LMPhosSite: A deep learning-based approach for general protein phosphorylation site prediction using embeddings from the local window sequence and pretrained protein language model
Pakhrin, Subash C. Pokharel, Suresh Pratyush, Pawel Chaudhari, Meenal Ismail, Hamid D. KC, Dukka B.
Pakhrin, Subash C.
Pokharel, Suresh
Pratyush, Pawel
Chaudhari, Meenal
Ismail, Hamid D.
KC, Dukka B.
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2023-07-17
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Article
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Keywords
Deep learning,Embedding,Phosphorylation,Post-translational modification,Protein language model,Score-level fusion,Stack generalization
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Citation
Subash C. Pakhrin, Suresh Pokharel, Pawel Pratyush, Meenal Chaudhari, Hamid D. Ismail, and Dukka B. KC. 2023. LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model Journal of Proteome Research, v.22, iss.8, pages 2548-2557
Abstract
Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins. © 2023 American Chemical Society.
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American Chemical Society
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Journal of Proteome Research
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1535-3893