Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs

A Systemic Receptor Network Triggered by Human cytomegalovirus Entry

Virus entry is a multistep process that triggers a variety of cellular pathways interconnecting into a complex network, yet the molecular complexity of this network remains largely unsolved. Here, by employing systems biology approach, we reveal a systemic virus-entry network initiated by human cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network contains all known interactions and functional modules (i.e. groups of proteins) coordinately responding to HCMV entry. The number of both genes and functional modules activated in this network dramatically declines shortly, within 25 min post-infection. While modules annotated as receptor system, ion transport, and immune response are continuously activated during the entire process of HCMV entry, those for cell adhesion and skeletal movement are specifically activated during viral early attachment, and those for immune response during virus entry. HCMV entry requires a complex receptor network involving different cellular components, comprising not only cell surface receptors, but also pathway components in signal transduction, skeletal development, immune response, endocytosis, ion transport, macromolecule metabolism and chromatin remodeling. Interestingly, genes that function in chromatin remodeling are the most abundant in this receptor system, suggesting that global modulation of transcriptions is one of the most important events in HCMV entry. Results of in silico knock out further reveal that this entire receptor network is primarily controlled by multiple elements, such as EGFR (Epidermal Growth Factor) and SLC10A1 (sodium/bile acid cotransporter family, member 1). Thus, our results demonstrate that a complex systemic network, in which components coordinating efficiently in time and space contributes to virus entry.Comment: 26 page

OrChem - An open source chemistry search engine for Oracle®

<p>Abstract</p> <p>Background</p> <p>Registration, indexing and searching of chemical structures in relational databases is one of the core areas of cheminformatics. However, little detail has been published on the inner workings of search engines and their development has been mostly closed-source. We decided to develop an open source chemistry extension for Oracle, the de facto database platform in the commercial world.</p> <p>Results</p> <p>Here we present OrChem, an extension for the Oracle 11G database that adds registration and indexing of chemical structures to support fast substructure and similarity searching. The cheminformatics functionality is provided by the Chemistry Development Kit. OrChem provides similarity searching with response times in the order of seconds for databases with millions of compounds, depending on a given similarity cut-off. For substructure searching, it can make use of multiple processor cores on today's powerful database servers to provide fast response times in equally large data sets.</p> <p>Availability</p> <p>OrChem is free software and can be redistributed and/or modified under the terms of the GNU Lesser General Public License as published by the Free Software Foundation. All software is available via <url>http://orchem.sourceforge.net</url>.</p

Application of Ion Mobility-High Resolution Mass Spectrometry and In Silico Tools for Identifying Non-Volatile Substances in Food Contact Material

The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from food contact materials (FCMs). Collision cross section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from FCMs is the lack of published empirical CCS values, thus, this thesis firstly established a CCS database for extractables and leachables from FCMs. On the other hand, many chemicals in FCMs don't have commercial standards, their experimental CCS values cannot be obtained, in this case, machine learning approaches were used to build the models to predict the CCS values for chemicals in FCMs. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]+ adducts and 95.0% of CCS values for [M + Na]+ adducts in testing sets predicted with Besides the CCS values, the retention time (RT) is also very important for the unknown identifications. therefore, we also developed a prediction model to generate the predicted RT values. Based on the in-silico RT and CCS prediction models, a workflow to identify nonvolatile migrates from FCMs was proposed using liquid chromatography-ion mobility-high-resolution mass spectrometry. This workflow was evaluated by screening the chemicals that migrated from polyamide spatulas, we found that the predicted RT and CCS values can reduce the number of candidates and increase the confidence of identification in targeted and suspect screening analysis. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.<br /

Development and implementation of in silico molecule fragmentation algorithms for the cheminformatics analysis of natural product spaces

Computational methodologies extracting specific substructures like functional groups or molecular scaffolds from input molecules can be grouped under the term “in silico molecule fragmentation”. They can be used to investigate what specifically characterises a heterogeneous compound class, like pharmaceuticals or Natural Products (NP) and in which aspects they are similar or dissimilar. The aim is to determine what specifically characterises NP structures to transfer patterns favourable for bioactivity to drug development. As part of this thesis, the first algorithmic approach to in silico deglycosylation, the removal of glycosidic moieties for the study of aglycones, was developed with the Sugar Removal Utility (SRU) (Publication A). The SRU has also proven useful for investigating NP glycoside space. It was applied to one of the largest open NP databases, COCONUT (COlleCtion of Open Natural prodUcTs), for this purpose (Publication B). A contribution was made to the Chemistry Development Kit (CDK) by developing the open Scaffold Generator Java library (Publication C). Scaffold Generator can extract different scaffold types and dissect them into smaller parent scaffolds following the scaffold tree or scaffold network approach. Publication D describes the OngLai algorithm, the first automated method to identify homologous series in input datasets, group the member structures of each group, and extract their common core. To support the development of new fragmentation algorithms, the open Java rich client graphical user interface application MORTAR (MOlecule fRagmenTAtion fRamework) was developed as part of this thesis (Publication E). MORTAR allows users to quickly execute the steps of importing a structural dataset, applying a fragmentation algorithm, and visually inspecting the results in different ways. All software developed as part of this thesis is freely and openly available (see https://github.com/JonasSchaub)

Production, Characterization and Design of Applications of Regenerated Humic Acids

