PCBert-Kla: an efficient prediction method for lysine lactylation sites based on
ProtBert and fusion of physicochemical features
#MMPMID41259418
Zhang HQ
; Qi YX
; Fida H
; Zhang HJ
; Arif M
; Zhao PY
; Alam T
; Qi YC
; Yu XL
; Deng KJ
Brief Bioinform
2025[Nov]; 26
(6
): ? PMID41259418
show ga
Protein post-translational modifications (PTMs) play a critical role in
regulating protein functionality and structural diversity. Among them, lysine
lactylation (Kla), a newly identified PTM, is involved in energy metabolism,
cellular reprogramming, and the progression of various diseases. In this study,
we propose PCBert-Kla, a feature-fusion deep learning model based on ProtBert.
This model leverages ProtBert to extract deep features from protein sequences,
effectively capturing global and local contextual information. It integrated
various physicochemical properties, including molecular weight, isoelectric
point, amino acid composition, secondary structure content, hydrophobicity, and
net charge. An attention mechanism in the fully connected layers enabled the
model to select features automatically. PCBert-Kla exhibited exceptional accuracy
and reliability in Kla site identification and demonstrated excellent
generalization capability to outperform the existing models. In addition, we
further enhanced the interpretability of the PCBert-Kla model by incorporating
average attention maps. This model provided powerful tools for studying the
functions of Kla and elucidating the mechanisms of related diseases, which can
advance biomedical research and drug development. We also developed a free web
service, available at http://pcbert-kla.lin-group.cn/, to provide users with easy
access and usage.
free
free
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