Alain-Dominique Gorse
University of Queensland
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Featured researches published by Alain-Dominique Gorse.
Current Topics in Medicinal Chemistry | 2006
Alain-Dominique Gorse
The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10(13) to 10(180). These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.
Journal of Molecular Structure-theochem | 1993
Alain-Dominique Gorse; Michel Pesquer
Abstract Geometries and energies of some para-substituted N , N -dimethylaniline derivatives have been calculated using AM1 and ab initio methods for planar and various pyramidal structures with full geometry optimization. The calculated geometry characteristics for aniline show the ability of some theoretical methods to reproduce accurately the experimental data. The influence and interdependence of rotation and inversion angles around C-NX 2 of the amino group are discussed in relation to the nature of the substituents on dimethylaniline. Qualitative and quantitative rules are formulated for the structural properties of these compounds.
PLOS ONE | 2015
Mauro Pascolutti; Marc Ronald Campitelli; Bao Nguyen; Ngoc Bich Pham; Alain-Dominique Gorse; Ronald J. Quinn
Natural products are universally recognized to contribute valuable chemical diversity to the design of molecular screening libraries. The analysis undertaken in this work, provides a foundation for the generation of fragment screening libraries that capture the diverse range of molecular recognition building blocks embedded within natural products. Physicochemical properties were used to select fragment-sized natural products from a database of known natural products (Dictionary of Natural Products). PCA analysis was used to illustrate the positioning of the fragment subset within the property space of the non-fragment sized natural products in the dataset. Structural diversity was analysed by three distinct methods: atom function analysis, using pharmacophore fingerprints, atom type analysis, using radial fingerprints, and scaffold analysis. Small pharmacophore triplets, representing the range of chemical features present in natural products that are capable of engaging in molecular interactions with small, contiguous areas of protein binding surfaces, were analysed. We demonstrate that fragment-sized natural products capture more than half of the small pharmacophore triplet diversity observed in non fragment-sized natural product datasets. Atom type analysis using radial fingerprints was represented by a self-organizing map. We examined the structural diversity of non-flat fragment-sized natural product scaffolds, rich in sp3 configured centres. From these results we demonstrate that 2-ring fragment-sized natural products effectively balance the opposing characteristics of minimal complexity and broad structural diversity when compared to the larger, more complex fragment-like natural products. These naturally-derived fragments could be used as the starting point for the generation of a highly diverse library with the scope for further medicinal chemistry elaboration due to their minimal structural complexity. This study highlights the possibility to capture a high proportion of the individual molecular interaction motifs embedded within natural products using a fragment screening library spanning 422 structural clusters and comprised of approximately 2800 natural products.
PLOS ONE | 2015
Jasmin Straube; Alain-Dominique Gorse; Bevan Emma Huang; Kim-Anh Lê Cao
Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.
Biotechnology Advances | 2015
Pakornwit Sarnpitak; Prashant Mujumdar; Paul Taylor; Megan Cross; Mark J. Coster; Alain-Dominique Gorse; Mikhail Krasavin; Andreas Hofmann
Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.
Journal of Cheminformatics | 2015
Parisa Amani; Todd Sneyd; Sarah Preston; Neil D. Young; Lyndel Mason; Ulla-Maja Bailey; Jonathan B. Baell; David G Camp; Robin B. Gasser; Alain-Dominique Gorse; Paul Taylor; Andreas Hofmann
BackgroundThe increased use of small-molecule compound screening by new users from a variety of different academic backgrounds calls for adequate software to administer, appraise, analyse and exchange information obtained from screening experiments. While software and spreadsheet solutions exist, there is a need for software that can be easily deployed and is convenient to use.ResultsThe Java application cApp addresses this need and aids in the handling and storage of information on small-molecule compounds. The software is intended for the appraisal of compounds with respect to their physico-chemical properties, analysis in relation to adherence to likeness rules as well as recognition of pan-assay interference components and cross-linking with identical entries in the PubChem Compound Database. Results are displayed in a tabular form in a graphical interface, but can also be written in an HTML or PDF format. The output of data in ASCII format allows for further processing of data using other suitable programs. Other features include similarity searches against user-provided compound libraries and the PubChem Compound Database, as well as compound clustering based on a MaxMin algorithm.ConclusionscApp is a personal database solution for small-molecule compounds which can handle all major chemical formats. Being a standalone software, it has no other dependency than the Java virtual machine and is thus conveniently deployed. It streamlines the analysis of molecules with respect to physico-chemical properties and drug discovery criteria; cApp is distributed under the GNU Affero General Public License version 3 and available from http://www.structuralchemistry.org/pcsb/. To download cApp, users will be asked for their name, institution and email address. A detailed manual can also be downloaded from this site, and online tutorials are available at http://www.structuralchemistry.org/pcsb/capp.php.
Journal of Natural Products | 2016
Marie-Laure Vial; Dusan Zencak; Tanja Grkovic; Alain-Dominique Gorse; Alan Mackay-Sim; George D. Mellick; Stephen A. Wood; Ronald J. Quinn
Harnessing the inherent biological relevance of natural products requires a method for the recognition of biological effects that may subsequently lead to the discovery of particular targets. An unbiased multidimensional profiling method was used to examine the activities of natural products on primary cells derived from a Parkinsons disease patient. The biological signature of 482 natural products was examined using multiparametric analysis to investigate known cellular pathways and organelles implicated in Parkinsons disease such as mitochondria, lysosomes, endosomes, apoptosis, and autophagy. By targeting several cell components simultaneously the chance of finding a phenotype was increased. The phenotypes were then clustered using an uncentered correlation. The multidimensional phenotypic screening showed that all natural products, in our screening set, were biologically relevant compounds as determined by an observed phenotypic effect. Multidimensional phenotypic screening can predict the cellular function and subcellular site of activity of new compounds, while the cluster analysis provides correlation with compounds with known mechanisms of action. This study reinforces the value of natural products as biologically relevant compounds.
Journal of Computational Chemistry | 1994
Alain-Dominique Gorse; Michel Pesquer
Fraga potential calculations with atomic point charges and geometrical parameters calculated from AM1 calculations have been used to calculate spectral shifts upon electronic excitation in twisted intramolecular charge transfer (TICT) systems due to intermolecular interactions. Changes of atomic polarizabilities have also been taken into account. Present calculations deal with absorption transitions of the p‐N,N‐dimethylaminobenzonitrile (DMABN) surrounded by methane, water, acetone, or acetonitrile solvent molecules. The methodology permits us to evaluate the influence of the solvent molecule on DMABN dimethylamino motions and to find the most stable conformation of a cluster configuration which can lead to a blue or red shift. The results have been compared with the experimental work of Warren et al.7 and confirm their analysis.
The Journal of Physical Chemistry | 1995
Alain-Dominique Gorse; Michel Pesquer
Protein Engineering | 1997
Alain-Dominique Gorse; Jill E. Gready
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