Předkládaná disertační práce se zabývá huminovými látkami (HL), zejména huminovými kyselinami a jejich solemi, tj. humáty. V práci je prezentována literární rešerše o huminových látkách, stručně je zmíněna historie výzkumu huminových látek, obsáhle jsou pak prezentovány práce o jejich struktuře a supramolekulovém uspořádání. Jsou též zmíněny metody extrakce huminových látek z různých zdrojů. Dále jsou v tomto pojednání zařazeny a diskutovány práce zabývající se biologickými „hormonálními“ vlastnostmi HL. Z hlediska chemie životního prostředí jsou zmíněny zejména sorpční vlastnosti HL. Popsané průmyslové aplikace HL pak zahrnují celou řadu patentů a publikovaných prací, zabývající se využitím huminových látek jakožto barviv či přísad do polymerů, atd. V experimentální části je popsána příprava regenerovaných lignitů, z nichž jsou dále extrahovány huminové kyseliny a připraveny huminové soli, tzv. humáty. Princip regenerace spočívá v oxidaci původního lignitu širokou koncentrační řadou kyseliny dusičné a peroxidu vodíku. K charakterizaci získaných huminových materiálů je výhodně použit nový přístup, kdy jsou kombinovány výsledky z analýz chemických (elementární analýza, infračervená spektroskopie, termogravimetrie) a fyzikálně-chemických (dynamický rozptyl světla, spektrometrie relaxační nukleární magnetické rezonance, vysoceúčelná velikostně-vylučovací chromatografie a fluorescenční spektrometrie se zhodnocením hydratace humátů pomocí vysocerozlišovací ultrazvukové spektrometrie a hustoměru). Tyto metody jsou navíc položeny do kontextu s charakterizací biologické aktivity humátů, která je provedena pomocí modifikované metody založené na měření délky a hmotnosti kořenů kukuřice a laterálních kořenů. V závěru je použit statistický přístup s využitím Pearsonova korelačního koeficientu a jsou též (na základě výsledků pilotních studií) navrženy dvě potenciální environmentální aplikace regenerovaných huminových materiálů – sorpce antibiotika tetracyklinu a využití regenerovaného lignitu jako zdroje zkvasitelných cukrů.Submitted thesis deals with the humic substances, namely the humic acids and their salts, i.e. potassium humates. A literature review about humic substances is presented, the brief history of their research, their structure issues, supramolecular arrangement and applications. A special attempt is paid to review methods of extractions of humic acids from various sources. Further, the papers concerning biological, hormone-like properties are listed and discussed. From the environmental chemistry point of view mainly sorption properties are overviewed. Industrial applications of humics are covered by a wide range of patents and published works such as for instance dyes, polymer additives, etc. In experimental part, the regeneration of lignite and preparation of humic acids and their salts is described. The regeneration principle lies in the oxidation of parental lignite with a range of concentrations of nitric acid and hydrogen peroxide. Novel approach is presented in the characterization humic substances, where the chemical characteristics like Elemental Analysis, Fourier Transform Infra-red Spectroscopy and Thermogravimetry are followed by physico-chemical assessments (Dynamic Light Scattering, Fast Field Cycling Nuclear Magnetic Resonance Relaxometry, High Performace Size Exclusion Chromatography and Fluorescence Spectroscopy in combination with research of humates hydration by means of High Resolution Ultrasonic Spectrometry and Densitometry). These chemical and physico-chemical characteristics are put into the correlation with the biological characteristics, i.e. biological activity of humates, which is assessed by a slightly modified method based on the determination of maize roots weight, length and number of lateral roots. Finally, the statistical approach is applied via Pearson’s correlation coefficient and as a pilot studies, two environmental applications of regenerated humic materials are designed, in the field of sorption of tetracycline antimicrobial and researching of regenerated lignited as a potential source of fermentable sugars.

Confident metabolite structure annotation with COSMIC

Small molecules are key to biomarker discovery, drug development, toxicity screenings of ecosystems like rivers and lakes, and many more important research areas in multiple life sciences. Elucidating the exact structure of these metabolites is often crucial in determining their functionality, however, confident annotation of these structures remains a major challenge. To analyse samples of small molecules occurring in nature, mass spectrometry is the currently predominant technique. While mass spectrometry is used to measure the mass of a compound, tandem mass spectrometry can be used to additionally measure the mass of its fragments. The resulting spectral data however is highly non-trivial to interpret. This bottleneck accelerates the development of computational tools to annotate metabolite structures from mass spectrometry data, which enables rapid, large-scale structure annotation independent from spectral libraries. These tools return some proportion of incorrect annotations, which can vastly outnumber correct annotations. Scientists using these tools need to be able to differentiate correct from incorrect annotations. We develop an E-value computation that is based on proxy decoys drawn from the PubChem database and show that this E-value score outperforms the current CSI:FingerID hit score for the task of separating correct from incorrect annotations. To further improve on this, we develop a Percolator inspired machine learning approach, where we train linear support vector machines for this separation task. The confidence score outperforms the original CSI:FingerID hit score, the E-value score and all other tools that participated in the CASMI 2016 contest by a wide margin. Arguably, our confidence score enables confident structure annotation for a relevant portion of a dataset for the first time. We then show the power of this COSMIC workflow by annotating novel bile acid conjugate structures never reported before in a mouse fecal dataset