Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins
IntroductionLysine 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-consumi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Cellular and Infection Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1615443/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849318909229924352 |
|---|---|
| author | Yong-Zi Chen Yong-Zi Chen Xiaofeng Wang Zhuo-Zhi Wang Haixin Li Haixin Li |
| author_facet | Yong-Zi Chen Yong-Zi Chen Xiaofeng Wang Zhuo-Zhi Wang Haixin Li Haixin Li |
| author_sort | Yong-Zi Chen |
| collection | DOAJ |
| description | IntroductionLysine 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.MethodsTo 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.ResultsComprehensive 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. DiscussionThe 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. |
| format | Article |
| id | doaj-art-0ca899c193e14f57aef18df9a61ae839 |
| institution | Kabale University |
| issn | 2235-2988 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj-art-0ca899c193e14f57aef18df9a61ae8392025-08-20T03:50:40ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882025-07-011510.3389/fcimb.2025.16154431615443Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteinsYong-Zi Chen0Yong-Zi Chen1Xiaofeng Wang2Zhuo-Zhi Wang3Haixin Li4Haixin Li5Cancer Biobank, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Molecular Cancer Epidemiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaCollege of Mathematics and Computer Sciences, Shanxi Normal University, Taiyuan, ChinaSchool of Biomedical Engineering, Tianjin Medical University, Tianjin, ChinaCancer Biobank, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Molecular Cancer Epidemiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaIntroductionLysine 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.MethodsTo 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.ResultsComprehensive 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. DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fcimb.2025.1615443/fulllysine crotonylationfungal proteinslanguage modelpathogenpost-translational modifications |
| spellingShingle | Yong-Zi Chen Yong-Zi Chen Xiaofeng Wang Zhuo-Zhi Wang Haixin Li Haixin Li Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins Frontiers in Cellular and Infection Microbiology lysine crotonylation fungal proteins language model pathogen post-translational modifications |
| title | Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| title_full | Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| title_fullStr | Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| title_full_unstemmed | Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| title_short | Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| title_sort | fungi kcr a language model for predicting lysine crotonylation in pathogenic fungal proteins |
| topic | lysine crotonylation fungal proteins language model pathogen post-translational modifications |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1615443/full |
| work_keys_str_mv | AT yongzichen fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins AT yongzichen fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins AT xiaofengwang fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins AT zhuozhiwang fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins AT haixinli fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins AT haixinli fungikcralanguagemodelforpredictinglysinecrotonylationinpathogenicfungalproteins |