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A Flow Cytometry-Based Cell Surface Protein Binding Assay for Assessing Selectivity and Specificity of an Anticancer Aptamer
Published on: September 13, 2022
ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides
Patrick Brendan Timmons1, Chandralal M Hewage1
1UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
Abstract:
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
Insights
Researchers developed a new deep learning model to predict anticancer peptides, offering a promising alternative to expensive cancer treatments with fewer side effects. This tool aids in discovering novel peptide therapeutics.
Area of Science:
- Computational biology
- Bioinformatics
- Drug discovery
Background:
- Cancer remains a leading cause of death globally, necessitating the development of novel and effective therapeutic agents.
- Current cancer treatments can be costly and associated with adverse side effects, highlighting the need for safer alternatives.
- Anticancer peptides (ACPs) are an emerging class of therapeutics with a favorable toxicity profile, offering a promising avenue for cancer treatment.
Purpose of the Study:
- To develop an accurate in silico method for predicting anticancer peptide activity.
- To leverage machine learning to identify novel anticancer peptide chemotherapeutics.
- To provide a user-friendly tool for the research community to facilitate ACP screening and design.
Main Methods:
- Utilized a sequence-based deep neural network (DNN) architecture.
- Trained and validated the DNN classifier on a comprehensive dataset of known anticancer peptides.
- Employed rigorous cross-validation techniques to assess model performance.
Main Results:
- Achieved high predictive performance with a cross-validated accuracy of 98.3%.
- Obtained a Matthews correlation coefficient (MCC) of 0.91 and an Area Under the Curve (AUC) of 0.95.
- Demonstrated performance comparable to state-of-the-art methods in anticancer peptide prediction.
Conclusions:
- The developed deep neural network classifier is a powerful tool for identifying potential anticancer peptides.
- The availability of this in silico tool can accelerate the discovery and design of novel peptide-based cancer therapeutics.
- This approach offers a cost-effective and efficient method for screening and developing new anticancer agents.
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