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ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies
1School of Computer Science and Engineering, Central South University, Changsha 410000, China.
Abstract:
Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticancer peptides (ACPs) have emerged as promising candidates for highly specific anticancer drugs, and a number of computational approaches have been developed to identify ACPs. However, existing methods do not recognize specific types of anticancer function. In this article, we propose ACPScanner, an integrated approach to predict ACPs and non-ACPs at first and then predict several specific activity types for potential ACPs. We incorporate sequential, physicochemical properties, secondary structural information, and deep representation learning embeddings which are generated from artificial intelligence methods to build feature space. Customized deep learning and statistical learning methods are combined to form an integral architecture for the comprehensive two-level prediction task. To the best of our knowledge, ACPScanner is the first approach for specific ACP activity prediction. The comparative evaluation illustrates that ACPScanner achieves competitive prediction performance in both prediction phases in independent testings. We establish a web server at http://acpscanner.denglab.org to provide convenient usage of ACPScanner and make the predictive framework, source code, and data sets publicly available.
Insights
Researchers developed ACPScanner, a novel computational tool to identify anticancer peptides (ACPs) and predict their specific functions. This advancement offers a more targeted approach to cancer therapy, moving beyond general identification.
Area of Science:
- Biochemistry
- Bioinformatics
- Computational Biology
Background:
- Chemotherapy is a primary cancer treatment but causes significant side effects.
- Anticancer peptides (ACPs) show promise as targeted cancer therapeutics.
- Existing computational methods for ACP identification lack functional specificity.
Purpose of the Study:
- To develop ACPScanner, an integrated computational approach for predicting ACPs and non-ACPs.
- To enable prediction of specific anticancer activity types for identified ACPs.
- To provide a publicly available web server for ACPScanner.
Main Methods:
- Incorporation of sequential, physicochemical, and secondary structural properties of peptides.
- Utilizing deep representation learning embeddings generated by artificial intelligence.
- Employing a hybrid architecture combining deep learning and statistical learning for two-level prediction.
Main Results:
- ACPScanner achieves competitive prediction performance in both ACP identification and activity type prediction.
- The approach effectively integrates diverse feature types for enhanced prediction accuracy.
- Comparative evaluations demonstrate the efficacy of the proposed method in independent tests.
Conclusions:
- ACPScanner is the first computational approach capable of predicting specific anticancer activity types.
- The tool offers a significant advancement in the identification and functional characterization of potential anticancer peptides.
- The public availability of ACPScanner facilitates further research and development in peptide-based cancer therapeutics.
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