**3.4 MS/MS-identification of PTMs in hormone proteoforms**

MS/MS was an effective method to identify the PTMs and their modification sites of hGH and hPRL to clarify the reasons of formation of hGH proteoforms and hPRL proteoforms. MS/MS identified phosphorylation at sites Ser-77, Ser-132, and Ser-176 in many hGH proteoforms (**Table 3**). A representative MS/MS spectrum was shown to determine phosphorylation site Ser-77 in hGH proteoform 6 (Spot 6) (**Figure 7**). Also, deamidation was found in many hGH proteoforms. In addition, there would be other PTMs in hGH proteoforms that need to be further characterized.


*Modified from Qian et al. [15], with permission from Frontiers publisher open access article, copyright 2018. Note: The bold value means a statistically significantly positive result.*

#### **Table 6.**

*Prediction of O-glycosylation sites in hPRL prohormone (position 1–227) with NetOGlyc 4.0 server with score more than 0.5.*

*Mass Spectrometry - Future Perceptions and Applications*

*\*Score > 0.5 means a statistically significant result.*

**Table 4.**

*score more than 0.5.*

**Sequence # x Context Score\* Kinase Answer** Sequence 6 S NIKGSPWKG 0.779 unsp YES Sequence 11 S PWKGSLLLL 0.848 PKA YES Sequence 18 S LLLVSNLLL 0.523 cdc2 YES Sequence 42 T RCQVTLRDL 0.891 unsp YES Sequence 61 S IHNLSSEMF 0.718 unsp YES Sequence 62 S HNLSSEMFS 0.553 unsp YES Sequence 66 S SEMFSEFDK 0.991 unsp YES Sequence 72 Y FDKRYTHGR 0.503 INSR YES Sequence 73 T DKRYTHGRG 0.557 unsp YES Sequence 90 S CHTSSLATP 0.585 DNAPK YES Sequence 93 T SSLATPEDK 0.737 unsp YES Sequence 110 S KDFLSLIVS 0.507 PKA YES Sequence 118 S SILRSWNEP 0.749 unsp YES Sequence 124 Y NEPLYHLVT 0.956 unsp YES Sequence 142 S EAILSKAVE 0.517 CKII YES Sequence 151 T IEEQTKRLL 0.983 unsp YES Sequence 163 S ELIVSQVHP 0.623 ATM YES Sequence 169 T VHTEPKENE 0.541 CKII YES Sequence 175 Y ENEIYPVWS 0.804 unsp YES Sequence 191 S ADEESRLSA 0.576 cdc2 YES Sequence 194 S ESRLSAYYN 0.982 unsp YES Sequence 207 S LRRDSHKID 0.993 unsp YES *Modified from Qian et al. [15], with permission from Frontiers publisher open access article, copyright 2018.*

*Prediction of phosphorylation sites in hPRL prohormone (positions 1–227) with NetPhos server with a* 

*Modified from Qian et al. [15], with permission from Frontiers publisher open access article, copyright 2018.* 

*Prediction of N-glycosylation sites in hPRL prohormone (positions 1–227) with NetNGlyc 1.0 server with score* 

SEQUON ASN-XAA-SER/THR.

**Seq name Position Potential Jury agreement N-Glyc result** Sequence 2 NIKG 0.7530 **(9/9)** +++

Sequence 19 NLLL 0.7151 **(9/9)** ++ Sequence 59 NLSS 0.7380 **(9/9)** ++ Sequence 84 NSCH 0.7312 (8/9) + Sequence 104 NQKD 0.6020 (7/9) + Sequence 120 NEPL 0.6051 (6/9) + Sequence 172 NEIY 0.5346 (5/9) + Sequence 198 NLLH 0.5642 (5/9) + Sequence 212 NYLK 0.6726 (8/9) + Sequence 224 NNNC 0.5146 (5/9) + Sequence 225 NNC- 0.3576 (8/9) — Sequence 226 NC-- 0.3351 **(9/9)** —

*Note: Asparagines predicted to be N-glycosylated are highlighted in bold font.*

**80**

**Table 5.**

*more than 0.5.*

For hPRL proteoforms, bioinformatics including NetPhos 3.1 Server (http:// www.cbs.dtu.dk/services/NetPhos) [24, 25] predicted 14 pS sites, 5 pT sites, and 3 pY sites in the hPRL (**Table 4**), NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/ services/NetNGlyc) [26] predicted ten significantly N-glycosylated sites (**Table 5**), and NetOGlyc 4.0 Server (http://www.cbs.dtu.dk/services/NetOGlyc) [27] predicted six significantly O-glycosylated sites in the hPRL (**Table 6**) in human pituitaries. These predicted PTM sites in hPRL proteoforms provided clues and needed to be confirmed with MS/MS in future studies.
