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      Examination of molecular space and feasible structures of bioactive components of humic substances by FTICR MS data mining in ChEMBL database

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          Abstract

          Humic substances (HS) are complex natural mixtures comprising a large variety of compounds produced during decomposition of decaying biomass. The molecular composition of HS is extremely diverse as it was demonstrated with the use of high resolution mass spectrometry. The building blocks of HS are mostly represented by plant-derived biomolecules (lignins, lipids, tannins, carbohydrates, etc.). As a result, HS show a wide spectrum of biological activity. Despite that, HS remain a ‘biological activity black-box’ due to unknown structures of constituents responsible for the interaction with molecular targets. In this study, we investigated the antiviral activity of eight HS fractions isolated from peat and coal, as well as of two synthetic humic-like materials. We determined molecular compositions of the corresponding samples using ultra-high resolution Fourier-transform ion cyclotron resonance mass-spectrometry (FTICR MS). Inhibitory activity of HS was studied with respect to reproduction of tick-borne encephalitis virus (TBEV), which is a representative of Flavivirus genus, and to a panel of enteroviruses (EVs). The samples of natural HS inhibited TBEV reproduction already at a concentration of 1 µg/mL, but they did not inhibit reproduction of EVs. We found that the total relative intensity of FTICR MS formulae within elemental composition range commonly attributed to flavonoid-like structures is correlating with the activity of the samples. In order to surmise on possible active structural components of HS, we mined formulae within FTICR MS assignments in the ChEMBL database. Out of 6502 formulae within FTICR MS assignments, 3852 were found in ChEMBL. There were more than 71 thousand compounds related to these formulae in ChEMBL. To support chemical relevance of these compounds to natural HS we applied the previously developed approach of selective isotopic exchange coupled to FTICR MS to obtain structural information on the individual components of HS. This enabled to propose compounds from ChEMBL, which corroborated the labeling data. The obtained results provide the first insight onto the possible structures, which comprise antiviral components of HS and, respectively, can be used for further disclosure of antiviral activity mechanism of HS.

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          Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

          Background Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas. Results An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries. Conclusion The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.
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            Fourier transform ion cyclotron resonance mass spectrometry: a primer.

            This review offers an introduction to the principles and generic applications of FT-ICR mass spectrometry, directed to readers with no prior experience with the technique. We are able to explain the fundamental FT-ICR phenomena from a simplified theoretical treatment of ion behavior in idealized magnetic and electric fields. The effects of trapping voltage, trap size and shape, and other nonidealities are manifested mainly as perturbations that preserve the idealized ion behavior modified by appropriate numerical correction factors. Topics include: effect of ion mass, charge, magnetic field, and trapping voltage on ion cyclotron frequency; excitation and detection of ICR signals; mass calibration; mass resolving power and mass accuracy; upper mass limit(s); dynamic range; detection limit, strategies for mass and energy selection for MSn; ion axialization, cooling, and remeasurement; and means for guiding externally formed ions into the ion trap. The relation of FT-ICR MS to other types of Fourier transform spectroscopy and to the Paul (quadrupole) ion trap is described. The article concludes with selected applications, an appendix listing accurate fundamental constants needed for ultrahigh-precision analysis, and an annotated list of selected reviews and primary source publications that describe in further detail various FT-ICR MS techniques and applications.
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              PubChem BioAssay: 2017 update

              PubChem's BioAssay database (https://pubchem.ncbi.nlm.nih.gov) has served as a public repository for small-molecule and RNAi screening data since 2004 providing open access of its data content to the community. PubChem accepts data submission from worldwide researchers at academia, industry and government agencies. PubChem also collaborates with other chemical biology database stakeholders with data exchange. With over a decade's development effort, it becomes an important information resource supporting drug discovery and chemical biology research. To facilitate data discovery, PubChem is integrated with all other databases at NCBI. In this work, we provide an update for the PubChem BioAssay database describing several recent development including added sources of research data, redesigned BioAssay record page, new BioAssay classification browser and new features in the Upload system facilitating data sharing.
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                Author and article information

                Contributors
                dmitry_o@qsar.chem.msu.ru
                iperm@med.chem.msu.ru
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 August 2019
                19 August 2019
                2019
                : 9
                : 12066
                Affiliations
                [1 ]FSBSI “Chumakov FSC R&D IBP RAS”, Moscow, 108819 Russia
                [2 ]ISNI 0000 0004 0555 3608, GRID grid.454320.4, Skolkovo Institute of Science and Technology, ; Moscow, 143026 Russia
                [3 ]ISNI 0000 0001 2342 9668, GRID grid.14476.30, Department of Chemistry, , Lomonosov Moscow State University, ; Moscow, 119991 Russia
                [4 ]ISNI 0000 0001 2342 9668, GRID grid.14476.30, Department of Fundamental Medicine, , Lomonosov Moscow State University, ; Moscow, 119991 Russia
                [5 ]ISNI 0000 0001 2288 8774, GRID grid.448878.f, Sechenov First Moscow State Medical University, ; Moscow, 119991 Russia
                [6 ]GRID grid.465277.5, State Research Center “Institute of Immunology” of the Federal Medical-Biological Agency of Russia, ; Moscow, 115478 Russia
                Author information
                http://orcid.org/0000-0001-8734-5527
                http://orcid.org/0000-0002-0462-2945
                Article
                48000
                10.1038/s41598-019-48000-y
                6700089
                1bd480e7-9df8-49de-90a9-fe229af0720f
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 August 2018
                : 29 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002261, Russian Foundation for Basic Research (RFBR);
                Award ID: 16-03-01057
                Award ID: 16-03-01057
                Award Recipient :
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                © The Author(s) 2019

                Uncategorized
                cheminformatics,screening
                Uncategorized
                cheminformatics, screening

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