TumorHoPe: A Database of Tumor Homing Peptides
Pallavi Kapoor, Harinder Singh, Ankur Gautam, Kumardeep Chaudhary, Rahul Kumar,
Gajendra P. S. Raghava*
Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
Background: Cancer is responsible for millions of immature deaths every year and is an economical burden on developing
countries. One of the major challenges in the present era is to design drugs that can specifically target tumor cells not
normal cells. In this context, tumor homing peptides have drawn much attention. These peptides are playing a vital role in
delivering drugs in tumor tissues with high specificity. In order to provide service to scientific community, we have
developed a database of tumor homing peptides called TumorHoPe.
Description: TumorHoPe is a manually curated database of experimentally validated tumor homing peptides that
specifically recognize tumor cells and tumor associated microenvironment, i.e., angiogenesis. These peptides were collected
and compiled from published papers, patents and databases. Current release of TumorHoPe contains 744 peptides. Each
entry provides comprehensive information of a peptide that includes its sequence, target tumor, target cell, techniques of
identification, peptide receptor, etc. In addition, we have derived various types of information from these peptide
sequences that include secondary/tertiary structure, amino acid composition, and physicochemical properties of peptides.
Peptides in this database have been found to target different types of tumors that include breast, lung, prostate, melanoma,
colon, etc. These peptides have some common motifs including RGD (Arg-Gly-Asp) and NGR (Asn-Gly-Arg) motifs, which
specifically recognize tumor angiogenic markers. TumorHoPe has been integrated with many web-based tools like simple/
complex search, database browsing and peptide mapping. These tools allow a user to search tumor homing peptides based
on their amino acid composition, charge, polarity, hydrophobicity, etc.
Conclusion: TumorHoPe is a unique database of its kind, which provides comprehensive information about experimentally
validated tumor homing peptides and their target cells. This database will be very useful in designing peptide-based drugs
and drug-delivery system. It is freely available at http://crdd.osdd.net/raghava/tumorhope/.
Citation: Kapoor P, Singh H, Gautam A, Chaudhary K, Kumar R, et al. (2012) TumorHoPe: A Database of Tumor Homing Peptides. PLoS ONE 7(4): e35187.
Editor: Bin Xue, University of South Florida, United States of America
Received December 24, 2011; Accepted March 9, 2012; Published April 16, 2012
Copyright: ? 2012 Kapoor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors are thankful to Council of Scientific and Industrial Research (CSIR), Open Source Drug Discovery (OSDD) and Department of Biotechnology,
Government of India for providing research fellowships. The funders had no role in study design, data collection and analysis, decision to publish, or preparation
of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Cancer is one of the leading causes of immature deaths all over
the world . In 2008, more than 7 million deaths (around 13% of
all deaths) occurred due to cancer and more new cancer cases are
Statistics-2011) . Both developed and developing countries are in
the grip of this deadly disease. Despite tremendous progress in the
field of cancer research, still we are unable to design effective and
efficient anti-cancer therapy . The current anti-cancer drugs are
not selective and kill both tumor cells and normal cells. Therefore,
designing drugs that specifically target cancer cells without affecting
normal cells is one of the most challenging tasks for researchers. In
order to overcome this limitation, various approaches/strategies/
delivery systems have been developed. These approaches include
the use of engineered antibodies, cell penetrating peptides, tumor
homing peptides, and aptamers .
In the last few years, tumor homing peptides have gained
recognition as a drug delivery vehicle. Due to advancement in
phage display technology, a large number of peptides have been
discovered, which specifically bind to tumor cells. These peptides
have a strong affinity towards a specific receptor/marker that is
often present in many tumors and tumor vasculature. In several
cases, some of these receptors/markers are over expressed in
tumors, in comparison to their expression in normal tissues .
These peptides can also recognize angiogenic/metastatic lesions,
which may not be detectable using other traditional methods .
Since these peptides home to tumor site/vasculature through the
circulation, they are often called as tumor homing/ targeting
peptide. Most of the tumor homing peptides have been identified
by phage display technique  and have shown good accumu-
lation at the tumor site, when injected in mice models . Drugs
conjugated to such peptides, when administered, have shown more
efficacies in mice models , and many peptide based drugs are
already in clinical trials [10,11]. In addition, these peptides have
also been successfully used to deliver various imaging agent and
inorganic nanoparticles . Peptide based therapy has many
advantages over the conventional anti-cancer chemotherapy.
