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Peptide record

TPDB30705

Toxicity standard
13 amino acids
Basic Information
3D PDB MODEL
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TPDB30705
Toxicity
Toxicity and safety peptides
Peptipedia ToxinPred 3.0
standard
No
A 13-aa standard natural toxicity peptide sequence curated from Peptipedia and ToxinPred 3.0, with an available 3D structural model.
Sequence
FLPILGKILGGLL
Physicochemical Analysis
C68H116N14O14
ARNDCEQHMSTWYV
L
1353.75
10.10
1
0
8
+1
-3.08
1.854
210.00
Mammalian: 1.1 hour Yeast: 3 min E.coli: 2 min
0
0.00
0
Residue Composition
number
0
A
0
R
0
N
0
D
0
C
0
E
0
Q
3
G
0
H
2
I
5
L
1
K
0
M
1
F
1
P
0
S
0
T
0
W
0
Y
0
V
Amino Acid Distribution
A: 0 R: 0 N: 0 D: 0 C: 0 E: 0 Q: 0 G: 3 H: 0 I: 2 L: 5 K: 1 M: 0 F: 1 P: 1 S: 0 T: 0 W: 0 Y: 0 V: 0
Chemical Descriptors
13
C68H116N14O14
1353.75
10.10
+1
-3.08
1.854
Evidence Records 4 records
Evidence 1 Activity

Activity

IC50
Toxicity
34.03±1.24 µM
Cytotoxic
IC50 | 34.03±1.24 | µM | target cell: Human pancreas epithelial cells HPNE

Target

Human pancreas epithelial cells HPNE

Source & Reference

NA
DBAASP
Discovery of anticancer peptides from natural and generated sequences using deep learning. | Int J Biol Macromol | 2025
Evidence 2 Activity

Activity

IC50
Toxicity
25.12±1.59 µM
Cytotoxic
IC50 | 25.12±1.59 | µM | target cell: Human umbilical vein endothelial cells HUVEC

Target

Human umbilical vein endothelial cells HUVEC

Source & Reference

NA
DBAASP
Discovery of anticancer peptides from natural and generated sequences using deep learning. | Int J Biol Macromol | 2025
Evidence 3 Activity

Activity

IC50
Toxicity
23.23±2.01 µM
Cytotoxic
IC50 | 23.23±2.01 | µM | target cell: Human keratinocytes HaCat

Target

Human keratinocytes HaCat

Source & Reference

NA
DBAASP
Discovery of anticancer peptides from natural and generated sequences using deep learning. | Int J Biol Macromol | 2025
Evidence 4 Activity

Activity

IC50
Toxicity
>50 µM
Cytotoxic
IC50 | >50 | µM | target cell: Human embryonic kidney HEK293T cells

Target

Human embryonic kidney HEK293T cells

Source & Reference

NA
DBAASP
Discovery of anticancer peptides from natural and generated sequences using deep learning. | Int J Biol Macromol | 2025