data of peptides with amino acid sequences in a
protein database. J Am Soc Mass Spectrom
5:976–989
20. Kall L, Canterbury JD, Weston J, Noble WS,
MacCoss MJ (2007) Semi-supervised learning
for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925
21. Keller A, Nesvizhskii AI, Kolker E, Aebersold R
(2002) Empirical statistical model to estimate
the accuracy of peptide identifications made by
MS/MS and database search. Anal Chem
74:5383–5392
22. Capriotti AL, Cavaliere C, Foglia P,
Piovesana S, Samperi R, Zenezini Chiozzi R,
Lagana ` A (2015) Development of an analytical
strategy for the identification of potential bioactive peptides generated by in vitro tryptic
digestion of fish muscle proteins. Anal Bioanal
Chem 407:845–854
23. Amorim FG, Coitinho LB, Dias AT, Friques
AGF, Monteiro BL, Rezende LCD, Pereira
TMC, Campagnaro BP, De Pauw E, Vasquez
EC, Quinton L (2019) Identification of new
bioactive peptides from kefir milk through proteopeptidomics: bioprospection of antihypertensive molecules. Food Chem 282:109–119
24. Wang G, Li X, Wang Z (2016) APD3: the
antimicrobial peptide database as a tool for
research and education. Nucleic Acids Res 4:
D1087–D1093
25. Jimsheena VK, Gowda LR (2010) Arachin
derived peptides as selective angiotensin
I-converting enzyme (ACE) inhibitors:
structure-activity
relationship.
Peptides
31:1165–1176
26. Minkiewicz P, Iwaniak A, Darewicz M (2019)
BIOPEP-UWM database of bioactive peptides:
current opportunities. Int J Mol Sci 20:5978
27. Shi L, Zhang Q, Rui W, Lu M, Jing X, Shang T,
Tang J (2004) BioPD: a web-based information center for bioactive peptides. Regul Pept
120:1–3
28. Li Q, Zhang C, Chen H, Xue J, Guo X,
Liang M, Chen M (2018) BioPepDB: an
integrated data platform for food-derived bioactive peptides. Int J Food Sci Nutr
69:963–968
29. Thomas S, Karnik S, Barai RS, Jayaraman VK,
Idicula-Thomas S (2010) CAMP: a useful
resource for research on antimicrobial peptides.
Nucleic Acids Res 38:D774–D780
30. Panyayai T, Ngamphiw C, Tongsima S,
Mhuantong
W,
Limsripraphan
W,
Choowongkomon K, Sawatdichaikul O
(2019) FeptideDB: a web application for new
bioactive peptides from protein. Heliyon 5:
e02076
31. Rong M, Zhou B, Zhou R, Liao Q, Zeng Y,
Xu S, Liu Z (2019) PPIP: automated software
for identification of bioactive endogenous peptides. J Proteome Res 18:721–727
32. Wang J, Yin T, Xiao X, He D, Xue Z, Jiang X,
Wang Y (2018) StraPep: a structure database of
bioactive peptides. Database (Oxford) 2018:
bay038
33. Aguilera-Mendoza L, Marrero-Ponce Y, Beltran JA, Tellez Ibarra R, Guillen-Ramirez HA,
Brizuela CA (2019) Graph-based data integration from bioactive peptide databases of pharmaceutical interest: towards an organized
collection enabling visual network analysis.
Bioinformatics 35:4739–4747
34. Mooney C, Haslam NJ, Pollastri DC (2012)
Towards the improved discovery and design of
functional peptides: common features of
diverse classes permit generalized prediction
of bioactivity. PLoS One 7:e45012
35. Gasteiger E, Hoogland C, Gattiker A,
Duvaud S, Wilkins MR, Appel RD, Bairoch A
(2005) Protein identification and analysis tools
on the ExPASy server. In: Walker JM (ed) The
proteomics protocols handbook. Humana
Press, Totowa, NJ, pp 571–607
Shotgun Proteomics and Protein-ased Bioinformatics. . .