They are more specific and thus, exhibit lesser side effects .
In addition, due to direct delivery of the anti-cancer agent/drug to
PLoS ONE | www.plosone.org1April 2012 | Volume 7 | Issue 4 | e35187
the malignant tissue, chemo-resistance of the cancer cells can be
overcome or reduced .
Despite the huge therapeutic potential of tumor homing
peptides, till date, there is no database or bioinformatics method
for designing of effective tumor homing peptides. In order to
facilitate scientific community, we have made a systematic attempt
to collect and compile experimentally validated tumor homing
Materials and Methods
Data collection and compilation
Data for TumorHoPe database is manually collected from
published papers, patents, and web sites by using a combination of
keywords like tumor homing peptides and tumor targeting
peptides (Pubmed: http://www.ncbi.nlm.nih.gov/pubmed/). Ex-
perimentally validated sequences were collected from published
literature. Sequences were also collected from the patents which
are available at websites of United States Patent and Trademark
Office and World Intellectual Property Organization.
Database Architecture and Web interface
TumorHoPe is built on Apache HTTP server 2.2 with MySQL
server 5.1.47 as the back end and the PHP 5.2.9, HTML and
preferred as these are open-source softwares and platform
independent. The architecture of TumorHoPe database is shown
in Figure 1.
Organization of data
TumorHoPe is a manually curated database which provides
comprehensive information about peptides that target, bind to,
and or home to tumor. Data for each peptide can be categorized
as primary (e.g. peptide sequence, PubMed ID (PMID) and
experimental details) and secondary data (e.g. secondary/tertiary
structure, amino acid composition, frequency and physicochemical
properties). Each peptide is assigned a unique entry number, and
information is divided into different tables. Each table provides
Primary data contains general information of
tumor homing peptides. All the information is extracted manually
from various resources, mainly from publications and patents. As
shown in the Figure 1, database provides comprehensive
information on each peptide using more than 15 types of fields.
Main fields are described as follows: (i) Sequence: it contains
amino acid sequence of tumor homing peptide; (ii) Target tumor:
it contains the name of tumor targeted by peptide; (iii) Target cell:
it is type of cells targeted by peptide; (iv) Phage/library: It provides
details of phage display library; (v) in vitro/ex vivo: it provides details
of experiment carried out in in vitro and ex vivo conditions; (vi)
Clone name: it provides information of clones of tumor homing
peptides obtained by phage display technology against various
tumors; (vii) Payloads: it provides information of conjugate
attached with the peptide for imaging or drug delivery; (viii)
information whether peptide has been used in vivo experiment
(tumor homing) or has been used in vitro experiments (tumor
In the past, it has been shown that efficacy
of tumor homing peptide depends on its structure . Therefore,
understanding of tertiary structure of these peptides is a
prerequisite. Since these peptide structures are not available in
Protein Data Bank (PDB) and existing methods for predicting
tertiary structure of protein are unsuitable for predicting tertiary
Figure 1. Overall architecture of TumorHoPe database.
Tumor Homing Peptides Repository
PLoS ONE | www.plosone.org2 April 2012 | Volume 7 | Issue 4 | e35187
structure of peptides, we have predicted tertiary structure of tumor
homing peptides using software PEPstr . PEPstr is a state of art
method for predicting structure of bioactive peptides. It is a de-novo
method which builds a tertiary structure from predicted secondary
structure and tight turns . In addition, secondary structures of
these peptides have also been predicted from predicted tertiary
structure using software DSSP . In our database, we maintain
both secondary and tertiary structure of each peptide in PDB
In order to understand the nature of the peptide, it is imperative
to know the constituents of each peptide. Therefore, in-house
PERL scripts have been used for computing frequency and
composition of each type of amino acid residue in all the peptides.
This information is very useful to understand which residues/
motifs are preferred in tumor homing peptides. Apart from amino
acid frequency and composition, users may also want to know
which types of residues are preferred like charged, polar,
hydrophobic, etc. Therefore, we have computed frequency and
composition of each class (aromatic, aliphatic, positively charged,
negatively charged, neutral, polar and hydrophobic) of residues.
