Systems Biology: Applications in pharma research 20 September 2010, TU München Andrea Schafferhans
Andrea Schafferhans @ TU München Similar proteins have similar interaction partners (?) 20 January 2011 Introduction 2
Andrea Schafferhans @ TU München Applications • Function prediction • Drug development – “Target Class” approach – Side effects – “Polypharmacology” / “Network pharmacology” Hopkins,A.L. (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol, 4, 682-690. 20 January 2011 Introduction 3
Andrea Schafferhans @ TU München Contents 1. Introduction 2. Protein comparison – Computational binding site identification – Binding site comparison 3. Application examples 20 January 2011 Introduction 4
Andrea Schafferhans @ TU München Types of protein similarity • Function • Sequence – Paralogs – within species – Orthologs – across species • Binding sites / interaction patterns 20 January 2011 Protein similarity 5
Andrea Schafferhans @ TU München What is a binding site? • Function – Binding other proteins (e.g. signal transduction) – Binding substrates (enzymes) – Binding Co-Factors (e.g. Heme) – … • Form – Cavity in the protein – CAVE: induced fit / conformational selection more realistic • Pragmatic – Around all HETATM records in PDB (CAVE: e.g. metals…) 20 January 2011 Protein similarity 6
Andrea Schafferhans @ TU München Binding site characteristics • Usually a pocket or cleft in the protein • Less hydrophobic than the interior of a protein • Specific through complementarity of – Form – Electrostatic interactions – Hydrogen bonds – Hydrophobic interactions Henrich S, Salo-Ahen OM, Huang B, et al.: Computational approaches to identifying and characterizing protein binding sites for ligand design. Journal of Molecular Recognition 2010, 23 :209-219 20 January 2011 Protein similarity 7
Andrea Schafferhans @ TU München Binding site analysis – Applications • Automated drug target annotation – E.g. estimation of druggability (binding site size, hydrophobicity, etc.) • Virtual screening – Restrict the search space for docking experiments • Function prediction • Prediction of drug side effects 20 January 2011 Protein similarity 8
Andrea Schafferhans @ TU München Finding binding sites – geometrically Observation: Binding sites usually are the largest pockets e.g. 83% of enzyme active sites found in the largest pocket (Laskowski RA, et al. Protein clefts in molecular recognition and function. Protein Sci. 1996; 5 :2438-2452.) 20 January 2011 Protein similarity 9
Andrea Schafferhans @ TU München POCKET • Fill the protein with a grid (3 Å spacing) • Mark grid points as “protein“ (within 3 Å of an atom ) or “solvent“ • Go along grid and mark “solvent” points that lie between “protein” points for potential pocket • Find largest “clusters” of “pocket” points Levitt D, Banaszak L. POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph 1992, 10 :229-234. 20 January 2011 Protein similarity 10
Andrea Schafferhans @ TU München LIGSITE Differences to POCKET • More efficient searching for neighbour atoms • Cubic diagonals also used for finding pockets less dependent on orientation • Grid points scored by the number of times they are found (between 0 and 7) adjustable “buriedness“ • Smaller and adjustable grid spacing (best: 0.5 to 0.75 Å) Hendlich M, et al.: LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J. Mol. Graph. Mod. 1997, 15 :359-363 20 January 2011 Protein similarity 11
Andrea Schafferhans @ TU München Finding binding sites – energetically Binding sites interact with the bound molecules Find location of favourable interaction energies 20 January 2011 Protein similarity 12
Andrea Schafferhans @ TU München GRID • Calculates interaction energies of probe molecules • Uses three terms: – Lennard-Jones (attraction + repulsion) – electrostatic – directional hydrogen bond Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985 28 :849-857 20 January 2011 Protein similarity 13
Andrea Schafferhans @ TU München GRID application • Cluster energy minima binding site • BUT: – Hard to cluster – Computationally intensive • Good for binding site characterisation Picture from: Henrich S, Salo-Ahen OM, Huang B, et al. JMR 2010, 23:209-19. 20 January 2011 Protein similarity 14
