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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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Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions.

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

Summary

This study introduces PSPire, a novel machine learning tool for predicting protein phase separation (PS). PSPire accurately identifies phase-separating proteins (PSPs), even those lacking intrinsically disordered regions (IDRs), improving upon existing bioinformatics methods.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Biochemistry
Computational Biology
Molecular Biology

Background:

Protein phase separation (PS) is a crucial cellular process.
Existing bioinformatics tools for predicting phase-separating proteins (PSPs) often overlook proteins lacking intrinsically disordered regions (IDRs).
PS is influenced by both intrinsically disordered regions (IDRs) and structured domains.

Purpose of the Study:

To develop a more accurate machine learning predictor for phase-separating proteins (PSPs).
To improve the identification of PSPs that do not rely heavily on intrinsically disordered regions (IDRs).
To highlight the importance of structure-based features in predicting protein phase separation.

Main Methods:

Developed PSPire, a machine learning predictor incorporating residue-level and structure-level features.
Evaluated PSPire's performance against existing PSP prediction tools.
Conducted biological validation experiments to confirm predicted PSPs.

Main Results:

PSPire demonstrates improved accuracy in identifying PSPs, particularly those without intrinsically disordered regions (IDRs).
The predictor effectively utilizes non-IDR, structure-based characteristics for predicting protein phase separation.
Biological validation confirmed 9 out of 11 candidate PSPs identified by PSPire form cellular condensates.

Conclusions:

PSPire offers a significant advancement in predicting protein phase separation (PS).
Structure-based features are critical for identifying phase-separating proteins (PSPs), complementing the role of intrinsically disordered regions (IDRs).
The findings underscore the importance of considering diverse protein features for accurate PS prediction.