This information is very useful to know which peptides are
dominated by positively charged residues. We have also computed
overall physicochemical properties (hydrophobicity, hydrophilicity
molecular weight, iso-electric point and net charge) of each
peptide [17,18]. All above information is stored in the database as
secondary data for analysis and browsing of peptides based on
Implementation of tools
Apart from the collection of tumor homing peptides and their
targets, a wide variety of information can be generated using the
various online software/tools provided with TumorHoPe. Follow-
ing are the main tools provided with the TumorHoPe database.
TumorHoPe is integrated with a user friendly interface for
retrieving data from the database. A brief description of these
interfaces is as follows:
This interface allows users to search their
query in most of the fields of database (e.g., peptide sequence,
motif, target tumor, target cell, source clone). One of the powerful
features of this interface is that it allows users to select fields they
wish to display in their results.
Advance search allows users to search the
whole database in a stepwise manner. A query can be submitted
using the following five steps: (i) fill the field name; (ii) choose any
of four match operators (=, ., ,, and LIKE); (iii) fill the value,
(iv) choose between two condition operators (AND & OR); and (v)
press to add another query. Users are able to add multiple queries
to search the database. In summary, advance search allows users
to perform more complex queries in TumorHoPe.
This interface is designed for searching a
peptide sequence in the database. It allows two types of queries: (i)
Containing peptide: it is for searching user defined peptide
sequence in tumor homing peptides, and (ii) Exact search: it allows
user to search tumor homing peptides, which are 100 percent
identical to user’s peptide.
We have designed powerful browsing facility that allows a user
to browse data using various options. A brief description of
interfaces designed for browsing are as follows:
This interface allows a user to browse database
on the following four major fields: (i) target tumor; (ii) cell lines; (iii)
year of publication; and (iv) target site. As shown in Figure 2, it
provides a number of peptides for different types of target tumor,
target site and cell lines.
searching or extracting peptides, which have desired frequency
of specific type of residues. For example, user may search all
peptides that have two Arg and three Ala. This is useful option of
searching peptides dominated by particular type of residues.
Amino Acid Composition.
extract peptides from the database based on their amino acid
composition. For example, user can search all the peptides that
have all Arg residues by specifying 100% composition of Arg. User
can easily extract tumor homing peptides with desired amino acid
Physicochemical Property Frequency.
tumor homing peptides with desired frequency of certain types of
residues like, positively charged, negatively charged polar
aliphatic, aromatic residues, etc. For example, user can easily
extract all tumor homing peptides that have more than four
positively charged residues.
Physicochemical Property Composition.
allows users to extract tumor homing peptides with desired
composition of different types of residues like, positively charged,
negatively charged, polar residues, etc. For example, user can
easily extract all tumor homing peptides that have more than 80%
positively charged residues.
Physicochemical Property Value.
physicochemical properties of each tumor homing peptide like
hydrophobicity, hydrophilicity, net charge, iso-electric point. This
tool allows users to extract tumor homing peptides with desired
physicochemical properties. For example, user can search all
peptides having net positive charge and molecular weight in the
desired range [17,18].
This option is designed for
This interface allows users to
User can extract
We have also computed
Structure based browsing tools
In this database, predicted secondary and tertiary structure of
each peptide has been stored. Three interfaces have been
developed to extract structural information of tumor homing
peptides. A brief description of these interfaces is as follows:
Secondary Structure (SS) composition.
allows users to browse peptides based on their secondary
structure composition. We have assigned four types of secondary
structure states (H-helix, E-beta strand, T-turn and C-coil) from
DSSP. User can search tumor homing peptides with desired
composition of these four types of secondary structure states. For
example, user can search peptides that have more than 60%
residues in the helix state.
Secondary Structure (SS) Search.
users to search segment of particular secondary structure states.
This interface is designed to browse tertiary
structure of tumor homing peptides. It also allows user to search
results on any field. First, it displays structure of peptide as static
image. This image is clickable, and on click, it launches Jmol
(http://www.jmol.org) after loading structure of peptides. This
allows user to visualize 3D structure in any format or size/
orientation supported by Jmol.