Andrea Schafferhans @ TU München Q-SiteFinder • GRID methyl probe (0.9 Å grid) • Cluster: adjacent grid points that meet energy criterion → Success: > 70% first predicted binding site > 90% first three → 68% average precision (precision: overlap between ligand and predicted binding site) Laurie AT, Jackson RM: Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005, 21 :1908-16 20 January 2011 Protein similarity 15
Andrea Schafferhans @ TU München i-Site Variation of Q-Site: • Better probe distribution (more dense grid) • Two energy limits – low value for cluster seeds – higher value for extension filtering out meaningful clusters • AMBER force field Morita M, Nakamura S, Shimizu K: Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures. Proteins 2008, 73 :468-479 20 January 2011 Protein similarity 16
Andrea Schafferhans @ TU München i-Site Variation of Q-Site: • Better probe distribution (more dense grid) • Two energy limits – low value for cluster seeds – higher value for extension filtering out meaningful clusters • AMBER force field Morita M, Nakamura S, Shimizu K: Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures. Proteins 2008, 73 :468-479 20 January 2011 Protein similarity 17
Andrea Schafferhans @ TU München Challenges in binding site identification • Protein flexibility can “hide” binding sites → Use multiple experimental conformations → Use molecular dynamics to generate conformations • Dimerisation has to be considered → Carefully look at PDB unit cell → Carefully look at information about the protein 20 January 2011 Protein similarity 18
Andrea Schafferhans @ TU München Characterising binding sites Properties to characterise: • Geometry • Amino acid composition • Solvation • Hydrophobicity • Electrostatics • Interactions with functional groups 20 January 2011 Protein similarity 19
Andrea Schafferhans @ TU München Hydrophobicity Measured by logP (partitioning between water and octanol) • Map atom / residue based contributions • Calculate interaction energies of hydrophobic probes (e.g. GRID) 20 January 2011 Protein similarity 20
Andrea Schafferhans @ TU München Electrostatics • Map electrostatic potential onto surface (e.g. using DelPhi, see http://structure.usc.edu/ howto/delphi-surface- pymol.html) • CAVE: dependence on protonation! 20 January 2011 Protein similarity 21
Andrea Schafferhans @ TU München Functional groups • Superstar – Analyse the spatial distribution of functional groups in CSD density maps – Break the protein into fragments found in CSD – Map the observed distribution of interaction partners onto the protein Verdonk ML, Cole JC, Taylor R: SuperStar: a knowledge-based approach for identifying interaction sites in proteins. Journal of molecular biology 1999, 289:1093-108. 20 January 2011 Protein similarity 22
Andrea Schafferhans @ TU München Binding site comparison • Align structures in 3D • Analyse differences and similarities of – Amino acid composition – Local conformation – Pocket size – Presence of interaction partners • Straightforward in case of – Sequence similarity or – Structural similarity 20 January 2011 Protein similarity 23
Andrea Schafferhans @ TU München RELIBASE 20 January 2011 Protein similarity 24
Andrea Schafferhans @ TU München RELIBASE • Stores binding sites from PDB structures • Allows superposition of related binding sites • Computes differences between binding sites Hendlich M, Bergner A, Günther J, Klebe G: Relibase: Design and Development of a Database for Comprehensive Analysis of Protein-Ligand Interactions. Journal of Molecular Biology 2003, 326 :607-620. http://relibase.ccdc.cam.ac 20 January 2011 Protein similarity 25
Andrea Schafferhans @ TU München Similar but not homologous binding sites • cAMP-dependent protein kinase (1cdk) with adenyl-imido-triphosphate • trypanothione reductase (1aog) with flavine-adenine-dinucleotide 20 January 2011 Protein similarity 26
Andrea Schafferhans @ TU München Similar but not homologous binding sites Graphics from www.ebi.ac.uk/ pdbsum/ 20 January 2011 Protein similarity 27
Andrea Schafferhans @ TU München Similar but not homologous binding sites Graphics from Schmitt S, Kuhn D, Klebe G. Journal of molecular biolog y 2002, 323 :387-406 20 January 2011 Protein similarity 28
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