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Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

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相关实验视频

Updated: Jul 1, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

机器学习预测器PSPire屏幕用于缺少内在无序区域的相分离蛋白.

Shuang Hou¹, Jiaojiao Hu^2,3, Zhaowei Yu¹

¹State Key Laboratory of Cardiology and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.

Nature communications

|March 8, 2024

概括

这项研究介绍了PSPire,这是一种用于预测蛋白相分离 (PS) 的新型机器学习工具. PSPire准确地识别分相蛋白 (PSP),甚至那些缺乏内在无序区域 (IDR) 的蛋白质,改进了现有的生物信息学方法.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

相关实验视频

Last Updated: Jul 1, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

科学领域:

生物化学生物化学
计算生物学计算生物学
分子生物学分子生物学

背景情况:

蛋白相分离 (PS) 是一个关键的细胞过程.
预测相分离蛋白 (PSP) 的现有生物信息学工具往往忽略了缺乏内在无序区域 (IDR) 的蛋白质.
PS受到内在无序区域 (IDR) 和结构化域的影响.

研究的目的:

开发一种更准确的机器学习预测器,用于相分离蛋白 (PSP).
改进不严重依赖内在无序区域 (IDR) 的PSP的识别.
突出基于结构的特征在预测蛋白质相位分离中的重要性.

主要方法:

开发了PSPire,这是一种机器学习预测器,包含了残留级和结构级的功能.
与现有的PSP预测工具对比,评估了PSPire的性能.
进行生物验证实验以确认预测的PSP.

主要成果:

PSPire在识别PSP方面表现出更高的准确性,特别是那些没有内在无序区域 (IDR) 的PSP.
该预测器有效地利用非IDR,基于结构的特征来预测蛋白质相位分离.
生物验证证实了PSPire形成细胞凝聚物的11个候选PSP中的9个.

结论:

在预测蛋白质相分离 (PS) 方面,PSPire提供了显著的进步.
基于结构的特征对于识别相分离蛋白 (PSP) 至关重要,补充了内在无序区域 (IDR) 的作用.
这些发现强调了考虑多样化的蛋白质特征对于准确的PS预测的重要性.