Bioinformatics I. CPBS 7711 October 29, 2015 Protein interaction networks. Debra Goldberg

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1 Bioinformatics I CPBS 7711 October 29, 2015 Protein interaction networks Debra Goldberg debra@colorado.edu

2 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

3 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

4 Introduction to Networks

5 What is a network? A collection of objects (nodes, vertices) Binary relationships (edges) May be directed Also called a graph Networks are everywhere!

6 Social networks Nodes: People Edges: Friendship from

7 Sexual networks Nodes: People Edges: Romantic and sexual relations

8 Transportation networks Nodes: Locations Edges: Roads

9 Power grids Nodes: Power station Edges: High voltage transmission line

10 Airline routes Nodes: Airports Edges: Flights

11 Internet Nodes: MBone Routers Edges: Physical connection

12 World-Wide-Web Nodes: Web documents Edges: Hyperlinks

13 Quick activity What kinds of biological networks are there or might there be? Molecular biology

14 Gene and protein networks

15 Metabolic networks Nodes: Metabolites Edges: Biochemical reaction (enzyme) from web.indstate.edu

16 Signaling networks Nodes: Molecules (e.g., Proteins or Neurotransmitters) Edges: Activation or Deactivation from

17 Gene regulatory networks Nodes: Genes or gene products Edges: Regulation of expression Inferred from error-prone gene expression data from Wyrick et al. 2002

18 Disease Networks Nodes: Diseases Edges: Common genes SARS, progresssion_of Myocardial infarction Alzheimer disease Obesity Hypertension Rheumatoid arthritis from Goh et al., PNAS 2007 HIV

19 Disease Gene Networks Nodes: Genes Edges: Common diseases from Goh et al., PNAS 2007

20 Protein interaction networks Nodes: Proteins Edges: Observed interaction Gene function predicted from

21 Synthetic sick or lethal networks (SSL) X X X X Y Y Y Y Cells live (wild type) Cells live Cells live Cells die or grow slowly from Tong et al Nodes: Nonessential genes Edges: Genes co-lethal Gene function, drug targets predicted

22 Other gene networks Homology edges Sometimes used to connect other network types across species Coexpression Edges: transcribed at same times, conditions Gene knockout / knockdown Edges: similar phenotype (defects) when suppressed

23 What they really look like We need models!

24 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

25 Traditional graph modeling from GD2002 Random Regular Erdos-Renyi (1960) Lattice

26 Network Research Renaissance Change in direction of network research: 1998 Four factors Theoretical analysis coupled with empirical evidence Networks are not static, they evolve over time Dynamical systems modeling real-world behaviors Computing power! Enables large system analysis

27 Introduce small-world networks Small-World Networks

28 Small-world Networks Six degrees of separation friends each Six steps: But We live in communities

29 Small-world measures Typical separation between two vertices Measured by characteristic path length (average distance) Cliquishness of a typical neighborhood Measured by clustering coefficient v C v = 1.00 v C v = 0.33

30 Watts-Strogatz small-world model

31 Measures of the W-S model Path length drops faster than cliquishness Wide range of p has both small-world properties

32 Small-world measures of various graph types Cliquishness Characteristic Path Length Regular graph High Long Random graph Low Short Small-world graph High Short

33 Another network property: Degree distribution P (k) The degree (notation: k) of a node is the number of its neighbors The degree distribution is a histogram showing the frequency of nodes having each degree

34 Degree distribution of E-R random networks Erdös-Rényi random graphs Binomial degree distribution, well-approximated by a Poisson P(k) Network figures from Strogatz, Nature 2001 Degree = k

35 Degree distribution of many realworld networks Scale-free networks Degree distribution follows a power law P (k = x) = α x -β P(k) log P(k) log k Degree = k

36 Other degree distributions Amaral, Scala, et al., PNAS (2000)

37 Hierarchical Networks Ravasz, et al., Science

38 Properties of hierarchical networks 1. Scale-free 2. Clustering coefficient independent of N 3. Scaling clustering coefficient (DGM) 38

39 C of 43 metabolic networks Independent of N Ravasz, et al., Science

40 Clustering coefficient scaling C(k) Metabolic networks Ravasz, et al., Science

41 Summary of network models Random Small world Scale-free Hierarchical Poisson degree distribution high CC, short pathlengths power law degree distribution high CC, modular, power law degree distribution

