ChemGPS-NP: tuned for navigation in biologically relevant chemical space.
ABSTRACT 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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ABSTRACT: Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.PLoS ONE 01/2013; 8(6):e63730. · 3.73 Impact Factor
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ABSTRACT: Natural products have historically been an extremely productive source for new medicines in all cultures and continue to deliver a great variety of structural templates for drug discovery and development. Although products derived from natural sources may not necessarily represent active ingredients in their final form, the majority of all drugs in the market have their origin in nature [1, 2]. Therefore, the foremost emphasis in this chapter is given to aspects concerning the identification, properties, and development of potential drug candidates from natural products. It is the intention to give a high-level overview of the current status and developments in the field.06/2013: pages 3-35; , ISBN: ISBN 978-953-51-1158-0
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ABSTRACT: Drug discovery based on natural products has a long successful history. To further advance the identification of new drugs from compounds of natural origin, natural product research is increasingly being combined with computer-aided drug design techniques. Herein, we review the recent advances in the application of chemoin-formatics methods to quantify the chemical diversity and structural complexity of natural products and analyze their distribution in chemical space. We also discuss the progress in virtual screening to systematically identify bioactive compounds in natural products databases and the advancement of target fishing methods to uncover molecular targets of compounds from natural origin. www.relaquim.comRevista latinoamericana de quimica. 09/2013; 41:95.