Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins

TL;DR

Fungi-Kcr is a deep learning model that predicts lysine crotonylation in fungal proteins, offering a faster alternative to costly experiments.

Contribution

Fungi-Kcr combines CNN, GRU, and word embedding for improved prediction of Kcr sites in pathogenic fungi.

Findings

Fungi-Kcr outperforms conventional machine learning models in predicting lysine crotonylation sites.

General predictive models perform better than species-specific models for Kcr site prediction.

The model aids in understanding fungal pathogenesis and identifying therapeutic targets.

Abstract

Lysine crotonylation (Kcr) is an important post-translational modification (PTM) of proteins, playing a key role in regulating various biological processes in pathogenic fungi. However, the experimental identification of Kcr sites remains challenging due to the high cost and time-consuming nature of mass spectrometry-based techniques.

To address this limitation, we developed Fungi-Kcr, a deep learning-based model designed to predict Kcr modification sites in fungal proteins. The model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and word embedding to effectively capture both local and long-range sequence dependencies.

Comprehensive evaluations, including ten-fold cross-validation and independent testing, demonstrate that Fungi-Kcr achieves superior predictive performance compared to conventional machine learning models. Moreover, our results indicate that a general predictive model performs better than species-specific models.

The proposed model provides a valuable computational tool for the large-scale identification of Kcr sites, contributing to a deeper understanding of fungal pathogenesis and potential therapeutic targets. The source code and dataset for Fungi-Kcr are available at https://github.com/zayra77/Fungi-Kcr.

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