Peptide name : Brevinin-2DYb

Source/Organism : Rana dybowskii

Linear/Cyclic : Linear

Chirality : L

Peptide length: 33

C-terminal modification: Linear

N-terminal modification : Free

Non-natural peptide information:

Assay type : MTT assay

Assay time : 24-h

Activity : IC50 = 24.01 µM

Cell line : A-549

Cancer type : Lung Cancer

Other activity : Anticancer

Amino acid composition bar chart :

Molecular mass : 3289.9087 Dalton

Aliphatic index : 1.121

Instability index : 4.1303

Hydrophobicity (GRAVY) : 0.397

Isoelectric point : 9.2432

Charge (pH 7) : 2.7392

Aromaticity : 3.030

Molar extinction coefficient (cysteine, cystine): (2, 1)

Hydrophobic/hydrophilic ratio : 2

hydrophobic moment : -0.558

Missing amino acid : H,I,M,P,R,T,W,Y

Most occurring amino acid : G

Most occurring amino acid frequency : 7

Least occurring amino acid : F

Least occurring amino acid frequency : 1

Secondary structure fraction (Helix, Turn, Sheet): (42., 33., 33.)

SMILES Notation: CC(C)C[C@H](NC(=O)CN)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)NCC(=O)NCC(=O)N[C@@H](CS)C(=O)O)C(C)C)C(C)C)C(C)C)C(C)C

1 : Zhao Y, et al. Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides. Int J Mol Sci. 2021; 22:(unknown pages). doi: 10.3390/ijms22115630

Paper title : Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.

Doi : https://doi.org/10.3390/ijms22115630

Abstract : Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.