This interface allows
A number of web-based tools have been integrated in this
database to facilitate further analysis of peptides. A brief
description of these tools is as follows:
We have integrated BLAST search tool 
that allows users to perform similarity based search against tumor
homing peptides. This option allows users to submit one or more
Tumor Homing Peptides Repository
PLoS ONE | www.plosone.org3April 2012 | Volume 7 | Issue 4 | e35187
peptide sequences in FASTA format for performing BLAST
search against tumor homing peptides.
search effectively in case of small peptides, we have integrated
Smith-Waterman algorithm . This option allows users to
search tumor homing peptides in the database that are similar to
their peptides. User can submit multiple peptide sequences in
In addition to BLAST and Smith-
Waterman algorithm, a simple algorithm has been integrated
called Identical Residues. It takes user defined peptide sequence
and aligns it with each tumor homing peptide using overlapping
approach (alignment without gap). It displays query and target
sequence with a number of identical residues in two peptides.
It allows users to map tumor homing
peptides on their peptide sequence. User may submit protein or
polypeptide sequence on this page to identify segments that are
identical to peptides in TumorHoPe.
In order to handle similarity
TumorHoPe contains 744 peptides in which 359 peptides have
been collected from research publications and 253 peptides have
been collected from patents. The rest 132 peptides have been
collected from both research publications and patents. As shown in
Figure 2, peptides target more than 20 types of tumors. For
example, 282 peptides target breast tumor, 75 peptides target
melanomas, and 60 peptides target prostate tumor. In this study,
we have covered more than 70 types of cancer cell lines. A few cell
lines are found to be targeted by a large number of homing
peptides. For example, 151 peptides target MDA-MB cell line, 75
peptides target MCF cell line, and 49 peptides target SK-BR-3 cell
line (column 2 of Figure 2).
We have computed average amino acid composition of these
peptides and observed that certain types of residues (Cys, Arg, Gly,
Leu and Ser) are more abundant in tumor homing peptides
(Figure 3A). We have also computed and plotted the average
amino acid composition of equal number of proteins (744)
extracted from SwissProt. As shown in Figure 3A, tumor homing
peptides have relatively higher average composition of Arg, Cys
and Trp compared to SwissProt sequences, while residues Gly and
Leu are abundant in both the cases. As shown in Figure 3A, Cys
residue has the highest average composition. This is unusual as
composition of Cys residues is very low in natural proteins and
peptides. We have further examined the homing peptides and
found that a wide range of peptides are cyclic where start and end
residues are cysteine. In order to avoid biasness in our analysis, we
computed average amino acid composition of non-cyclic tumor
homing peptides (cyclic peptides were removed). As shown in
Figure 3A, the composition of Cys of non-cyclic peptides is lower
than the composition of Cys in all tumor homing peptides, but still
it is significantly higher than the composition of Cys in normal
proteins (SwissProt proteins). In addition, composition of Arg is
also found to be very high. This is in agreement with various
studies, which showed that positively charged residues are usually
preferred in homing peptides. We have also calculated the
frequency of poly-Arg (RR, RRR and RRRR) in tumor homing
peptides (data not shown). Only 30 peptides contain RR motif,
while no peptide has RRR motif. Therefore, high average
composition of Arg in tumor homing peptides is not due to the
presence of poly-Arg peptides/motifs and it may be due to the
presence of some common motifs like RGD, NGR, etc. which
contain Arg. It is also observed that most of the peptides have 7, 9,
10 and 12 residues (Figure 3B). This could be due to the more
frequent use of phage display libraries consisting of 7 mer/9 mer
Figure 2. Screenshot of major fields page of TumorHoPe.
Tumor Homing Peptides Repository
PLoS ONE | www.plosone.org4 April 2012 | Volume 7 | Issue 4 | e35187
In past, many tumor homing motifs have been identified from
homing peptides. These are smallest peptides that can selectively
recognize tumor cells and tumor vasculature. One of the common
motifs is RGD, which has a tumor homing ability. RGD
specifically recognizes integrin (avb3 and avb5) receptor, which
is a good marker of angiogenic blood vessels . This motif has
been studied extensively and many tumor homing peptides have
been designed by incorporating this motif. Similarly, NGR motif
has also been identified from tumor homing peptides, which
specifically bind to cells expressing aminopeptidase N, a
membrane-bound metallopeptidase that plays important roles in
tumor angiogenesis . We have examined all such motifs
present in tumor homing peptides in our database and computed
the number of peptides containing each of the motifs. Distribution
of peptides containing most frequent motifs is shown in Figure 3C.
