Related Experiment Video
Updated: Nov 2, 2025
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Survey of In-silico Prediction of Anticancer Peptides
1School of Finance and Economics, Xinyang Agriculture and Forestry University, Xinyang 464000, China.
Abstract:
Cancer is one of the major causes of death in human beings. While traditional cancer treatments kill cancerous cells, they negatively affect normal cells. In addition, the side effects and high medical costs of treatment prevent effective management of cancer. Nonetheless, anticancer peptides have gained popularity over the recent years as potential therapeutic agents that may complement traditional therapies. Compared to conventional wet-lab experiments, computation-based methods provide a promising platform for high-throughput identification of peptides that have anticancer activity. Therefore, this review summarizes the currently available databases for anticancer peptides/proteins. This is a survey of 22 recently published in-silico methods that aim to predict anticancer peptides accurately. More specifically, the article details the benchmark datasets, feature construction, feature selection, machine learning algorithms, assessment criteria, comparison of different methods, and publicly available predictors. We also compare the prediction performance of these predictors to the benchmark dataset. Finally, the study makes several recommendations concerning the future development of databases for anticancer peptides and methods that can be used to predict anticancer peptides.
Insights
This review surveys computational methods for identifying anticancer peptides, offering a faster, cheaper alternative to traditional lab work. It details 22 in-silico tools to aid in discovering new cancer therapies.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Cancer remains a leading cause of mortality, with conventional treatments causing significant side effects and high costs.
- Anticancer peptides are emerging as promising therapeutic agents, complementing traditional cancer therapies.
- Computational methods offer a high-throughput, cost-effective alternative to wet-lab experiments for identifying anticancer peptides.
Purpose of the Study:
- To review existing databases of anticancer peptides/proteins.
- To survey 22 in-silico methods for accurately predicting anticancer peptides.
- To provide recommendations for future development of anticancer peptide databases and prediction methods.
Main Methods:
- Detailed analysis of benchmark datasets used in in-silico prediction.
- Examination of feature construction and selection techniques.
- Review of machine learning algorithms, assessment criteria, and publicly available predictors.
Main Results:
- Comparison of prediction performances of various in-silico methods against benchmark datasets.
- Identification of strengths and weaknesses across different computational approaches.
- Evaluation of the accuracy and utility of current anticancer peptide prediction tools.
Conclusions:
- In-silico methods show significant promise for accelerating the discovery of anticancer peptides.
- Further development is needed in database construction and prediction algorithm refinement.
- Standardized benchmarks and validation are crucial for advancing the field of computational anticancer peptide research.
