Protein Interaction Networks

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Protein Interaction Networks

• Thanks to Mehmet Koyuturk

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Protein-Protein Interactions

• Physical association between proteins
• Signal transduction, phosphorylation
• Docking, complex formation
• Permanent vs. transient interactions
• Co-location of proteins
• Proteins that work in the same cellular component
• Soluble location lysosome, mitochondrial stroma
• Membrane location receptors in plasma membrane,
  transporters in mitochondrial membrane
• Functional association of proteins
• Proteins involved in the same biomolecular
  activity
• Enzymes in the same pathway, co-regulated proteins

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Permanent vs Transient Interactions

• Permanent interactions
• Some proteins form a stable protein complex that
  carries out a structural or functional
  biomolecular role
• These proteins are protein subunits of the
  complex and they work together
• ATPase subunits, subunits of nuclear pore
• Transient interactions
• Proteins that come together in certain cellular
  states to undertake a biomolecular function
• DNA replicative complex, signal transduction

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Signal Transduction

• Phosphorylation
• Protein-kinase interaction
• Enzyme activation

• Signaling cascade

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Why Study Protein Interactions?

• Identification of functional modules and
  interconnections between these modules
• Functional annotation based on binding partners
  and interaction patterns
• Identification of evolutionarily conserved
  pathways
• Identification of drug target proteins to
  minimize side effects

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Identification of Protein Interactions

• Traditionally, protein interactions are
  identified by wetlab experiments based on
  hypotheses on candidate proteins
• Small scale assays
• Coimmunoprecipitation Immunoprecipitate one
  protein, see if other is also precipitated
• Reliable, but can only verify interactions
  between suspected partners
• High throughput screening
• Throw in thousands of ORFs and see which ones
  bind to each other
• Yeast two hybrid, tandem affinity purification
• Large scale, but a lot of noise

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Yeast Two Hybrid

• Split yeast GAL4 gene, which encodes a
  transcription factor, required for activation of
  GAL genes in two parts
• Activating domain, binding domain
• The split protein does not work unless the two
  parts are in physical contact

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Protein Interaction Networks

• Organize all identified interactions in a
  network, where proteins are represented by nodes
  and interactions are represented by edges
• TAP identifies a group of proteins that are
  caught by target protein
• Spoke model (star network) vs. matrix model
  (clique)

Interaction
Protein
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Functional Modularity in PPI Networks

• A protein complex
• Dense subgraph
• A signal transduction pathway
• Simple path, parallel paths
• A protein with common, key,
• fundamental role (e.g., a kinase)
• Hub node

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Computational Prediction of PPIs

• Functional association is a higher level
  conceptualization of interaction
• Proteins that act as enzymes catalyzing reactions
  in the same metabolic pathway
• Functionally associated proteins are likely to
  show up in similar contexts
• Co-regulation, co-expression, co-evolution,
  co-citation
• Functional association between proteins can be
  computationally identified by looking at
  different sources of data such as sequences, gene
  expression, literature
• Can also be extended to capture physical
  associations, for example, by taking into account
  evolution at structural level

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Conservation of Gene Neighborhood

• In bacteria, the genome of an organism is
  organized in such a way that that functionally
  related proteins are coded by neighboring regions
• Operons
• When more than one bacterial species are
  considered, it is observed that this neighborhood
  relationship becomes even more relevant

Distribution of neighboring genes in H.
Influenzae and E. coli into functional classes
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Comparison of Nine Bacterial Genomes

• trpB-trpA is the only gene pair whose proximity
  is conserved across nine prokaryotic genomes
• These genes encode the two subunits of tryptophan
  synthase that interact and catalyze a single
  reaction

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Close Orthologs

• Run of genes
• A set of genes on one strand, such that gaps
  between adjacent genes is less than a threshold,
  g (in practice, g ? 300 bp)
• Any pair of genes on the same run are said to be
  close
• Bidirectional best hits
• Genes X1 and X2 from genomes G1 and G2 are BBH,
  if their sequence similarity is significant and
  there are no Y1 (Y2) in G1(G2) that is more
  similar to X2 (X1) than X1 (X2)

Pair of close bidirectional best hits Xa, Ya
close in G1, Xb, Yb close in G2, XaXb BBH, Ya
Yb BBH
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Predicting Interactions

• For each pair of close orthologs (occuring at
  least one pair of genomes), calculate a score
• Score should increase with the phylogenetic
  distance between the two genomes, since closely
  related organisms are more likely to have similar
  genes nearby due to chance alone
• Existence of a triplet (P1, P2, P3) should be
  stronger than the existence of two pairs (P1, P2
  and P1, P3)
• Triplet distance can be estimated as the minimum
  distance between any pair of organisms (in
  addition to pair score)

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Reconstructing Pathways
Purine Metabolism

• Can identify the association between unknown
  proteins and known pathways!

