General purpose detection, characterization and comparison of molecular interactions.
Discovery of novel bioactive molecules (biologics and small chemical entities) is a complex endeavour and generally involves several years of research and development. During this discovery process several properties of such molecular candidates have to be optimized towards various endpoints, such as activity vs a therapeutic target, ADMET properties, pharmacokinetics without speaking about putative patentability issues. One of the most important hallmarks of a novel bioactive entity is its pattern of intermolecular interactions.
Since the seeding paper on SIFTs (Structural Interaction Fingerprints (Deng, Chuaqui, & Singh, 2004)), several studies have been proposed for the detection and comparison of molecular interactions. (Da & Kireev, 2014; Desaphy, Raimbaud, Ducrot, & Rognan, 2013). CREDO is an example of first attempts to make such information publicly available (A. Schreyer & Blundell, 2009; A. M. Schreyer & Blundell, 2013).
Most of these, as well as similar studies, reduce the representation of a molecular interaction to an easily comparable fingerprint. Unfortunately these representations have inherent drawbacks on querying large databases of molecular interactions and furthermore, none of the methods are easily available for privately held companies as well as for academics interested by high-throughput approaches.
This PhD project aims at providing methodological basics for a large-scale description and analysis of molecular interactions. The underlying methods should be able to describe molecular interactions allowing later comparison of subsets of such descriptions instantaneously when querying a large database. Furthermore, the underlying methods should be able to correctly treat non-curated data, such as available in the Protein Data Bank. Seeding work has been already performed in the scope of binding site comparison (Doppelt-Azeroual, Delfaud, Moriaud, & De Brevern, 2010; Schmidtke, n.d.).
The project is part of a larger development project within Discngine and carried out in collaboration with a major pharmaceutical company.
We are seeking for a highly motivated and autonomous PhD candidate for a period of three years. This PhD project will be supervised by Vincent Le Guilloux, Ph.D. & Peter Schmidtke, Ph.D. Discngine (Paris based privately held company, http://www.discngine.com, publications at https://scholar.google.fr/citations?user=UtOSocMAAAAJ&hl=en) and Alexandre G. de Brevern, Ph.D., D.Sc. from the DSIMB lab (INSERM, Univ Paris Diderot, Sorbonne Paris Cité, INTS, GR-Ex, see publications at https://scholar.google.fr/citations?user=NB4OJhoAAAAJ&hl=fr). The PhD will be conducted under CIFRE conventions (http://www.anrt.asso.fr/fr/pdf/plaquette_cifre_complete_avril2009_GB.pdf).
The ideal PhD candidate should hold a Master's degree in bioinformatics, biophysics or informatics with a strong interest on structural biology and perception of intermolecular interactions. Notions of database design / querying, graph-based technologies, graph-theory and molecular perception are required for this positions. Strong programming expertise as well as analytical skills are a must. Previous experience in the area of peptide & small molecular design would facilitate the initial phase of the project.
Expected starting date: Octobre 2015
Duration: 3 years
Location: Paris (France)
Da, C., & Kireev, D. B. (2014). Structural Protein-Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study. Journal of Chemical Information and Modeling, 140813100524004. doi:10.1021/ci500319f
Deng, Z., Chuaqui, C., & Singh, J. (2004). Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions. Journal of Medicinal Chemistry, 47(2), 337–344. doi:10.1021/jm030331x
Desaphy, J., Raimbaud, E., Ducrot, P., & Rognan, D. (2013). Encoding protein-ligand interaction patterns in fingerprints and graphs. Journal of Chemical Information and Modeling, 53(3), 623–637. doi:10.1021/ci300566n
Doppelt-Azeroual, O., Delfaud, F., Moriaud, F., & De Brevern, A. G. (2010). Fast and automated functional classification with MED-SuMo: An application on purine-binding proteins. Protein Science, 19(4), 847–867. doi:10.1002/pro.364
Schmidtke, P. (n.d.). Protein-ligand binding sites. Identification, characterization and interrelations. Universitat de Barcelona. Retrieved from http://tdx.cat/handle/10803/51340
Schreyer, A., & Blundell, T. (2009). CREDO: A protein-ligand interaction database for drug discovery. Chemical Biology and Drug Design, 73, 157–167. doi:10.1111/j.1747-0285.2008.00762.x
Schreyer, A. M., & Blundell, T. L. (2013). CREDO: A structural interactomics database for drug discovery. Database, 2013. doi:10.1093/database/bat049
Weill, N., & Rognan, D. (2009). Development and validation of a novel protein-ligand fingerprint to mine chemogenomic space: Application to G protein-coupled receptors and their ligands. Journal of Chemical Information and Modeling, 49(4), 1049–1062. doi:10.1021/ci800447g