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ACP-DRL: an anticancer peptides recognition method based on deep representation learning
Xiaofang Xu1, Chaoran Li1, Xinpu Yuan2
1State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing, China.
Abstract:
Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.
Insights
Researchers developed ACP-DRL, a novel deep learning method for identifying anticancer peptides (ACPs). This approach enhances cancer research by improving the efficiency and reducing the cost of discovering potential anti-cancer therapeutics.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Cancer is a leading cause of mortality globally, necessitating novel therapeutic strategies.
- Anticancer peptides (ACPs) show promise in inhibiting tumor growth with fewer side effects than traditional treatments.
- Current methods for identifying ACPs via wet-lab experiments are inefficient and costly.
Purpose of the Study:
- To introduce ACP-DRL, a deep representation learning-based method for accurate and efficient recognition of anticancer peptides.
- To overcome the limitations of traditional wet-lab identification methods for ACPs.
- To leverage advanced computational techniques for accelerating the discovery of novel ACPs.
Main Methods:
- Integration of protein language models with in-domain further pre-training for enhanced representation learning.
- Utilization of bidirectional long short-term memory (BiLSTM) networks to extract sequence-based amino acid features.
- Development of a deep learning framework (ACP-DRL) for ACP recognition, independent of sequence length and manual feature engineering.
Main Results:
- ACP-DRL demonstrates superior performance compared to existing ACP recognition methods.
- The model effectively extracts relevant features from amino acid sequences without manual intervention.
- Achieved high accuracy in identifying potential anticancer peptides, reducing experimental costs and time.
Conclusions:
- ACP-DRL offers a computationally efficient and cost-effective alternative for identifying anticancer peptides.
- The integration of protein language models and deep learning represents a significant advancement in computational drug discovery for cancer.
- This method facilitates the accelerated discovery and development of novel peptide-based cancer therapeutics.
