CancerPPD: a database of anticancer peptides and proteins

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.

[1]  Mingming Jia,et al.  COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer , 2010, Nucleic Acids Res..

[2]  Faiza Hanif Waghu,et al.  CAMP: Collection of sequences and structures of antimicrobial peptides , 2013, Nucleic Acids Res..

[3]  Sylvia Van Dorpe,et al.  Brainpeps: the blood–brain barrier peptide database , 2012, Brain Structure and Function.

[4]  Gajendra P.S. Raghava,et al.  PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. , 2007, Protein and peptide letters.

[5]  George A. Khoury,et al.  Forcefield_NCAA: Ab Initio Charge Parameters to Aid in the Discovery and Design of Therapeutic Proteins and Peptides with Unnatural Amino Acids and Their Application to Complement Inhibitors of the Compstatin Family , 2014, ACS synthetic biology.

[6]  M. Castanho,et al.  From antimicrobial to anticancer peptides. A review , 2013, Front. Microbiol..

[7]  G. Hong,et al.  Nucleic Acids Research , 2015, Nucleic Acids Research.

[8]  Xia Li,et al.  APD2: the updated antimicrobial peptide database and its application in peptide design , 2008, Nucleic Acids Res..

[9]  Rahul Kumar,et al.  TumorHoPe: A Database of Tumor Homing Peptides , 2012, PloS one.

[10]  Jyothi Thundimadathil,et al.  Cancer Treatment Using Peptides: Current Therapies and Future Prospects , 2012, Journal of amino acids.

[11]  Evelien Wynendaele,et al.  Quorumpeps database: chemical space, microbial origin and functionality of quorum sensing peptides , 2012, Nucleic Acids Res..

[12]  D. Hoskin,et al.  Studies on anticancer activities of antimicrobial peptides. , 2008, Biochimica et biophysica acta.

[13]  Gajendra P. S. Raghava,et al.  CPPsite: a curated database of cell penetrating peptides , 2012, Database J. Biol. Databases Curation.

[14]  Julie Clark,et al.  Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles , 2016, ACS central science.

[15]  Gajendra P. S. Raghava,et al.  Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides , 2013, Nucleic Acids Res..

[16]  Olivier Michielin,et al.  SwissSidechain: a molecular and structural database of non-natural sidechains , 2012, Nucleic Acids Res..

[17]  George A. Khoury,et al.  Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications. , 2013, Journal of chemical theory and computation.

[18]  Zhi Jie Li,et al.  Peptides as targeting probes against tumor vasculature for diagnosis and drug delivery , 2012, Journal of Translational Medicine.

[19]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[20]  N. Arber,et al.  Peptides for diagnosis and treatment of colorectal cancer. , 2014, Current medicinal chemistry.

[21]  C. Widmann,et al.  Promises of apoptosis-inducing peptides in cancer therapeutics. , 2011, Current pharmaceutical biotechnology.

[22]  Peter M. Kasson,et al.  GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit , 2013, Bioinform..

[23]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[24]  Gajendra P. S. Raghava,et al.  A neural network method for prediction of ?-turn types in proteins using evolutionary information , 2004, Bioinform..

[25]  N. Dubrawsky Cancer statistics , 1989, CA: a cancer journal for clinicians.

[26]  F. Schweizer,et al.  Cationic amphiphilic peptides with cancer-selective toxicity. , 2009, European journal of pharmacology.

[27]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[28]  L. Otvos Peptide-based drug design: here and now. , 2008, Methods in molecular biology.

[29]  James M Aramini,et al.  Assessment of template‐based protein structure predictions in CASP10 , 2014, Proteins.

[30]  D. Hoskin,et al.  Cationic antimicrobial peptides as novel cytotoxic agents for cancer treatment , 2006, Expert opinion on investigational drugs.

[31]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[32]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[33]  David S. Goodsell,et al.  The RCSB Protein Data Bank: new resources for research and education , 2012, Nucleic Acids Res..

[34]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

[35]  A. Khaled,et al.  The use of therapeutic peptides to target and to kill cancer cells. , 2012, Current medicinal chemistry.

[36]  A. Jemal,et al.  Cancer statistics, 2013 , 2013, CA: a cancer journal for clinicians.

[37]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[38]  W R Pearson,et al.  Flexible sequence similarity searching with the FASTA3 program package. , 2000, Methods in molecular biology.

[39]  G P S Raghava,et al.  Tumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics. , 2014, Current medicinal chemistry.

[40]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[41]  Davor Juretic,et al.  DADP: the database of anuran defense peptides , 2012, Bioinform..

[42]  M. Khrestchatisky,et al.  Synthetic therapeutic peptides: science and market. , 2010, Drug discovery today.

[43]  P. Johnston,et al.  Cancer drug resistance: an evolving paradigm , 2013, Nature Reviews Cancer.

[44]  D. Craik,et al.  The Future of Peptide‐based Drugs , 2013, Chemical biology & drug design.

[45]  Gajendra P. S. Raghava,et al.  ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures , 2014, Database J. Biol. Databases Curation.