Computational Biology Signaling Networks Drug Repositioning

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Lars Juhl Jensen

The document discusses computational biology approaches for analyzing signaling networks and applying them to drug repositioning. It describes using text mining of literature, integrating diverse datasets on protein interactions and genomic context, developing methods to map kinase-substrate networks from sequence motifs, and applying these networks along with side effect similarity to identify new uses for existing drugs. Validation experiments confirmed several predicted drug-target relationships.

Computational Biology Signaling networks and drug repositioning Lars Juhl Jensen

sequence analysis

Jensen, Gupta et al., Journal of Molecular Biology , 2002

data integration

de Lichtenberg, Jensen et al., Science , 2005

text mining

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009

human variation

signaling networks

phosphoproteomics

in vivo phosphosites

kinases are unknown

sequence motifs

Miller, Jensen et al., Science Signaling , 2008

NetPhorest

data organization

Miller, Jensen et al., Science Signaling , 2008

automated pipeline

Miller, Jensen et al., Science Signaling , 2008

compilation of datasets

training and evaluation

motif atlas

179 kinases

89 SH2 domains

8 PTB domains

BRCT domains

WW domains

14-3-3 proteins

sequence specificity

kinase-specific

in vitro

network context

Linding, Jensen, Ostheimer et al., Cell , 2007

STRING

Jensen, Kuhn et al., Nucleic Acids Research , 2009

630 genomes

2.5 million proteins

genomic context

gene fusion

Korbel et al., Nature Biotechnology , 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology , 2004

primary experimental data

physical interactions

Jensen & Bork, Science , 2008

gene coexpression

curated knowledge

Letunic & Bork, Trends in Biochemical Sciences , 2008

literature mining

not comparable

confidence scores

von Mering et al., Nucleic Acids Research , 2005

cross-species integration

Linding, Jensen, Ostheimer et al., Cell , 2007

putting it all together

NetworKIN

Linding, Jensen, Ostheimer et al., Cell , 2007

>2x better accuracy

use case

DNA damage response

Linding, Jensen, Ostheimer et al., Cell , 2007

experimental validation

ATM phosphorylates Rad50

Linding, Jensen, Ostheimer et al., Cell , 2007

drug repositioning

new uses for old drugs

drug–drug network

shared target(s)

chemical similarity

Tanimoto coefficients

Campillos & Kuhn et al., Science , 2008

Campillos & Kuhn et al., Science , 2008

similar drugs share targets

only trivial predictions

phenotypic similarity

chemical perturbations

phenotypic readouts

drug treatment

side effects

no database

package inserts

Campillos & Kuhn et al., Science , 2008

text mining

side-effect ontology

backtracking

Campillos & Kuhn et al., Science , 2008

side-effect correlations

Campillos & Kuhn et al., Science , 2008

GSC weighting

side-effect frequencies

Campillos & Kuhn et al., Science , 2008

raw similarity score

Campillos & Kuhn et al., Science , 2008

p-values

Campillos & Kuhn et al., Science , 2008

side-effect similarity

chemical similarity

Campillos & Kuhn et al., Science , 2008

confidence scores

drug–drug network

Campillos & Kuhn et al., Science , 2008

categorization

Campillos & Kuhn et al., Science , 2008

experimental validation

20 drug–drug relations

in vitro binding assays

K i <10 µM for 11 of 20

cell assays

9 of 9 showed activity

summary

computational biology

network analysis

testable predictions

save much time in the lab

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larsjuhljensen

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Computational Biology Signaling Networks Drug Repositioning

1. Computational Biology Signaling networks and drug repositioning Lars Juhl Jensen

2.

3.

4. sequence analysis

5. Jensen, Gupta et al., Journal of Molecular Biology , 2002

6.

7.

8. data integration

9. de Lichtenberg, Jensen et al., Science , 2005

10.

11.

12. text mining

13. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009

14. human variation

15.

16. signaling networks

17. phosphoproteomics

18.

19. in vivo phosphosites

20. kinases are unknown

21. sequence motifs

22. Miller, Jensen et al., Science Signaling , 2008

24. data organization

25. Miller, Jensen et al., Science Signaling , 2008

26. automated pipeline

27. Miller, Jensen et al., Science Signaling , 2008

28. compilation of datasets

29. training and evaluation

30. motif atlas

31.

32. 179 kinases

33. 89 SH2 domains

34. 8 PTB domains

35. BRCT domains

37. 14-3-3 proteins

38. sequence specificity

39. kinase-specific

41. network context

42. Linding, Jensen, Ostheimer et al., Cell , 2007

44. Jensen, Kuhn et al., Nucleic Acids Research , 2009

45. 630 genomes

46. 2.5 million proteins

47. genomic context

48. gene fusion

49. Korbel et al., Nature Biotechnology , 2004

50. phylogenetic profiles

51. Korbel et al., Nature Biotechnology , 2004

52. primary experimental data

53. physical interactions

54. Jensen & Bork, Science , 2008

55. gene coexpression

56.

57. curated knowledge

58. Letunic & Bork, Trends in Biochemical Sciences , 2008

59. literature mining

60.

61. not comparable

62. confidence scores

63. von Mering et al., Nucleic Acids Research , 2005

64. cross-species integration

65. Linding, Jensen, Ostheimer et al., Cell , 2007

66. putting it all together

68. Linding, Jensen, Ostheimer et al., Cell , 2007

69. >2x better accuracy

71. DNA damage response

72. Linding, Jensen, Ostheimer et al., Cell , 2007

73. experimental validation

74. ATM phosphorylates Rad50

75. Linding, Jensen, Ostheimer et al., Cell , 2007

76. drug repositioning

77. new uses for old drugs

78. drug–drug network

79. shared target(s)

80. chemical similarity

81. Tanimoto coefficients

82. Campillos & Kuhn et al., Science , 2008

83. Campillos & Kuhn et al., Science , 2008

84. similar drugs share targets

85. only trivial predictions

86. phenotypic similarity

87. chemical perturbations

88. phenotypic readouts

89. drug treatment

90. side effects

91. no database

92. package inserts

93. Campillos & Kuhn et al., Science , 2008

94. text mining

95. side-effect ontology

96. backtracking

97. Campillos & Kuhn et al., Science , 2008

98. side-effect correlations

99. Campillos & Kuhn et al., Science , 2008

100. GSC weighting

101. side-effect frequencies

102. Campillos & Kuhn et al., Science , 2008

103. raw similarity score

104. Campillos & Kuhn et al., Science , 2008

106. Campillos & Kuhn et al., Science , 2008

107. side-effect similarity

108. chemical similarity

109. Campillos & Kuhn et al., Science , 2008

110. confidence scores

111. drug–drug network

112. Campillos & Kuhn et al., Science , 2008

113. categorization

114. Campillos & Kuhn et al., Science , 2008

115. experimental validation

116. 20 drug–drug relations

117. in vitro binding assays

118. K i <10 µM for 11 of 20

119. cell assays

120. 9 of 9 showed activity

122. computational biology

123. network analysis

124. testable predictions

125. save much time in the lab

126.

127. larsjuhljensen

Hinweis der Redaktion

Integration Automation Collaboration
Atlas of human kinases Atlases for phospho-binding proteins Atlases for model organisms Ubiquitination would be welcome