ChemGPS-NP: Tuned for Navigation in Biologically Relevant Chemical Space
Division of Pharmacognosy, Department of Medicinal Chemistry, BMC, Uppsala University, Box 574, S-751 23 Uppsala, Sweden.Journal of Natural Products (Impact Factor: 3.8). 06/2007; 70(5):789-94. DOI: 10.1021/np070002y
Natural compounds are evolutionary selected and prevalidated by Nature, displaying a unique chemical diversity and a corresponding diversity of biological activities. These features make them highly interesting for studies of chemical biology, and in the pharmaceutical industry for development of new leads. Of utmost importance, for the discovery of new biologically active compounds, is the identification and charting of the corresponding biologically relevant chemical space. The primary key to this is the coverage of the natural products' chemical space. Here we introduce ChemGPS-NP, a new tool tuned for handling the chemical diversity encountered in natural products research, in contrast to previous tools focused on the much more restricted drug-like chemical space. The aim is to provide a framework for making compound classification and comparison more efficient and stringent, to identify volumes of chemical space related to particular biological activities, and to track changes in chemical properties due to, for example, evolutionary traits and modifications in biosynthesis. Physical-chemical properties not directly discernible from structural data can be discovered, making selection more efficient and increasing the probability of hit generation when screening natural compounds and analogues.
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- "In addition to the molecular formula, which can be used as a generic parameter for database searches, the RT of the analytes is the other accessible information in metabolite profiling, each metabolite being characterised by its RT, m/z and intensity in any LC–MS datasets. RT is correlated to the physicochemical parameters of a given analyte, and efforts to predict these parameters based on structure information are needed, especially for NPs that have extremely diverse properties and are known to occupy a large chemical space (Feher and Schmidt, 2002; Larsson et al., 2007). "
ABSTRACT: The detection and early identification of natural products (NPs) for dereplication purposes require efficient, high-resolution methods for the profiling of crude natural extracts. This task is difficult because of the high number of NPs in these complex biological matrices and because of their very high chemical diversity. Metabolite profiling using ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HR-MS) is very efficient for the separation of complex mixtures and provides molecular formula information as a first step in dereplication. This structural information alone or even combined with chemotaxonomic information is often not sufficient for unambiguous metabolite identification. In this study, a representative set of 260 NPs containing C, H, and O atoms only was analysed in generic UHPLC-HR-MS profiling conditions. Two easy to use quantitative structure retention relationship (QSRR) models were built based on the measured retention time and on eight simple physicochemical parameters calculated from the structures. First, an original approach using several partial least square (PLS) regressions according to the phytochemical classes provided satisfactory results with an easy calculation. Secondly, a unique artificial neural network (ANN) model provided similar results on the whole set of NPs but required dedicated software. The retention prediction methods described in this study were found to improve the level of confidence of the identification of given analytes among putative isomeric structures. Its applicability was verified for the dereplication of NPs in model plant extracts.
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- "This step is essential because of the well-recognized disparity between commercial fragment libraries and natural products—commercial fragments have a structural makeup that is largely biased toward readily available small, flat, heterocyclic molecules, whereas natural products have a prevalence of stereogenic centers and even include reactive functional groups.[3b, 10] In the first step of the method, a set of known ligands of both the protein of interest (where available) as well as other related proteins is compiled. Subsequently, a fragment library is assembled that samples the substructural chemical space present in the ligands and is hence expected to increase fragment hit rates. "
ABSTRACT: We present a novel fragment-based approach that tackles some of the challenges for chemical biology of predicting protein function. The general approach, which we have termed biofragments, comprises two key stages. First, a biologically relevant fragment library (biofragment library) can be designed and constructed from known sets of substrate-like ligands for a protein class of interest. Second, the library can be screened for binding to a novel putative ligand-binding protein from the same or similar class, and the characterization of hits provides insight into the basis of ligand recognition, selectivity, and function at the substrate level. As a proof-of-concept, we applied the biofragments approach to the functionally uncharacterized Mycobacterium tuberculosis (Mtb) cytochrome P450 isoform, CYP126. This led to the development of a tailored CYP biofragment library with notable 3D characteristics and a significantly higher screening hit rate (14 %) than standard drug-like fragment libraries screened previously against Mtb CYP121 and 125 (4 % and 1 %, respectively). Biofragment hits were identified that make both substrate-like type-I and inhibitor-like type-II interactions with CYP126. A chemical-fingerprint-based substrate model was built from the hits and used to search a virtual TB metabolome, which led to the discovery that CYP126 has a strong preference for the recognition of aromatics and substrate-like type-I binding of chlorophenol moieties within the active site near the heme. Future catalytic analyses will be focused on assessing CYP126 for potential substrate oxidative dehalogenation.
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- "Is it not time to, at least for a while, study those already well-defined chemical structures? Furthermore, the selection of organisms for collection and further studies could in much higher degree be based on prediction models, for example, combining phylogeny with bioinformatic data (Larsson et al., 2007). Also important for a sustainable use of natural products is the increased knowledge emerging about the real producer of specific secondary metabolites found in plants and animals. "
ABSTRACT: Identifying bioactive molecules from complex biomasses requires careful selection and execution of relevant bioassays in the various stages of the discovery process of potential leads and targets. The aim of this review is to share our long-term experience in bioassay-guided isolation, and mechanistic studies, of bioactive compounds from different organisms in nature with emphasis on anti-inflammatory and antimicrobial activity. In the search for anti-inflammatory activity, in vivo and in vitro model combinations with enzymes and cells involved in the inflammatory process have been used, such as cyclooxygenases, human neutrophils and human cancer cell lines. Methods concerning adsorption and perforation of bacteria, fungi, human cells and model membranes, have been developed and optimised, with emphasis on antimicrobial peptides and their interaction with the membrane target, in particular their ability to distinguish host from pathogen. A long-term research has provided experience of selection and combination of bioassay models, which has led to an increased understanding of ethnopharmacological and ecological observations, together with in-depth knowledge of mode of action of isolated compounds. A more multidisciplinary approach and a higher degree of fundamental research in development of bioassays are often necessary to identify and to fully understand the mode of action of bioactive molecules with novel structure-activity relationships from natural sources. Copyright © 2013 John Wiley & Sons, Ltd.
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