William H. Bisson (University of Geneva, Switzerland)- Leonardo Scapozza (University of Geneva, Switzerland)
Chemical Genomics (CG) represents a convergence of biology and chemistry in the era of global approaches to target identification and intervention and reflects the need for chemistry on a genomic scale.
We heritated a significant amount of gene and protein sequence informations from the Human Genome and Epigenome Project. The post-genomic challenge is now shifting 1D genome sequence data into 3D protein structure data.
Computational CG originated by the fact that the effect of small molecules in the cell is often initiated by direct interactions with proteins and it is a valuable tool to generate and investigate these interactions at the molecular level. The knowledge-based exploration of the ligand-target SAR matrix (the chemogenomics knowledge space) will help discovering and validating an increase number of small molecule compounds and characterize critical protein targets earlier and faster. 1
Computational CG can also become very useful to explain intracellular mechanistic events involved in cancer or other diseases at a 3D level by studying binding interactions at protein-protein 2 or protein-ligand level. 3, 4
Computational CG techniques screen and select biological active molecules in the same way as classical HTS but faster and cheaper. As a matter of fact, Computational CG can characterize new small molecule chemical probes to be used in phenotypic screens for the validation of protein targets that are part of critical intracellular signaling pathways. 5, 6 In addition, In Silico CG approaches either using chemical similarity as a probe of protein function or to predict off-target effects of drug using networks methods are reported. 7
Drug polypharmacology has inspired efforts to predict and characterize drug-target associations. The field of systems chemical biology integrates chemistry, biology and computation to generate understanding about the way small molecules affect biological system as a whole. Including Computational CG methods, system biology analyzes networks of many kinds of data including compounds, targets, genes, diseases, side effects, metabolic pathway to investigate complex systematic effects of drugs and other chemical compounds on biological systems. 8
Computational CG techniques can help designing and finding hit chemical classes, hitting a specific protein target with potential to be developed into drugs of success. Thus, successful prediction tools for binding affinity 9, toxicity 10, ADME properties 11 off-target bioactivities 7, 12 and other important physical chemistry properties 13 are extremely important and challenging in computational medicinal chemistry. Significant steps have been taking in these directions so far.
Currently, in silico CG techniques can be divided into structure- (when a experimental resolved structure is available or through homology models) and ligand-based (scaffold hopping, pharmacophore screening methods). 14 The structure-based approach usually involves virtual ligand screening (VLS) of databases of compounds through molecular docking, binding pocket site analysis or Molecular Dynamics simulations of protein-ligand complexes and key protein-protein interactions. 15, 16 Successful in silico applications in CG have been also utilized in the field of in silico enzyme activity proving the usefulness of these techniques in complementing in vitro and in vivo screens. 17
Improving VLS performance and range of action in computational medicinal chemistry is a must. Recent successful applications for the identification of bioisosteric replacements in drug design and the use of protein fingerprints to enhance chemogenomics-based virtual screening have been reported. 18, 19 In addition, handling molecular diversity will make compound design more adaptive. 20 The druggable chemical space is limited by biological restraints, hence we must learn from existing active chemical scaffolds.
Based on this concept, drug repurposing techniques are becoming more and more popular. In silico screenings using FDA approved drugs databases have been described to be very useful to accelerate and optimize drug discovery and development pipelines. 7, 21
Chemical Genomics is a cutting-edge interdisciplinary research topic and can definitely play a key role to understand computationally system biology and to improve current computational medicinal chemistry techniques. These major aspects will be discussed during the Workshop with the presence of internationally recognized speakers and experts from both academia and industry.
- E. Jacoby. Computational chemogenomics. 2011 John Wiley & Sons, Ltd. 1, 57-67.
- A. Stein et al. A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks. Structure 19, 881-889 (2011).
- R.H. Gunby et al. Structural insights into the ATP binding pocket of the anaplastic lymphoma kinase by site-directed mutagenesis, inhibitor binding analysis, and homology modeling. J. Med. Chem. 49, 5759-5768 (2006).
- W.H. Bisson et al. Ligand selectivity for the acetylcholine binding site of the rat alpha4beta2 and alpha3beta4 nicotinic subtypes investigated by molecular docking. J. Med. Chem. 48, 5123-5130 (2005).
- G. Laggner et al. Chemical informatics and target identification in a zebrafish phenotypic screen. Nat. Chem. Biol. 8, 144-146 (2011).
- R. Abagyan,. Computational chemistry in 25 years. J. Comput. Aided Mol. Des. 26, 9-10 (2012).
- M.J. Keiser et al. Predicting new molecular targets for known drugs. Nature 462, 175-181 (2009).
- D.J. Wild et al. Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discovery Today (in press)
- http://www.schrodinger.com; http://www.molsoft.com.
- F. Broccatelli et al. Improving the prediction of the brain disposition for orally administered drugs using BDDCS. Adv Drug Deliv Rev. (in press).
- G. Rossato et al. Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts. ChemMedChem 5, 2088-2101 (2010).
- J. Kirchmair et al. Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr. Med. Chem. 15, 2040-2053 (2008).
- http://www.springerlink.com/content/0920-654x/26/1 (2012)
- Y. Tanrikulu et al. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening. Nat. Rev. Drug Discovery 7, 667-677 (2008).
- W.H. Bisson et al. Modeling of the aryl hydrocarbon receptor (AhR) ligand binding domain and its utility in virtual ligand screening to predict new AhR ligands. J. Med. Chem. 52, 5635-5641 (2009).
- S. Lovera et al. The Different Flexibility of c-Src and c-Abl Kinases Regulates the Accessibility of a Druggable Inactive Conformation. J. Am. Chem. Soc. 134, 2496-2499 (2012).
- R. Lowe et al. Predicting the mechanism of phospholipidosis. J. of Chemoinformatics (in press).
- C. de Graaf et al. Structure-based discovery of allosteric modulators of two related class B G-protein-coupled receptors. ChemMedChem 6, 2159-2161 (2011).
- M. Devereux et al. In silico techniques for the identification of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10, 657-668 (2010).
- G. Schneider. Designing the molecular future. J. Comp. Aided Mol. Design 26, 115-120 (2012).
- W.H. Bisson et al. Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs. PNAS 104, 11927-11932 (2007).