The document discusses the systematic discovery of phosphorylation networks by combining linear motifs and protein interactions, highlighting the role of kinases and the importance of context for understanding phosphorylation sites. It outlines methods such as mass spectrometry, gene integration, and various computational approaches for interaction prediction, resulting in thousands of predictions related to kinases and substrates. The future of this research includes improved data organization, automatic pipelines, and further exploration of signaling pathways.

1. Systematic discovery of phosphorylation networks Combining linear motifs and protein interactions Lars Juhl Jensen EMBL Heidelberg

2. Lars Juhl Jensen

5. promoter analysis

7. genome visualization

9. protein function prediction

13. data integration

15. dynamic interactions

17. prediction of interactions

18. http://string.embl.de

19. prediction of interactions

20. http://networkin.info

21. the starting point

22. phosphoproteomics

23. mass spectrometry

25. phosphorylation sites

26. in vivo

27. kinases are unknown

28. HTP kinase assays

29. in vitro

30. no context

31. what a kinase could do

32. not what it actually does

33. computational methods

34. sequence motifs

36. kinase families

37. phosphorylation sites

38. overprediction

39. no context

40. what a kinase could do

41. not what it actually does

42. in vitro

43. in vivo

44. context

45. localization

46. expression

47. co-activators

48. scaffolders

49. protein networks

51. the idea

52. mass spectrometry

54. phosphorylation sites

55. sequence motifs

57. kinase families

58. protein networks

60. context

61. in vitro

62. in vivo

63. “ shake and bake”

65. NetworKIN

66. the context network

67. STRING

68. functional interactions

69. 373 genomes

71. genomic context methods

72. gene neighborhood

74. gene fusion

76. phylogenetic profiles

78. primary experimental data

79. protein interactions

81. genetic interactions

83. gene coexpression

85. literature mining

87. curated knowledge

89. many sources

90. different formats

91. different gene identifiers

92. redundancy

93. variable quality

94. spread over many species

95. benchmarking

97. transfer by orthology

99. combine all evidence

101. the results

103. 7797 predictions

104. 1790 substrates

105. 69 kinases

107. benchmarking

108. Phospho.ELM

110. 2.5-fold better accuracy

111. context is crucial

112. localization

114. visualization

116. ATM signaling

118. small-scale validation

119. ATM phosphorylates Rad50

121. Cdk1 phosphorylates 53BP1

123. high-throughput validation

124. multiple reaction monitoring

126. the future

127. NetPhorest

128. sequence motifs

129. in vivo

130. in vitro

131. automatic pipeline

132. data organization

134. benchmarking

135. selection

137. ~200 kinases

138. ~100 SH2 domains

139. ~15 PTB domains

140. upstream signaling

141. downstream signaling

142. ordered signaling events

143. signaling pathways

144. Acknowledgments The NetworKIN method Rune Linding Gerard Ostheimer Francesca Diella Karen Colwill Jing Jin Pavel Metalnikov Vivian Nguyen Adrian Pasculescu Jin Gyoon Park Leona D. Samson Rob Russell Peer Bork Michael Yaffe Tony Pawson The STRING database Christian von Mering Michael Kuhn Berend Snel Martijn Huynen Samuel Chaffron Peer Bork The NetPhorest method Martin Lee Miller Rune Linding Nikolaj Blom Søren Brunak