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. 2017 Jun 20;114(30):E6240–E6249. doi: 10.1073/pnas.1703939114

Table 2.

Sites in our dataset modulated by any PDE inhibitor condition that are identified as regulatory in the PFP database (28)

Gene Description Amino acid Position no. Predictive models RP
B L M R
LASP1 LIM and SH3 domain protein 1 S 146 +
ARHGEF2 Rho guanine nucleotide exchange factor 2 S 858 + +
PTPN7 Tyrosine-protein phosphatase nonreceptor type 7 S 125 + + + + +
BRAF Serine/threonine-protein kinase B-raf S 446 + + + + +
NOP58 Nucleolar protein 58 S 502 +
NUP50 Nuclear pore complex prot Nup50 S 287 +
RAB3IP Rab-3A-interacting protein S 162 +
BAD Bcl2-associated agonist of cell death S 74/75 + + +
PGRMC1 Member-associated progesterone receptor component 1 S 57 +
STAT1 Signal transducer and activator of transcription S 727 + + + +
SLC9A1 Sodium/hydrogen exchanger 1 S 796 + +
SP1 Transcription factor Sp1 S 7 + + + + +
PRKCB Protein kinase C β-type S 660 + + +
TBC1D1 TBC1 domain family member 1 T 596 + + + + +
ETS1 Protein C-ets-1 S 282 + + + +
PPP1R2 Protein phosphatase inhibitor 2 S 121 + + + + +
PPP1R2 Protein phosphatase inhibitor 2 S 122 + + + + +
HMGA1 High mobility group protein HMG-I/HMG-Y T 53 +
STMN1 Stathmin S 63 + +
CAD CAD protein S 1,343 + + + + +
CAMKK1 C++/calmodulin-dependent protein kinase kinase 1 S 485 +
USP20 Ubiquitin carboxyl-terminal hydrolase 20 S 333 +

A truncated peptide sequence of four amino acid residues flanking the regulated phosphosite was used to screen the PFP proteomic database for predicted functional phosphosites. Predictive models used by PFP are Bayes (B), logistic (L), multilayer (M), and random (R). Empirically determined regulatory sites (RP) as derived from the PhosphositePlus database (29) are reported in the last column.

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