223
protein database. J Am Soc Mass Spectrom
5:976–989
20. Kall L, Canterbury JD, Weston J, Noble WS,
MacCoss MJ (2007) Semi-supervised learning
for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925
21. Keller A, Nesvizhskii AI, Kolker E, Aebersold R
(2002) Empirical statistical model to estimate
the accuracy of peptide identifications made by
MS/MS and database search. Anal Chem
74:5383–5392
22. Capriotti AL, Cavaliere C, Foglia P,
Piovesana S, Samperi R, Zenezini Chiozzi R,
Lagana ` A (2015) Development of an analytical
strategy for the identification of potential bioactive peptides generated by in vitro tryptic
digestion of fish muscle proteins. Anal Bioanal
Chem 407:845–854
23. Amorim FG, Coitinho LB, Dias AT, Friques
AGF, Monteiro BL, Rezende LCD, Pereira
TMC, Campagnaro BP, De Pauw E, Vasquez
EC, Quinton L (2019) Identification of new
bioactive peptides from kefir milk through proteopeptidomics: bioprospection of antihypertensive molecules. Food Chem 282:109–119
24. Wang G, Li X, Wang Z (2016) APD3: the
antimicrobial peptide database as a tool for
research and education. Nucleic Acids Res 4:
D1087–D1093
25. Jimsheena VK, Gowda LR (2010) Arachin
derived peptides as selective angiotensin
I-converting enzyme (ACE) inhibitors:
structure-activity
relationship.
Peptides
31:1165–1176
26. Minkiewicz P, Iwaniak A, Darewicz M (2019)
BIOPEP-UWM database of bioactive peptides:
current opportunities. Int J Mol Sci 20:5978
27. Shi L, Zhang Q, Rui W, Lu M, Jing X, Shang T,
Tang J (2004) BioPD: a web-based information center for bioactive peptides. Regul Pept
120:1–3
28. Li Q, Zhang C, Chen H, Xue J, Guo X,
Liang M, Chen M (2018) BioPepDB: an
integrated data platform for food-derived bioactive peptides. Int J Food Sci Nutr
69:963–968
29. Thomas S, Karnik S, Barai RS, Jayaraman VK,
Idicula-Thomas S (2010) CAMP: a useful
resource for research on antimicrobial peptides.
Nucleic Acids Res 38:D774–D780
30. Panyayai T, Ngamphiw C, Tongsima S,
Mhuantong
W,
Limsripraphan
W,
Choowongkomon K, Sawatdichaikul O
(2019) FeptideDB: a web application for new
bioactive peptides from protein. Heliyon 5:
e02076
31. Rong M, Zhou B, Zhou R, Liao Q, Zeng Y,
Xu S, Liu Z (2019) PPIP: automated software
for identification of bioactive endogenous peptides. J Proteome Res 18:721–727
32. Wang J, Yin T, Xiao X, He D, Xue Z, Jiang X,
Wang Y (2018) StraPep: a structure database of
bioactive peptides. Database (Oxford) 2018:
bay038
33. Aguilera-Mendoza L, Marrero-Ponce Y, Beltran JA, Tellez Ibarra R, Guillen-Ramirez HA,
Brizuela CA (2019) Graph-based data integration from bioactive peptide databases of pharmaceutical interest: towards an organized
collection enabling visual network analysis.
Bioinformatics 35:4739–4747
34. Mooney C, Haslam NJ, Pollastri DC (2012)
Towards the improved discovery and design of
functional peptides: common features of
diverse classes permit generalized prediction
of bioactivity. PLoS One 7:e45012
35. Gasteiger E, Hoogland C, Gattiker A,
Duvaud S, Wilkins MR, Appel RD, Bairoch A
(2005) Protein identification and analysis tools
on the ExPASy server. In: Walker JM (ed) The
proteomics protocols handbook. Humana
Press, Totowa, NJ, pp 571–607
Shotgun Proteomics and Protein-ased Bioinformatics. . .
223