Systems Biology: Applications in pharma research · • Drug development – “Target Class”...

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


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.


Contents

1.  Introduction 2.  Protein comparison

–  Computational binding site identification –  Binding site comparison

3.  Application examples



Types of protein similarity

•  Function

•  Sequence –  Paralogs – within species

–  Orthologs – across species

•  Binding sites / interaction patterns

20 January 2011 Protein similarity 5


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…)



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



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



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.)



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.



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



Finding binding sites – energetically

Binding sites interact with the bound molecules Find location of favourable interaction energies



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



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.



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



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


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



Characterising binding sites

Properties to characterise: •  Geometry •  Amino acid composition •  Solvation •  Hydrophobicity •  Electrostatics •  Interactions with functional groups



Hydrophobicity

Measured by logP (partitioning between water and octanol) •  Map atom / residue based

contributions •  Calculate interaction

energies of hydrophobic probes (e.g. GRID)



Electrostatics

•  Map electrostatic potential onto surface (e.g. using DelPhi, see http://structure.usc.edu/howto/delphi-surface-pymol.html)

•  CAVE: dependence on protonation!



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.



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



RELIBASE



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



•  cAMP-dependent protein kinase (1cdk) with adenyl-imido-triphosphate

•  trypanothione reductase (1aog) with flavine-adenine-dinucleotide


Similar but not homologous binding sites




Graphics from www.ebi.ac.uk/pdbsum/




Graphics from Schmitt S, Kuhn D, Klebe G. Journal of molecular biology 2002, 323:387-406


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



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



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



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



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



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.



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.



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)




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∑




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



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



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)

20 January 2011 Application examples 40

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)


Example 1: Validating results

•  Inverse search

•  Docking –  SERM –  similar compounds, correlate (?)


Graphics from Xie L, Wang J, Bourne PE (2007) PLoS Comput Biol 3(11)


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


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)


Example 2: Structure match


Graphics from Kinnings SL et al. (2009) PLoS Comput Biol 5(7)


Example 3: Analysing target relationships

Nodes: proteins Edges: similar binding

(within factor 103)


Paolini,G.V. et al. (2006) Global mapping of pharmacological space. Nature biotechnology, 24, 805-15.


Example 3: Analysing target relationships


Paolini,G.V. et al. (2006) Global mapping of pharmacological space. Nature biotechnology, 24, 805-15.


Summary

Pharma research focus moving from only individual interactions to system oriented research

Challenges: •  How to compare? •  Computational overhead

20 January 2011 Summary 46

Systems Biology: Applications in pharma research · • Drug development – “Target Class”...

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