TY - JOUR
T1 - Computational approach for designing tumor homing peptides
AU - Sharma, Arun
AU - Kapoor, Pallavi
AU - Gautam, Ankur
AU - Chaudhary, Kumardeep
AU - Kumar, Rahul
AU - Chauhan, Jagat Singh
AU - Tyagi, Atul
AU - Raghava, Gajendra P.S.
N1 - Funding Information:
Authors are thankful to funding agencies Council of Scientific and Industrial Research (project Open Source Drug Discovery and GENESIS BSC0121) and Department of Biotechnology (project BTISNET), Govt. of India.
PY - 2013/4/5
Y1 - 2013/4/5
N2 - Tumor homing peptides are small peptides that home specifically to tumor and tumor associated microenvironment i.e. tumor vasculature, after systemic delivery. Keeping in mind the huge therapeutic importance of these peptides, we have made an attempt to analyze and predict tumor homing peptides. It was observed that certain types of residues are preferred in tumor homing peptides. Therefore, we developed support vector machine based models for predicting tumor homing peptides using amino acid composition and binary profiles of peptides. Amino acid composition, dipeptide composition and binary profile-based models achieved a maximum accuracy of 86.56%, 82.03%, and 84.19% respectively. These methods have been implemented in a user-friendly web server, TumorHPD. We anticipate that this method will be helpful to design novel tumor homing peptides. TumorHPD web server is freely accessible at http://crdd.osdd.net/raghava/tumorhpd/.
AB - Tumor homing peptides are small peptides that home specifically to tumor and tumor associated microenvironment i.e. tumor vasculature, after systemic delivery. Keeping in mind the huge therapeutic importance of these peptides, we have made an attempt to analyze and predict tumor homing peptides. It was observed that certain types of residues are preferred in tumor homing peptides. Therefore, we developed support vector machine based models for predicting tumor homing peptides using amino acid composition and binary profiles of peptides. Amino acid composition, dipeptide composition and binary profile-based models achieved a maximum accuracy of 86.56%, 82.03%, and 84.19% respectively. These methods have been implemented in a user-friendly web server, TumorHPD. We anticipate that this method will be helpful to design novel tumor homing peptides. TumorHPD web server is freely accessible at http://crdd.osdd.net/raghava/tumorhpd/.
UR - https://www.scopus.com/pages/publications/84885391081
U2 - 10.1038/srep01607
DO - 10.1038/srep01607
M3 - Article
C2 - 23558316
AN - SCOPUS:84885391081
SN - 2045-2322
VL - 3
JO - Scientific Reports
JF - Scientific Reports
M1 - 1607
ER -