THPep: A machine learning-based approach for predicting tumor homing peptides
Institution:
1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;2. Department of Medical Laboratory Technology, Faculty of Health Science, Setia Budi University, Surakarta 57127, Indonesia;1. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;1. School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China;2. College of Cybersecurity, Sichuan University, Chengdu 610065, China;3. School of Geography and Resources, Guizhou Education University, Guiyang 550018, China;4. Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, Sichuan, China;1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China;2. School of Systems and Technology, Department of Informatics and System, University of Management and Technology, Lahore, 54770, Pakistan;3. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand;4. School of Systems and Technology, Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan;5. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box 110099, Taif, 21944, Saudi Arabia;1. Department of Computer Science, Abdul Wali Khan University Mardan, KP, 23200, Pakistan;2. Department of Physics, University of Lahore, Sargodha Campus, 40100, Sargodha, Pakistan
Abstract:
In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.