42 Many real-world networks are small-world, scale-free World-wide-web Collaboration of film actors (Kevin Bacon) Mathematical collaborations (Erdös number) Power grid of US Syntactic networks of English Neuronal network of C. elegans Metabolic networks Protein-protein interaction networks

43 So What?

44 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

45 There is information in a gene s position in the network We can use this to predict Relationships Interactions Regulatory relationships Protein function Process Complex / molecular machine

46 Implications from topology

47 Edges indicate function Proteins that are connected by an edge in many types of biological networks are more likely to have a common function

48 Adjacent edges indicate 3 rd In some biological networks, if gene A is connected both to genes B and C, then gene B is more likely to be connected to gene C

49 False positives, false negatives Can use topology to assess confidence if true edges and false edges have different network properties Assess how well each edge fits topology of true network Can also predict unknown relations

50 SSL hubs might be good cancer drug targets Normal cell Cancer cells w/ random mutations Alive Dead Dead (Tong et al, Science, 2004)

51 2-hop predictors for SSL SSL SSL (S-S) Homology SSL (H-S) Co-expressed SSL (X-S) Physical interaction SSL (P-S) 2 physical interactions (P-P) v w S: Synthetic sickness or lethality (SSL) H: Sequence homology X: Correlated expression P: Stable physical interaction Wong, et al., PNAS 2004

52 Multi-color motifs S: Synthetic sickness or lethality H: Sequence homology X: Correlated expression P: Stable physical interaction R: Transcriptional regulation Zhang, et al., Journal of Biology 2005

53 Protein complexes Tightly connected proteins may indicate a protein complex from Girvan and Newman, PNAS 2002

54 Beware of bias

55 Lethality Hubs are more likely to be essential Jeong, et al., Nature 2001

56 Protein abundance Abundant proteins are more likely to be represented in some types of experiments More likely to be essential Correlation between degree (hubs) and essentiality disappears or is reduced when corrected for protein abundance Bloom and Adami, BMC Evolutionary Biology 2003

57 Degree anti-correlation Few edges directly between hubs Edges between hubs and low-degree genes are favored Regulatory NW PPI Maslov and Sneppen, Science 2002

58 Degree correlation Anti-correlation of degrees of interacting proteins disappears in un-biased data average degree K essential non-essential degree k Coulomb, et al., Proceedings of the Royal Society B 2005

59 Predicting protein function

60 Methods: predicting function Homology Machine Learning Graph-theoretic methods Direct methods Module-assisted methods Review: Sharan, Ulitsky, Shamir. Molecular Systems Biology, 2007

61 Direct methods: Neighborhood Majority method Schwikowski, Uetz, et al., Nat Biotechnol 18, 2000 Neighborhood method How does frequency affect assignment? Hishigaki, Nakai, et al., Yeast 18, 2001

62 Minimum cut (graph-theoretic) methods Vazquez, Flammini, et al. (2003) globally tries to minimize the number of protein interactions between different annotations Karaoz, Murali, et al. (2004) incorporates gene-expression data for better performance Nabieva, Jim, et al. (2005) reformulated as an integer linear programming problem

63 Functional flow Nabieva, Jim, et al., Bioinformatics 21 Suppl 1, 2005

64 A Markov random field method Letovsky and Kasif, Bioinformatics 19 Suppl 1, 2003 Derive marginal probabilities given other proteins putative assignment Statistically, neighbors often share label Applies p(l N, k) = p(k L,N) p(l) p(k N) iteratively to propagate probabilities L is a Boolean random variable that indicates whether or not a node has that label N is the number of neighbors k is the number of neighbors with that label

65 Module-assisted methods Spirin and Mirny, PNAS 2003 Find fully connected subgraphs (cliques), OR Find subgraphs that maximize density: 2m/(n(n 1)) Bader and Hogue, BMC Bioinformatics 2003 Weight vertices: neighborhood density, connectedness Find connected communities with high weights MCODE : Molecular COmplex DEtection Girvan and Newman, PNAS 2002 Betweenness centrality Removes edges likely to go between communities

66 Confidence assessment, edge prediction

67 Confidence assessment Traditionally, biological networks determined individually High confidence Slow New methods look at entire organism Lower confidence ( 50% false positives) Inferences made based on this data

