SynthesisBriefings in bioinformatics2024
Current computational tools for protein lysine acylation site prediction.
Synthesis 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 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- KAT-Net: a hyperbolic routing framework for joint lysine acylation site prediction with shared sequence-structure representations.Briefings in bioinformatics · 2026Article
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.
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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.