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[pLM4ACP: a model for predicting anticancer peptides based on machine learning and protein language models]
Yitong Liu1, Wenxin Chen1, Juanjuan Li1
1School of Life and Health Sciences, Hainan University, Haikou 570228, Hainan, China.
Abstract:
Cancer is a serious global health problem and a major cause of human death. Conventional cancer treatments often run the risk of impairing vital organ functions. Anticancer peptides (ACPs) are considered to be one of the most promising therapeutic agents against common human cancers due to their small sizes, high specificity, and low toxicity. Since ACP recognition is highly limited to the laboratory, expensive, and time-consuming, we proposed pLM4ACP, a model for predicting ACPs based on machine learning and protein language models. In this model, the protein language model ProtT5 was used to extract the features of ACPs, and the extracted features were input into the support vector machine (SVM) classification algorithm for optimization and performance evaluation. The model showcased significantly higher accuracy than other methods, with the overall accuracy of 0.763, F1-score of 0.767, Matthews correlation coefficient of 0.527, and area under the curve of 0.827 on the independent test set. This study constructs an efficient anticancer peptide prediction model based on protein language models, further advancing the application of artificial intelligence in the biomedical field and promoting the development of precision medicine and computational biology.
Insights
This study introduces pLM4ACP, a machine learning model that accurately predicts anticancer peptides (ACPs). This AI-driven approach enhances the discovery of novel ACPs for cancer therapy, overcoming limitations of traditional methods.
Area of Science:
- Computational Biology
- Bioinformatics
- Artificial Intelligence in Medicine
Context:
- Cancer remains a leading cause of mortality worldwide, with conventional treatments posing risks to organ function.
- Anticancer peptides (ACPs) offer a promising alternative due to their specificity and low toxicity.
- Current methods for identifying ACPs are laboratory-intensive, costly, and time-consuming.
Purpose:
- To develop an efficient and accurate computational model for predicting anticancer peptides (ACPs).
- To leverage protein language models and machine learning for accelerated ACP identification.
- To overcome the limitations of traditional experimental ACP recognition methods.
Summary:
- A novel prediction model, pLM4ACP, was developed using the ProtT5 protein language model for feature extraction and a support vector machine (SVM) for classification.
- The pLM4ACP model achieved high performance on an independent test set, with an accuracy of 0.763, F1-score of 0.767, MCC of 0.527, and AUC of 0.827.
- This model significantly outperforms existing methods in predicting ACPs.
Impact:
- Advances the application of artificial intelligence in the biomedical field for drug discovery.
- Promotes the development of precision medicine through enhanced computational approaches.
- Facilitates faster and more cost-effective identification of potential anticancer peptide therapeutics.