It is observed that 45 peptides contain NGR motif, while 36
peptides contain RGD motif. We have also examined other motifs
that occur at least in four homing peptides.
We have computed secondary structure composition of each
peptide and classified peptides based on secondary structure
content. As shown in Figure 3D, most of the peptides have no
beta-strand or less than 20% beta-strand. A similar trend is
observed for helix. Only few peptides have 40–60% helix content.
In case of turn, most of the peptides have less than 20% turns
content and a sharp decrease in number of peptides having turns
up to 60%, is observed. In case of coil, a large number of peptides
have most of the residues forming coil. This is expected as it is
difficult to maintain a regular structure in small peptides. This is
just a primary analysis, and a detailed analysis is required in order
to understand the relation between structure and tumor homing
capability of the peptides.
TumorHoPe database is developed to serve the scientific
community working in the area of peptide based cancer
therapeutics. Although many peptide databases, e.g. MHCBN
, Bcipep , HMRbase , MimotopeDB  , CAMP
, and ANTIMIC  have been published, yet, at present, to
the best of our knowledge, there is no database available that
provides detailed information about the tumor homing peptides.
Therefore, we have developed this database, which not only saves
time and effort of researchers involved in the field, but will
facilitate the biological discovery process. We have compiled 744
Figure 3. Distribution of tumor homing peptides in TumorHoPe. (A) Average amino acid composition of peptides (SwissProt proteins, tumor
homing peptides (THP) and non-cyclic THP), (B) length wise distribution of tumor homing peptides, (C) distribution of peptides based on major
sequence motifs, and (D) distribution of peptides based on secondary structure composition.
Tumor Homing Peptides Repository
PLoS ONE | www.plosone.org5 April 2012 | Volume 7 | Issue 4 | e35187
peptides with experimental and structural information, making use
of literature and patents.
Although plethora of papers has been published on therapeutic
potential of tumor homing or targeting peptide in anti-cancer drug
delivery and development, results have not come up to the
expectations. We hope that this collection of tumor homing
peptides will be very helpful to researchers to design novel
peptides, which can be used further for developing novel anti-
cancer drugs. In addition, TumorHoPe will also be useful for the
generation of prediction models for novel tumor homing peptides.
The understanding of the role of amino acids of tumor homing
peptides is important for their rational drug designing. We have
integrated various analysis tools, which can be exploited by users
to search and modify their peptides in order to enhance the
specificity of the query peptides. Since most of the tumor homing
peptides bind to particular receptors present on tumor cells,
structure of these peptides may play an important role in their
binding to their receptors. Therefore, we have predicted 3D
structures of all the peptides. Users can exploit this information for
docking or molecular dynamics of the peptide-receptor complex.
We anticipate that this thorough and comprehensive database will
be extended to effective completeness and then maintained and its
content expanded, with constantly enhanced search and analysis
Submission and Update of TumorHoPe
The online data submission tool allows a user to submit a newly
identified tumor homing peptide in the TumorHoPe database.
However, before including in TumorHoPe, we will confirm the
validity of new entry in order to maintain the quality. Our team is
also searching and adding new entries of tumor homing peptides
from published literature. In order to maintain the consistency, we
will revive the TumorHoPe database.
Availability and Requirements
TumorHoPe is freely available at http://crdd.osdd.net/
Conceived and designed the experiments: GPSR PK. Performed the
experiments: PK HS AG KC RK. Analyzed the data: GPSR. Contributed
reagents/materials/analysis tools: HS KC RK. Wrote the paper: GPSR
1.Blanco E, Hsiao A, Mann AP, Landry MG, Meric-Bernstam F, et al. (2011)
Nanomedicine in cancer therapy: innovative trends and prospects. Cancer Sci
Jemal A, Bray F, Center MM, Ferlay J, Ward E, et al. (2011) Global cancer
Statistics . CA Cancer J Clin 61: 69–90.
Tang Y, Lei T, Manchanda R, Nagesetti A, Fernandez-Fernandez A, et al.
(2010) Simultaneous delivery of chemotherapeutic and thermal-optical agents to
cancer cells by a polymeric (PLGA) nanocarrier: an in vitro study. Pharm Res
Khandare JJ, Minko T (2006) Antibodies and peptides in cancer therapy. Crit
Rev Ther Drug Carrier Syst 23: 401–435.
