dbACP: A Comprehensive Database of Anti-Cancer Peptides

dbacp02144

General Description

Peptide name : C-C motif chemokine 7 (Monocyte chemoattractant protein 3; Monocyte chemotactic protein 3; MCP-3;

Source/Organism : Human

Linear/Cyclic : Not found

Chirality : Not found

Sequence Information

Sequence : QPVGINTSTTCCYRFINKKIPKQRLESYRRTTSSHCPREAVIFKTKLDKEICADPTQKWV

Peptide length: 60

C-terminal modification: Not found

N-terminal modification : Not found

Non-natural peptide information: None

Activity Information

Assay type : Not specified

Assay time : Not found

Activity : Not found

Cell line : Not found

Cancer type : Not found

Other activity : Not found

Physicochemical Properties

Amino acid composition bar chart :

Molecular mass : 7016.0749 Dalton

Aliphatic index : 0.633

Instability index : 24.0717

Hydrophobicity (GRAVY) : -0.741

Isoelectric point : 9.6394

Charge (pH 7) : 6.8093

Aromaticity : 0.083

Molar extinction coefficient (cysteine, cystine): (8480, 8730)

Hydrophobic/hydrophilic ratio : 0.66666666

hydrophobic moment : 0.1108

Missing amino acid : M

Most occurring amino acid : T

Most occurring amino acid frequency : 7

Least occurring amino acid : G

Least occurring amino acid frequency : 1

Structural Information

3D structure :

Secondary structure fraction (Helix, Turn, Sheet): (0.2, 0.2, 0.3)

SMILES Notation: CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@@H](N)CCC(N)=O)C(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H](C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](CS)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CS)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@H](C(=O)O)C(C)C)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)CC)C(C)C)[C@@H](C)O)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)[C@@H](C)O

Secondary Structure :

Method Prediction
GOR CEECCECTEEEEEHHHTTCCCTHHHTHEEEEETTTCCHHHHHHHHHHTHHHHHCTTTHEE
Chou-Fasman (CF) EEEEEEEEEEEEEEECCCCCCHHHHCEEEEEECCCCCCCEEEHHHHHHHHHCCCCEECCC
Neural Network (NN) CCCCECCCCCEEEECCCCCCCCCCCCEEECCCCCCCCCCHHHHHCCCCCCCCCCCCCCCE
Joint/Consensus CEEEEECCEEEEEECCCCCCCCCCCCEEEEEECCCCCCCHHHHHHHHCCCCCCCCCCCCC

Molecular Descriptors and ADMET Properties

Molecular Descriptors: Click here to download

ADMET Properties: Click here to download

Cross Referencing databases

Pubmed Id : 15340161

Uniprot : Not available

PDB : Not available

CancerPPD : Not available

ApIAPDB : Not available

CancerPPD2 ID : Not available

Reference

1 : Zhang Z and Henzel WJ. Signal peptide prediction based on analysis of experimentally verified cleavage sites. Protein Sci. 2004; 13:2819-24. doi: 10.1110/ps.04682504

Literature

Paper title : Signal peptide prediction based on analysis of experimentally verified cleavage sites.

Doi : https://doi.org/10.1110/ps.04682504

Abstract : A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data.