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mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic

Vinoth Kumar Sangaraju1, Nhat Truong Pham1, Leyi Wei2

  • 1Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.

Journal of Molecular Biology
|September 5, 2024
PubMed
Summary

A new tool, mACPpred 2.0, enhances the identification of anticancer peptides (ACPs) using advanced deep learning and feature representations. This improved method aids researchers in discovering novel ACPs for cancer therapy.

Keywords:
anticancer peptidespre-trained natural language processing-based embeddingsstacking deep learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunoinformatics

Background:

  • Anticancer peptides (ACPs) show promise for cancer treatment but are difficult to identify from amino acid sequences.
  • Existing machine learning (ML) tools aid ACP identification, with mACPpred (2019) being a notable advancement.
  • The rapid increase in characterized ACPs necessitates an updated and more accurate prediction model.

Purpose of the Study:

  • To develop an improved computational tool, mACPpred 2.0, for accurate prediction of anticancer peptides.
  • To integrate diverse feature descriptors, including NLP-based embeddings and probabilistic features, for enhanced ACP identification.
  • To create a user-friendly web server for accessible ACP prediction.

Main Methods:

  • Constructed an updated benchmarking dataset by merging all available public ACP datasets.
  • Employed a wide range of feature descriptors, including conventional and advanced natural language processing (NLP)-based embeddings.
  • Utilized a stacked deep learning (SDL) approach with 1D convolutional neural network (CNN) blocks and hybrid features (top NLP and 90 probabilistic features).

Main Results:

  • The stacked deep learning approach effectively identified hidden patterns using hybrid feature representations.
  • mACPpred 2.0 demonstrated superior performance compared to its predecessor (mACPpred) and existing state-of-the-art ACP predictors.
  • Rigorous cross-validation and independent tests confirmed the high accuracy and reliability of mACPpred 2.0.

Conclusions:

  • mACPpred 2.0 represents a significant advancement in the computational prediction of anticancer peptides.
  • The integration of spatial and probabilistic feature representations via SDL is crucial for improving prediction accuracy.
  • The publicly accessible mACPpred 2.0 web server (https://balalab-skku.org/mACPpred2/) will facilitate ACP research and drug discovery.