Reubi JC (2003) Peptide receptors as molecular targets for cancer diagnosis and
therapy. Endocr Rev 24: 389–427.
Ruoslahti ESR, Pasqualini R (1997) Method of identifying molecules that home
to a selected organ in vivo US005622699A.
Jiang YQ, Wang HR, Li HP, Hao HJ, Zheng YL, et al. (2006) Targeting of
hepatoma cell and suppression of tumor growth by a novel 12mer peptide fused
to superantigen TSST-1. Mol Med 12: 81–87.
Ellerby HM, Arap W, Ellerby LM, Kain R, Andrusiak R, et al. (1999) Anti-
cancer activity of targeted pro-apoptotic peptides. Nat Med 5: 1032–1038.
Temming K, Meyer DL, Zabinski R, Dijkers EC, Poelstra K, et al. (2006)
Evaluation of RGD-targeted albumin carriers for specific delivery of auristatin E
to tumor blood vessels. Bioconjug Chem 17: 1385–1394.
10. Laakkonen P, Vuorinen K (2010) Homing peptides as targeted delivery vehicles.
Integr Biol (Camb) 2: 326–337.
11. Corti A, Pastorino F, Curnis F, Arap W, Ponzoni M, et al. (2011) Targeted drug
delivery and penetration into solid tumors. Med Res Rev.
12. Karmali PP, Kotamraju VR, Kastantin M, Black M, Missirlis D, et al. (2009)
Targeting of albumin-embedded paclitaxel nanoparticles to tumors. Nanome-
dicine 5: 73–82.
13. Engel JB, Schally AV, Dietl J, Rieger L, Honig A (2007) Targeted therapy of
breast and gynecological cancers with cytotoxic analogues of peptide hormones.
Mol Pharm 4: 652–658.
14. Rosca EV, Koskimaki JE, Rivera CG, Pandey NB, Tamiz AP, et al. (2011) Anti-
angiogenic peptides for cancer therapeutics. Curr Pharm Biotechnol 12:
15. Kaur H, Garg A, Raghava GP (2007) PEPstr: a de novo method for tertiary
structure prediction of small bioactive peptides. Protein Pept Lett 14: 626–631.
16. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern
recognition of hydrogen-bonded and geometrical features. Biopolymers 22:
17. Eisenberg D (1984) Three-dimensional structure of membrane and surface
proteins. Annu Rev Biochem 53: 595–623.
18. Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from
amino acid sequences. Proc Natl Acad Sci U S A 78: 3824–3828.
19. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local
alignment search tool. J Mol Biol 215: 403–410.
20. Pearson WR (2000) Flexible sequence similarity searching with the FASTA3
program package. Methods Mol Biol 132: 185–219.
21. Ruoslahti E, Bhatia SN, Sailor MJ (2010) Targeting of drugs and nanoparticles
to tumors. J Cell Biol 188: 759–768.
22. Corti A, Curnis F, Arap W, Pasqualini R (2008) The neovasculature homing
motif NGR: more than meets the eye. Blood 112: 2628–2635.
23. Lata S, Bhasin M, Raghava GP (2009) MHCBN 4.0: A database of MHC/TAP
binding peptides and T-cell epitopes. BMC Res Notes 2: 61.
24. Saha S, Bhasin M, Raghava GP (2005) Bcipep: a database of B-cell epitopes.
BMC Genomics 6: 79.
25. Rashid M, Singla D, Sharma A, Kumar M, Raghava GP (2009) Hmrbase: a
database of hormones and their receptors. BMC Genomics 10: 307.
26. Ru B, Huang J, Dai P, Li S, Xia Z, et al. (2010) MimoDB: a new repository for
mimotope data derived from phage display technology. Molecules 15:
27. Thomas S, Karnik S, Barai RS, Jayaraman VK, Idicula-Thomas S (2009)
CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids
Res 38: D774–780.
28. Brahmachary M, Krishnan SP, Koh JL, Khan AM, Seah SH, et al. (2004)
ANTIMIC: a database of antimicrobial sequences. Nucleic Acids Res 32:
Tumor Homing Peptides Repository
PLoS ONE | www.plosone.org6April 2012 | Volume 7 | Issue 4 | e35187