68 Confidence assessment Can use topology to assess confidence if true edges and false edges have different network properties Assess how well each edge fits topology of true network Can also predict unknown relations Goldberg and Roth, PNAS 2003

69 Use clustering coefficient, a local property Number of triangles = N(v) N(w) y x v v w. w Normalization factor? N(x) = the neighborhood of node x

70 Mutual clustering coefficient (MCC) Jaccard Index: Meet / Min: Geometric: N(v) N(w) N(v) N(w) N(v) N(w) min ( N(v), N(w) ) N(v) N(w) N(v) N(w) Hypergeometric: a p-value

71 Prediction A v-w edge would have a high MCC v w

72 Questions Degree distribution? Clustering coefficient? 2, 5, 9 Mutual clustering coefficient: 2 & 7 Use Meet/Min definition 60

73 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

74 Protein Complexes Groups of proteins that bind together to perform a specific task. Examples: Ribosomes Proteasomes Replication complexes GINS complex, DNA polymerase Image from: Computation site for bioinformatics at Charité, Universitätsmedizin Berlin Found at

75 Finding protein compexes Dense regions may be an indication of a protein complex from Girvan and Newman, PNAS 2002

76 Protein-Protein Interaction Network Image from Yeast Proteomics, Genome News Network,

77 Looking for Complexes One goal of studying the interaction network is to discover previously unknown protein complexes. Methods: Look for cliques or near cliques Look for vertices with high clustering

78 Community structure: Partitioning methods

79 Community structure Proteins in a community may be involved in a common process or function from Girvan and Newman, PNAS 2002

80 Finding the communities Hierarchical clustering Betweenness centrality Dense subgraphs Similar subgraphs Spectral clustering Party and date hubs

81 Hierarchical clustering (1) Using natural edge weights Gene co-expression e.g., Eisen MB, et al., PNAS 1998 from

82 Hierarchical clustering (2) Topological overlap A measure of neighborhood similarity l i,j is 1 if there is a direct link between i and j, 0 otherwise Ravasz, et al., Science 2002

83 Hierarchical clustering (3) Adjacency vector Function cluster: Tong et al., Science 2004 Find drug targets: Parsons et al., Nature Biotechnology 2004

84 Party and date hubs Protein interaction network Partition hubs by expression correlation of neighbors Han, et al., Nature 2004

85 Network connectivity Scale-free networks are: Robust to random failures Vulnerable to attacks on hubs Removing hubs quickly disconnects a network and reduces the size of the largest component Albert, et al., Nature 2000

86 Removing date hubs shatters network into communities Date Hubs Many sub-networks A single main component

87 Similar subgraphs Across species Interaction network and genome sequence e.g., Ogata, et al., Nucleic Acids Research 2000

88 Betweenness centrality Consider the shortest path(s) between all pairs of nodes Betweenness centrality of an edge is a measure of how many shortest paths traverse this edge Edges between communities have higher centrality Girvan, et al., PNAS 2002

89 Spectral clustering Compute adjacency matrix eigenvectors Each eigenvector defines a cluster: Proteins with high magnitude contributions Bu, et al., Nucleic Acids Research 2003 positive eigenvalue negative eigenvalue

90 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

91 Questions How does the WWW evolve? How might protein interaction networks (PINs) evolve? How can we determine if our model is incorrect?

92 Model for scale-free networks Growth and preferential attachment New node has edge to existing node v with probability proportional to degree of v Biologically plausible?

93 Gene duplication gives functional diversity A primary mechanism for diversity After duplication, 2 routes to diversity: Subfunctionalization: function loss yields complementary subsets of original functions Edge Loss Neofunctionalization: de novo acquisition of functions Edge Gain Protein interactions are convenient proxy for functions

94 Gene duplication in a PIN Barabási and Oltvai, Nature Reviews Genetics (2004)

95 Another scale-free network model Duplication and divergence New nodes are copies of existing nodes Same neighbors, then some gain/ loss Solé, Pastor-Satorras, et al. (2002)

96 Advantages of this model This model generates networks that are: scale-free highly clustered PINs are also scale-free, highly clustered

97 Question Paralogs: x & w or y & t y x w v t

98 Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs Discovering protein complexes PIN evolution Final words

99 Final words Network analysis has become an essential tool for analyzing complex systems There is still much biologists can learn from scientists in other disciplines There is much other scientists can learn from us An exciting new direction