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ProteinBERT Algorithms: Applications in Antimicrobial Peptides Classification, Intrinsically Disordered Protein Prediction, and Toxicity Analysis
- 1 Xi’an Jiaotong-Liverpool University, Suzhou, China
* Author to whom correspondence should be addressed.
Abstract
The burgeoning field of computational biology has been markedly enhanced by the integration of advanced machine learning models capable of tackling intricate protein-related challenges. ProteinBERT, a transformer-based deep learning algorithm, has emerged as a formidable tool in deciphering complex patterns within protein sequences. This study delves into ProteinBERT's robust application across three pivotal domains: antimicrobial peptide (AMP) classification, intrinsically disordered protein (IDP) prediction, and protein toxicity prediction. Leveraging domain-specific datasets alongside sophisticated evaluation metrics, ProteinBERT has shown superior performance, surpassing both traditional models and other contemporary deep learning approaches in these areas. The analysis reveals that ProteinBERT not only accurately classifies AMPs, effectively predicts IDP configurations, and reliably forecasts protein toxicity but also sets new benchmarks in the precision of computational predictions. This research underscores the significant capabilities of ProteinBERT and discusses prospective enhancements that could refine its utility in computational protein analysis, aiming to push the boundaries of current methodologies and foster innovations in protein research.
Keywords
Antimicrobial peptides, intrinsically disordered proteins, protein toxicity
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Cite this article
Li,X. (2024). ProteinBERT Algorithms: Applications in Antimicrobial Peptides Classification, Intrinsically Disordered Protein Prediction, and Toxicity Analysis. Theoretical and Natural Science,71,99-107.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of ICBioMed 2024 Workshop: Computational Proteomics in Drug Discovery and Development from Medicinal Plants
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