ArticleBriefings in bioinformatics2024
Lactylation prediction models based on protein sequence and structural feature fusion.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Crosstalk between lactylation and other post-translational modifications in health and diseases.Molecular biomedicine · 2026Review
- Training PBertKla on an Integrated Multi-Source Dataset with a Machine-Learning Layer for Lysine Lactylation Site Prediction.International journal of molecular sciences · 2026Article
- Data Resources and Computational Methods for Lactylation Site Prediction: A Mini-Review.International journal of molecular sciences · 2026Review
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Protein lactylation in Alzheimer's disease: bridging metabolism, pathology, and therapeutic opportunity.Frontiers in aging neuroscience · 2026Review
- Unraveling the role of lactylation in cell death and correlative diseases: a comprehensive review.Frontiers in cell and developmental biology · 2026Review
- Exploring the mechanisms of mutual influence between lactylation and macrophage polarization in the context of disease.Clinical and translational medicine · 2025Review
- Research advances of lactylation modification in breast cancer.Discover oncology · 2025Review
- PBertKla: a protein large language model for predicting human lysine lactylation sites.BMC biology · 2025Article
- Lactate-induced lactylation: from basic research to clinical perspectives.Frontiers in pharmacology · 2025Review
- Recent advances of lysine lactylation in prokaryotes and eukaryotes.Frontiers in molecular biosciences · 2024Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.