Binding site analysis: Applications in pharma research · identifying and characterizing protein...
Transcript of Binding site analysis: Applications in pharma research · identifying and characterizing protein...
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Binding site analysis: Applications in
pharma research
28 June 2011, TU München
Andrea Schafferhans
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Types of protein similarity
• Function • Sequence
– Paralogs – within species
– Orthologs – across species
• Binding sites / interaction patterns
20 January 2011 2 Introduction
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Similar proteins have similar interaction partners
(?)
20 January 2011 Introduction 3
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Evidence: Analysing target relationships
Nodes: proteins Edges: similar binding
(within factor 103)
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Paolini,G.V. et al. (2006) Global mapping of pharmacological space. Nature biotechnology, 24, 805-15.
Introduction
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Evidence (2): Analysing target relationships
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Paolini,G.V. et al. (2006) Global mapping of pharmacological space. Nature biotechnology, 24, 805-15.
Introduction
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Applications
• Function prediction • Drug development
– “Target Class” approach – Side effects – “Polypharmacology” / “Network pharmacology”
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Hopkins,A.L. (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol, 4, 682-690.
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Contents
1. Introduction 2. Protein comparison
– Computational binding site identification – Binding site comparison
3. Application examples
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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…)
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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
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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
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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.)
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• 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.
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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
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Finding binding sites – energetically
Binding sites interact with the bound molecules Find location of favourable interaction energies
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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
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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.
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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
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i-Site
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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
Binding site identification
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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
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Characterising binding sites
Properties to characterise: • Geometry • Amino acid composition • Solvation • Hydrophobicity • Electrostatics • Interactions with functional groups
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Hydrophobicity
Measured by logP (partitioning between water and octanol) • Map atom / residue based
contributions • Calculate interaction
energies of hydrophobic probes (e.g. GRID)
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Electrostatics
• Map electrostatic potential onto surface (e.g. using DelPhi, see http://structure.usc.edu/howto/delphi-surface-pymol.html)
• CAVE: dependence on protonation!
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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.
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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
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RELIBASE
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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
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• cAMP-dependent protein kinase (1cdk) with adenyl-imido-triphosphate
• trypanothione reductase (1aog) with flavine-adenine-dinucleotide
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Similar but not homologous binding sites
Binding site comparison
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Similar but not homologous binding sites
Graphics from www.ebi.ac.uk/pdbsum/
Binding site comparison
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Similar but not homologous binding sites
Graphics from Schmitt S, Kuhn D, Klebe G. Journal of molecular biology 2002, 323:387-406
Binding site comparison
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Problems in binding site comparison
• Automatically locate binding site • Capture important features in efficient representation • Search efficiently across all structures
– Find best superimposition – Score the alignment
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Binding site comparison methods • Representation by
– Coordinate set with physico-chemical or evolutionary properties • Atoms • Chemical groups • Surface points
– 3D shape descriptors • Superimposition by
– Geometric hashing – Graph theory, clique search
• Similarity measurement by – RMSD – Residue conservation – Physico-chemical property similarity
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CavBase – Structure representation • Cavity detection with LIGSITE (stored in Relibase)
• Cavity-flanking residues represented as pseudo-centers: – Donor – Acceptor – Donor-Acceptor – Aliphatic – PI – several per residue if necessary
• Create Graph: – Nodes: pseudo-centers – Edges: distances between the pseudo-centres
Graphics from Schmitt S, Kuhn D, Klebe G. Journal of molecular biology 2002, 323:387-406
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CavBase – Alignment Create associated graph:"
Node: ""node from protein A and node from protein B with similar interaction properties"
Edge:""member nodes in protein A and B are connected member node distance <12Å distance difference <2Å
Find maximal common subgraph (Bron-Kerbosh) similar arrangement of pseudo-centers in original graphs 20 January 2011 33 Binding site comparison
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CavBase – Scoring • Scoring based on
overlap of similarly typed surface patches
Kuhn D, Weskamp N, Schmitt S, Hüllermeier E, Klebe G: From the Similarity Analysis of Protein Cavities to the Functional Classification of Protein Families Using Cavbase. Journal of Molecular Biology 2006, 359:1023-1044
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SOIPPA – Structure representation
• Delaunay tesselation of Cα atoms -> 1 tetrahedron/Cα
• Environmental boundary (red) and protein boundary (blue)
Bourne PE, Xie L: A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites. BMC Bioinformatics 2007, 8:S9. Bourne PE, Xie L: A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics 2009, 25:i305-312.
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SOIPPA – Structure representation (2)
• Each Cα characterized by – Vector with distance and direction
of boundaries – Substitution matrix
• Graph: Node: Cα Edge: connection of tetrahedra
Xie L., Bourne PE. Bioinformatics 2009, 25:i305-312.
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SOIPPA - Alignment Create associated graph:"
Node: ""node(A) + node(B) with similar geometric potential ""weight: amino acid frequency profile similarity"
Edge:""member nodes in protein A and B are connected""distance difference <2Å surface normal difference <30°
Find maximum-weight common subgraph (MWCS)
Xie L., Bourne PE. Bioinformatics 2009, 25:i305-312.
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SOIPPA – Scoring • Sum over aligned residue pairs:
Residue similarity "weighted by distance
and normal vector angle
• Statistical significance of score Background score distribution: – compare unrelated structures with random sequences – fit resulting score distribution to extreme value distribution function giving probability of randomness dependent on score
!
Sij = (Mij " paij " pdij )i, j#
Xie L., Bourne PE. Bioinformatics 2009, 25:i305-312.
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Isocleft • Structure representation: Cα / atoms within 5 Å of ligand • Alignment: Bron-Kerbosh of associated graph
• Scoring:
Najmanovich R, Kurbatova N, Thornton J: Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites. Bioinformatics 2008, 24:i105 http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/icfdb/StartPage.pl
!
S =NC
NA + NB " NC
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Isocleft - innovations • Two iterations of alignment:
1. Nodes: Cα atoms, Edges: distance difference <3.5 Å, minimal residue similarity Superimpose based on found graph
2. Nodes: all heavy atoms, Edges: distance <4 Å, similar atom type (hydrophilic, acceptor, donor, hydrophobic, aromatic, neutral, neutral-donor and neutral-acceptor)
• Use first result of Bron-Kerbosch, then terminate
Najmanovich R, Kurbatova N, Thornton J: Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites. Bioinformatics 2008, 24:i105
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Example 1: Explaining side effects
Problem: side effects of ERα modulators (SERMs)
Finding “off target” effects: • Map sequences to structures (BLAST) • Limit to “druggable” proteins (?) • Search with SOIPPA => SERCA (SarcoplasmicReticulum
Ca2+ channel ATPase)
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Xie L, Wang J, Bourne PE (2007) In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators. PLoS Comput Biol 3(11)
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Example 1: Validating results
• Inverse search
• Docking – SERM – similar compounds, correlate (?)
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Example 2: Repositioning known drug
Problem: new tuberculosis drugs needed, but many parameters to optimise
Finding compound to reuse against InhA: • Search other structures binding Adenine
(ATP, ADP, NAD, FAD, ...) • Compare binding sites with SOIPPA => SAM-dependent methyltransferases
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Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, et al. (2009) Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis. PLoS Comput Biol 5(7)
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Example 2: Structure match
catechol-O-methyltransferase (COMT), SAM, inhibitor InhA, NAD, ligand
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Summary
Pharma research focus moving from only individual interactions to system oriented research
Challenges: • How to compare? • Computational overhead
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