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A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

Published on: December 9, 2017

Current computational tools for protein lysine acylation site prediction

Zhaohui Qin¹, Haoran Ren¹, Pei Zhao²

¹Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China.

Briefings in Bioinformatics

|September 24, 2024

Summary

This review summarizes 166 computational methods for predicting protein lysine acylations (PLAs), a key post-translational modification. These insights aid in developing new predictors for biological insights.

Area of Science:

Biochemistry
Computational Biology
Proteomics

Background:

Protein lysine acylations (PLAs) are vital post-translational modifications (PTMs) regulating protein functions.
Proteomics advancements generate vast data, necessitating computational tools for biological insight extraction.
Accurate PLA detection is crucial, even with limited data, for understanding protein regulation.

Purpose of the Study:

To provide a comprehensive summary of 166 in silico PLA prediction methods.
To cover critical aspects for developing robust PLA predictors.
To highlight advanced techniques like protein language models and transfer learning for small-sample learning.

Main Methods:

Systematic review and summarization of 166 in silico PLA prediction methods.
Analysis of methods for single and multiple PLA site prediction.
Discussion of data collection, feature representation, algorithms, evaluation, and availability.

Main Results:

Categorization of PLA prediction methods based on various criteria.
Identification of key factors for developing effective PLA predictors.
Exploration of specialized predictors for functionally relevant and specific PLA sites.

Conclusions:

The review facilitates the development of novel PLA prediction tools.
It offers valuable insights for researchers across disciplines studying PTMs.
Understanding PLA prediction is key to advancing biological research.

Keywords:

deep learning lysine acylation post-translation modification protein language model transfer learning

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