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Projection of Gene Neighborhood

• The composition of operons is evolutionarily
  variable
• A particular set of functionally related genes do
  not always comprise an operon
• The application of gene neighborhood based
  interaction prediction is limited for a single
  organism
• With multiple organisms, it is possible to
  statistically strengthen conclusions and project
  findings on other organisms
• If an operon with functionally related genes
  exists in several genomes, a functional
  association can be predicted for other organisms,
  even if the corresponding genes are scattered
• Variability turns out to be an advantage for
  prediction

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Gene Neighborhood - Limitations

• It is only directly applicable to bacteria (and
  archaea), because relevance of gene order does
  not necessarily extend to eukaryotes
• For closely related species, conserved gene order
  might just be due to lack of time for genome
  rearrangements
• We are interested in selective constraints that
  preserve gene order
• Compared species should be distant enough
• But not too distant, because we need sufficient
  number of orthologs to be able to derive
  statistically meaningful results

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Gene Fusion

• Domain fusion events
• Two protein domains that act as independent
  proteins (components) in one organism may form
  (part of) a single polypoptide chain (composites)
  in another organism
• Most proteins that are involved in domain fusion
  events are known to be subunits of multiprotein
  complexes (76 in E. coli metabolic network)

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Gene Fusion Based PPI Prediction

• A pair of proteins in query genome are candidate
  interacting pairs if
• They show (local) sequence similarity to the same
  protein (rosetta stone) in reference genome
• They do now show sequence similarity with each
  other
• Complete genomes!

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Predicted Interactions
Known physical interactions
Proteins in the same pathway
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Gene Fusion Based Prediction - Results

• Interactions predicted based on gene fusion
  events
• Distance on circle shows distance on genome

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Co-evolution of Interacting Proteins

• Selective pressure is likely to act on common
  function
• Proteins that are interacting are expected to
  either be conserved together along with their
  interactions, or not conserved at all
• Hypothesis 1 Orthologs of interacting proteins
  also interact in other species (supported by
  evidence, but there are subtleties, which we will
  discuss this later)
• Hypothesis II If two proteins are
• interacting, then they will show
• similar conservation patterns
• Phylogenetic profiles

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Phylogenetic Profiles
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Correlation of Phylogenetic Profiles

• Assume we have N genomes, protein X has homologs
  in x of them, Y has y, and they co-occur in z
  genomes
• Hamming distance
• Pearson correlation
• Mutual information
• Statistical significance

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Phylogenetic Profiles - Limitations

• Many processes may be common across lineages
• Too many false positives
• Database of genomes may be biased
• All organisms are treated equally
• Improvement Use trees instead of profiles
• Proteins are assumed to be conserved as a whole
• It is domains that interact
• Improvement Use domain profiles

Yeast nucleoli and ribosomal proteins
Organisms
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Phylogenetic Tree Based Prediction

• Phylogenetic trees of Ntr-family two-component
  sensor histidine kinases and their corresponding
  regulators

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Mirror Tree Method

• Need to have sufficient number of genomes that
  contain homologs of both proteins

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Matrix Method

• Start with families of proteins that are
  suspected to interact
• Identify specific pairs of proteins that interact
  by aligning the phylogenetic trees that underly
  the two families
• Assumption Identical number of proteins in each
  family

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Correlated Mutations

• Co-evolution of interacting proteins can be
  followed more closely by quantifying the degree
  of co-variation between pairs of residues from
  these proteins
• Correlated mutations may correspond to
  compensatory mutations that stabilize the
  mutations in one protein with changes in the
  other

Distribution of distances between aminoacid
positions on a folded protein
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In silico Two-Hybrid

• The correlation of mutations between two
  positions (may be on different proteins) can be
  estimated from pairwise assessment of aligned
  multiple sequences
• Position pairs with high correlation are
  potential contact points
• Interaction index
• For a protein pair, compute the aggregate
  correlation (of mutations) across all positions

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In silico Two-Hybrid
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Performance of I2H

• I2H predicts physical, rather than functional
  association
• It requires complete genomes sufficient number
  of homologs

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Co-citation Based PPI Prediction

• Functionally associated proteins are likely to be
  cited in the same research article
• We can assess the statistical significance of
  co-citation based on hypergeometric model
• Algorithmic problem How to recognize match
  protein names?
• Train algorithm using annotated abstracts via
  conditional random fields (CRF)

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Performance of Co-citation

• Statistical significance is quite relevant until
  it saturates

• The method is robust to choice of parameters for
  name recognition

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Integrating PPI Networks

• Interaction data coming from multiple sources
• Different sources refer to different levels of
  interaction
• Can integration handle noise, making interaction
  data more reliable?
• Superpose interactions based on their reliability

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Bayesian Integration

• For each prediction method, compute
  log-likelihood score
• Let P(LE) be the number of interactions
  predicted by method E, such that functional
  association between corresponding proteins is
  known
• Let P(LE) be the number of false positives
• Let P(L) and P(L) be the corresponding priors
• Assign weights to methods based on their
  log-likelihood scores

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Comparison of Prediction Methods

• Integrated network captures functional
  association better
• Note that the integrated network is trained
  using available data on functional association

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Classification Based Integration

• Points Proteins, Space Expression,
  Conservation, Labels Function
• Points Protein Pairs, Space Co-expression,
  Co-evolution, etc., Labels Existence of
  Interaction

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Performance of Domain Co-evolution
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Co-Evolutionary Matrix
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Domain Identification
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Difference between Predicted PPIs