyl viologen. Reductases that do not require pyridine nucleotides and can accept electrons directly from reduced viologens are especially useful. Two examples of this type of enzyme are described which are of preparative interest. Many cells contain methyl viologen-dependent NAD(P) reductases, a large number of which have still not been characterized. A productivity number is proposed which allows different methods of bioconversion with microorganisms to be compared. The productivity numbers of compounds synthesized by the methods described in this review are often 10- to 100-fold higher than those of substances obtained by conventional techniques.     

Hochdurchsatz-Methoden in der Festkörperchemie. Schneller zum Ziel

High-throughput (HT) methods applied to materials discovery have attracted much attention over the last few years because they permit a fast and efficient investigation of parameter space while consuming only small amounts of starting materials. Nowadays, many different HT techniques in solid state science are employed in order to accelerate the discovery and optimization of materials. The degree of parallelisation, automation, and miniaturization determines the efficiency of a HT methodology. Systematic investigation of parameter space and careful data analysis allow the identification of reaction trends and may give important guidance in better understanding of reaction systems. The described HT methodology for solvothermal reactions allows the systematic investigation of 48 different hydrothermal reactions at a time. The methodology includes automatic dispensing of solids and liquids, followed by homogenization, pH measurement, synthesis, isolation, washing as well as automated phase analysis by X-ray diffraction without the manipulation of individual samples. The focus of this article is on the application of HT methods in the investigation of the parameter space in solid state sciences. Based on selected examples, the power of HT methods in the discovery of new compounds, the optimization of reactions, the identification of reaction trends, as well as the investigation of the influence of process parameters is described. The large amount of data obtained in a short time leads to an improvement towards the understanding of the role that synthesis and process parameters play in the formation of compounds and materials.     

Materials by design at high pressures

Pressure, a fundamental thermodynamic variable, can generate two essential effects on materials. First, pressure can create new high-pressure phases via modification of the potential energy surface. Second, pressure can produce new compounds with unconventional stoichiometries via modification of the compositional landscape. These new phases or compounds often exhibit exotic physical and chemical properties that are inaccessible at ambient pressure. Recent studies have established a broad scope for developing materials with specific desired properties under high pressure. Crystal structure prediction methods and first-principles calculations can be used to design materials and thus guide subsequent synthesis plans prior to any experimental work. A key example is the recent theory-initiated discovery of the record-breaking high-temperature superhydride superconductors H₃S and LaH₁₀ with critical temperatures of 200 K and 260 K, respectively. This work summarizes and discusses recent progress in the theory-oriented discovery of new materials under high pressure, including hydrogen-rich superconductors, high-energy-density materials, inorganic electrides, and noble gas compounds. The discovery of the considered compounds involved substantial theoretical contributions. We address future challenges facing the design of materials at high pressure and provide perspectives on research directions with significant potential for future discoveries.

This work summarizes and discusses recent progress in the theory-oriented discovery of new materials under high pressure, including hydrogen-rich superconductors, high-energy-density materials, inorganic electrides, and noble gas compounds.     

Energy‐resolved technique for discovery and identification of malonyl‐triterpene saponins in Caulophyllum robustum by UHPLC‐electrospray Fourier transform mass spectrometry

Malonyl‐triterpene saponins (MTSs) attract scientific attentions because of their structural diversities and valuable bioactivities. However, its thermal instability brings a huge amount of challenges for isolation and purification of this class of compounds. To our best knowledge, there has been no report on isolation and analysis of MTSs from genus Caulophyllum. In this study, a strategy combining data acquisition using an energy‐resolved technique and the narrow widow extracted ion chromatograms as data mining method was developed for discovery and identification of MTSs in Caulophyllum robustum hair roots by ultra high liquid chromatography coupled to electrospray ionization Fourier transform mass spectrometry. The method was performed at an independent MS full scan using our bottom‐up energies by in‐source collision induced dissociations with 0, 25, 50 and 100 eV in both positive and negative modes. Precursor ion as well as fragment ion information was simultaneously collected from four energy‐resolved MS spectra in a single run of 18 min. The fragmentation pathways of intact deprotonated, protonated and sodium ions of MTSs were proposed for the structural elucidation of Caulophyllum MTSs. A flowchart involving a stepwise procedure based on key fragments from ESI^?/ESI⁺‐FT‐MS^{(1, 1)} to MS^{(1, 4)} spectra was constructed for the identification of structural elements in the MTSs. As a result, a total of 23 MTSs were discovered and tentatively identified, which had not been reported from Caulophyllum species before. All of these were potentially new compounds. This study provides an excellent example for discovery and identification of MTSs in herb medicines. Copyright © 2016 John Wiley & Sons, Ltd.     

