Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25270878
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Nucleic+Acids+Res
2015 ; 43
(Database issue
): D837-43
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
CancerPPD: a database of anticancer peptides and proteins
#MMPMID25270878
Tyagi A
; Tuknait A
; Anand P
; Gupta S
; Sharma M
; Mathur D
; Joshi A
; Singh S
; Gautam A
; Raghava GP
Nucleic Acids Res
2015[Jan]; 43
(Database issue
): D837-43
PMID25270878
show ga
CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of
experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data
were manually collected from published research articles, patents and from other
databases. The current release of CancerPPD consists of 3491 ACP and 121
anticancer protein entries. Each entry provides comprehensive information related
to a peptide like its source of origin, nature of the peptide, anticancer
activity, N- and C-terminal modifications, conformation, etc. Additionally,
CancerPPD provides the information of around 249 types of cancer cell lines and
16 different assays used for testing the ACPs. In addition to natural peptides,
CancerPPD contains peptides having non-natural, chemically modified residues and
D-amino acids. Besides this primary information, CancerPPD stores predicted
tertiary structures as well as peptide sequences in SMILES format. Tertiary
structures of peptides were predicted using the state-of-art method, PEPstr and
secondary structural states were assigned using DSSP. In order to assist users, a
number of web-based tools have been integrated, these include keyword search,
data browsing, sequence and structural similarity search. We believe that
CancerPPD will be very useful in designing peptide-based anticancer therapeutics.