Rapid screening,identification, and purification of neuraminidase inhibitors from Lithospermum erythrorhizon Sieb.et Zucc. by ultrafiltration with HPLC–ESI‐TOF‐MS combined with semipreparative HPLC

We put forward an efficient strategy based on bioassay guidance for the rapid screening, identification, and purification of the neuraminidase inhibitors from traditional Chinese medicines, and apply to the discovery of anti‐influenza components from Lithospermiun erythrorhizon Sieb.et Zucc. Ultrafiltration with high‐performance liquid chromatography and electrospray ionization time‐of‐flight mass spectrometry was employed for the rapid screening and preliminarily identification of anti‐influenza components from Zicao. Semipreparative high‐performance liquid chromatography was used for the rapid separation and purification of the target compounds. NMR spectroscopy, mass spectrometry, and UV spectroscopy were used for further structural identification, and the activity of the compounds was verified by in vitro assay. Five compounds were found to have neuraminidase inhibitory activity by this method. Subsequently, the five compounds were separated by semipreparative high‐performance liquid chromatography with the purity over 98% for all of them by high‐performance liquid chromatography test. Combined with the NMR spectroscopy, mass spectrometry, and UV spectroscopy data, they were identified as alkannin, acetylalkannin, isobutyrylalkannin, β,β‐dimethylacryloylalkannin and isovalerylalkannin. The in vitro assay showed that all five compounds had good neuraminidase inhibitory activities. These results suggested that the method is highly efficient, and it can provide platform and methodology supports for the rapid discovery of anti‐influenza active ingredients from complex Chinese herbal medicines.     

Identifying promising compounds in drug discovery: genetic algorithms and some new statistical techniques

Throughout the drug discovery process, discovery teams are compelled to use statistics for making decisions using data from a variety of inputs. For instance, teams are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from multiple screens. To assist in the prioritization process, we propose a desirability function to account for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores. In addition to identifying existing desirable compounds, teams often use prior knowledge to suggest new, potentially promising compounds to be created in the laboratory. Because the chemistry space to search can be dauntingly large, we propose the sequential elimination of level combinations (SELC) method for identifying new optimal compounds. We illustrate this method on a combinatorial chemistry example.     

New approach to the nonparametric determination of the internal rotation potential from electron diffraction data. Reinvestigation ofm-bromonitrobenzene

A numerical algorithm for the nonparametric determination of the internal rotation potential from electron diffraction data using Tikhonov's regularization method is described. The range of admissible values of the regularization parameter is estimated using Hamilton's statistical criterion. m-Bromonitrobenzene was reinvestigated using this approach. It was found that the form of the potential may be reliably established only in the region of the minimum corresponding to the planar conformation of the molecule. Fourier series approximation of the experimental values of the potential in the region of the minimum gives the rotation barrier of 4.6–5.4 kcal/mole. The following basic geometrical parameters have been obtained (r_a in Å, ∠_α in deg, the error equals triple standard deviation): r(C?C)_ave=1.399(3), r(C?N)=1.459(16), 1.459(16), r(N=O)=1.244(3), r(C?Br)=1.884(6), r(C?H)=1.099(20), ∠CC_NC=123.9(1.4), ∠C_NCCBr=116.8(1.5), ∠C_NCC=116.6(1.9), ∠CNO=118.8(0.8). The results are compared with the data for the related compounds.     

Exploiting structural information in patent specifications for key compound prediction

Patent specifications are one of many information sources needed to progress drug discovery projects. Understanding compound prior art and novelty checking, validation of biological assays, and identification of new starting points for chemical explorations are a few areas where patent analysis is an important component. Cheminformatics methods can be used to facilitate the identification of so-called key compounds in patent specifications. Such methods, relying on structural information extracted from documents by expert curation or text mining, can complement or in some cases replace the traditional manual approach of searching for clues in the text. This paper describes and compares three different methods for the automatic prediction of key compounds in patent specifications using structural information alone. For this data set, the cluster seed analysis described by Hattori et al. (Hattori, K.; Wakabayashi, H.; Tamaki, K. Predicting key example compounds in competitors' patent applications using structural information alone. J. Chem. Inf. Model.2008, 48, 135-142) is superior in terms of prediction accuracy with 26 out of 48 drugs (54%) correctly predicted from their corresponding patents. Nevertheless, the two new methods, based on frequency of R-groups (FOG) and maximum common substructure (MCS) similarity measures, show significant advantages due to their inherent ability to visualize relevant structural features. The results of the FOG method can be enhanced by manual selection of the scaffolds used in the analysis. Finally, a successful example of applying FOG analysis for designing potent ATP-competitive AXL kinase inhibitors with improved properties is described.     

The use of Tikhonov regularization method for calculating the distribution function of relaxation times in impedance spectroscopy

The state-of-the-art in realization of the method of distribution of relaxation times (DRT) as applied to the analysis of data of electrochemical impedance spectroscopy is briefly surveyed. The theoretical fundamentals of the DRT method are described, the methods of solving the Fredholm equation of the 1st order with respect to the unknown DRT function are considered as an ill-defined problem. The Tikhonov regularization method presently considered as the most suitable for solving this equation is discussed. For several numerical experiments, the high resolution of the DRT method and its stability with respect to noise in impedance spectra are demonstrated. Among the problems and limitations of the DRT methods, the choice of the optimal regularization coefficient is considered as the most significant. Particularly, it is shown that in those cases where several relaxation processes with the constant phase angle appear in the response of objects under study to ac disturbances, different regularization coefficients should be selected for each of these elements in order to obtain adequate results.